A Comprehensive Review of LiDAR Applications in Crop Management for Precision Agriculture
Abstract
:1. Introduction
1.1. Overview of Precision Agriculture
1.2. Importance of Efficient Crop Management and the Emerging Role of LiDAR Technology
1.2.1. Enhancing Productivity
1.2.2. Resource Optimization
1.2.3. Disease and Pest Management
1.2.4. Sustainability and Environmental Impact
2. LiDAR Technology in Precision Agriculture: An Overview
2.1. LiDAR in Precision Agriculture
2.2. Types of LiDAR Systems and Their Use in Precision Agriculture
2.2.1. Airborne LiDAR Systems (ALSs)
2.2.2. Terrestrial LiDAR Systems (TLSs)
2.2.3. Mobile LiDAR Systems (MLSs)
3. Applications of LiDAR in Crop Cultivation
3.1. Crop Monitoring and Management
3.1.1. Plant Health Assessment, Maintenance, and Disease Detection
3.1.2. Weed Detection and Fertilizer Application
3.1.3. Crop Growth Monitoring
3.1.4. Autonomous Navigation System
3.1.5. Above-Ground Biomass (AGB) and Yield Estimation
3.2. Soil Analysis and Management
3.2.1. Soil Mapping and Classification
3.2.2. Soil Moisture Content and Nutrient Analysis
4. LiDAR Applications in Crop Harvesting
4.1. Crop Maturity and Yield Prediction
4.2. Autonomous Harvesting Systems
4.3. Post-Harvest Quality Assessment
5. Conclusions
5.1. Emerging Trends and Innovations in LiDAR Technology
5.2. Key Challenges and Research Gaps
5.3. Potential Solutions and Future Directions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Nomenclature
ASBS | Alpha Shape by Slices Algorithm |
ASA | Alpha Shape Algorithm |
APSIM | Agricultural Production Simulator |
BEM | Biomass Estimation Model |
BPNN | Back Propagation Neural Network |
CHM | Crop Height Model |
CRM | Crop Row Model |
CSM | Canopy Surface Model |
CMM | Canopy Metric Model |
CSF | Cloth Simulation Filter Algorithm |
CFPD | Canopy Foliage Prediction Model |
CMA | Canopy Metric Algorithm |
CHA | Concave Hull Algorithm |
CGA | Computational Geometry Algorithm |
CXHA | Convex Hull Algorithm |
CVM | Cross Validation Model |
DTM | Digital Terrain Model |
DEM | Digital Elevation Model |
DSM | Digital Surface Model |
DTA | Delaunay Triangulation Algorithm |
DBSCAN | Density Based Clustering Algorithm |
FLASH | Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes |
GBM | Generalized Boosted Model |
GMM | Gaussian Mixture Model |
GLM | Generalized Linear Model |
GNBA | Gaussian Naïve Bayes Algorithm |
IWM | Iterative Waterfall Model |
ICPA | Iterative Closest Point Algorithm |
IWM | Iterative Waterfall Model |
IDW | Inverse Distance Weighting Algorithm |
KM | Kinematic Model |
KNN | k-Nearest Neighbour Algorithm |
LAEM | Leaf Area Estimation Model |
LAI | Leaf Area Index |
LADE | Leaf Area Density Estimation Model |
LA-DT | Leaf Area- Delaunay Triangulation |
LBM | Laser Beam Model |
QTM | Quantitative Terrain Model |
QSM | Quantitative Structure Model |
RFR | Random Forest Regression Model |
LRM | Linear Regression Model |
LLSVM | Lateral Linear Stereoscopic Vision Model |
LME | Linear Mixed Effects Model |
MCSM | Multitemporal Crop Surface Model |
MSM | Multi-Spectral Model |
MLRM | Multiple Linear Regression Model |
MSM | Maize Segmentation Model |
MOSM | Maize Organ Stratification Model |
NDVI | Normalized Difference Vegetation Index |
OQSM | Optimized Quantitative Structural Model |
PLSRM | Partial Least Square Regression Model |
PCL | Point Cloud Library |
PCA | Poly Cylinder Algorithm |
PCSA | Point Cloud Segmentation Algorithm |
PCTA | Point Cloud Transformer Algorithm |
PCPA | Point Cloud Processing Algorithm |
PCAA | Principal Component Analysis Algorithm |
PH | Plant Hight |
RANSAC | Random Sample Consensus Algorithm |
ROS | Robot Operating System |
SLAM | Simultaneous Localization and Mapping Algorithm |
SVIA | Spectral Vegetation Indices Algorithm |
SLMR | Stepwise Multiple Linear Regression |
SVMM | Support Vector Machine Model |
SDA | Standard Deviation Algorithm |
SORA | Statistical Outlier Removal Algorithm |
TIN | Triangulation Network Filtering Algorithm |
TM | Tree Model |
ULR | Univariate Linear Regression |
VBA | Voxel-Based Algorithm |
3DPI | 3D Photo Inpainting |
3DPC | 3D Point Cloud |
3DCM | 3D Crop Model |
3DLSM | 3D Leaf Surface Model |
3DSM | 3D Surface Model |
References
- Bongiovanni, R.; Lowenberg-DeBoer, J. Precision Agriculture and sustainability. Precis. Agric. 2004, 5, 359–387. [Google Scholar] [CrossRef]
- Jin, Y.; Liu, J.; Xu, Z.; Yuan, S.; Li, P.; Wang, J. Development status and trend of agricultural robot technology. Int. J. Agric. Biol. Eng. 2021, 14, 1–19. [Google Scholar] [CrossRef]
- Tahilyani, S.; Saxena, S.; Karras, D.A.; Gupta, S.K. Deployment of autonomous vehicles in agricultural and using Voronoi partitioning. In Proceedings of the 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES), Chickballapur, India, 28–29 December 2022. [Google Scholar]
- Sparks, A. Use of LiDAR in the Design of Grassed Waterways: Case Study in Agricultural Management in Oklahoma. Master’s Thesis, Michigan Technological University, Houghton, MI, USA, 2016. [Google Scholar]
- Di Stefano, F.; Chiappini, S.; Gorreja, A.; Balestra, M.; Pierdicca, R. Mobile 3D scan LiDAR: A literature review. Geomatics, Natural Hazards and Risk. Geomatics 2021, 12, 2387–2429. [Google Scholar]
- Guo, Q.; Su, Y.; Hu, T.; Guan, H.; Jin, S.; Zhang, J.; Zhao, X.; Xu, K.; Wei, D.; Kelly, M.; et al. Lidar boosts 3D ecological observations and modelings: A review and perspective. IEEE Geosci. Remote Sens. Mag. 2020, 9, 232–257. [Google Scholar] [CrossRef]
- Qiu, Q.; Li, X. LiDAR point-cloud odometer based mobile robot routine tracking in orchards. In Proceedings of the 2022 12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), Changbai Mountain, China, 27–31 July 2022. [Google Scholar]
- Nehme, H.; Aubry, C.; Solatges, T.; Savatier, X. Lidar-based structure tracking for agricultural robots: Application to autonomous navigation in vineyards. J. Intell. Robot. Syst. 2021, 103, 1–16. [Google Scholar] [CrossRef]
- Reger, M.; Stumpenhausen, J.; Bernhardt, H. Evaluation of LiDAR for the free navigation in agriculture. AgriEngineering 2022, 4, 489–506. [Google Scholar] [CrossRef]
- Radočaj, D.; Plaščak, I.; Jurišić, M. Global navigation satellite systems as state-of-the-art solutions in precision agriculture: A review of studies indexed in the web of science. Agriculture 2023, 13, 1417. [Google Scholar] [CrossRef]
- Ding, H.; Zhang, B.; Zhou, J.; Yan, Y.; Tian, G.; Gu, B. Recent developments and applications of simultaneous localization and mapping in agriculture. J. Field Robot. 2022, 39, 956–983. [Google Scholar] [CrossRef]
- Ahmad, U.; Nasirahmadi, A.; Hensel, O.; Marino, S. Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring. Agronomy 2022, 12, 555. [Google Scholar] [CrossRef]
- Lin, Y.-C.; Habib, A. Quality control and crop characterization framework for multi-temporal UAV LiDAR data over mechanized agricultural fields. Remote Sens. Environ. 2021, 256, 112299. [Google Scholar] [CrossRef]
- Alexopoulos, A.; Koutras, K.; Ali, S.B.; Puccio, S.; Carella, A.; Ottaviano, R.; Kalogeras, A. Complementary use of ground-based proximal sensing and airborne/spaceborne remote sensing techniques in precision agriculture: A systematic review. Agronomy 2023, 13, 1942. [Google Scholar] [CrossRef]
- Debnath, S.; Paul, M.; Debnath, T. Applications of LiDAR in Agriculture and Future Research Directions. J. Imaging 2023, 9, 57. [Google Scholar] [CrossRef] [PubMed]
- Rivera, G.; Porras, R.; Florencia, R.; Sánchez-Solís, J.P. LiDAR applications in precision agriculture for cultivating crops: A review of recent advances. Comput. Electron. Agric. 2023, 207, 107737. [Google Scholar] [CrossRef]
- Li, X.; Liu, C.; Wang, Z.; Xie, X.; Li, D.; Xu, L. Airborne LiDAR: State-of-the-art of system design, technology and application. Meas. Sci. Technol. 2020, 32, 032002. [Google Scholar] [CrossRef]
- Dasika, S.S. Assessing the Spatial Accuracy and Precision of LiDAR for Remote Sensing in Agriculture. In Biosystems and Agricultural Engineering; University of Kentucky: Lexington, KY, USA, 2018. [Google Scholar]
- Ji, X.; Wei, X.; Wang, A.; Cui, B.; Song, Q. A novel composite adaptive terminal sliding mode controller for farm vehicles lateral path tracking control. Nonlinear Dyn. 2022, 110, 2415–2428. [Google Scholar] [CrossRef]
- Sishodia, R.P.; Ray, R.L.; Singh, S.K. Applications of Remote Sensing in Precision Agriculture: A Review. Remote Sens. 2020, 12, 3136. [Google Scholar] [CrossRef]
- Bhakta, I.; Phadikar, S.; Majumder, K. State-of-the-art technologies in precision agriculture: A systematic review. J. Sci. Food Agric. 2019, 99, 4878–4888. [Google Scholar] [CrossRef] [PubMed]
- Al-Sammarraie, M.A.J.; Ali, A.; Hussein, N.M. New irrigation techniques for precisions agriculture: A review. Plant Arch. 2021, 21, 1734–1740. [Google Scholar] [CrossRef]
- Achilles, B.; Maria, P.; Panagiotis, D.; Aglaia, L.-T.; Barouchas, P.; George, S.; George, K.; Shaohua, W.; Sotirios, G. Internet of things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smart farming: A comprehensive review. Internet Things 2022, 18, 100187. [Google Scholar]
- Monteiro, A.; Santos, S.; Gonçalves, P.H. Precision Agriculture for Crop and Livestock Farming—Brief Review. Animals 2021, 11, 2345. [Google Scholar] [CrossRef] [PubMed]
- Abobatta, W.F. Precision agriculture: A new tool for development. In Precision Agriculture Technologies for Food Security and Sustainability; IGI Global Publishing: Hershey, PA, USA, 2021; pp. 23–45. [Google Scholar] [CrossRef]
- Ali, A.; Hussain, T.; Tantashutikun, N.; Hussain, N.; Cocetta, G. Application of Smart Techniques, Internet of Things and Data Mining for Resource Use Efficient and Sustainable Crop Production. Agriculture 2023, 13, 397. [Google Scholar] [CrossRef]
- Xu, R.; Li, C.; Bernardes, S. Development and Testing of a UAV-Based Multi-Sensor System for Plant Phenotyping and Precision Agriculture. Remote Sens. 2021, 13, 3517. [Google Scholar] [CrossRef]
- Furbank, R.T.; Jimenez-Berni, J.A.; George-Jaeggli, B.; Potgieter, A.B.; Deery, D.M. Field crop phenomics: Enabling breeding for radiation use efficiency and biomass in cereal crops. New Phytol. 2019, 223, 1714–1727. [Google Scholar] [CrossRef] [PubMed]
- Zhang, F.; Hassanzadeh, A.; Kikkert, J.; Pethybridge, S.; van Aardt, J. Toward a structural description of row crops using UAS-based LiDAR point clouds. In Proceedings of the IGARSS 2020—2020 IEEE International Geoscience and Remote Sensing Symposium, Virtual Symposium, 26 September–2 October 2020; pp. 465–468. [Google Scholar]
- Sheikh, A.; Abbas, A. Barriers in efficient crop management in rice-wheat cropping system of Punjab. Pak. J. Agric. Sci. 2007, 44, 341–349. [Google Scholar]
- Arif, C.; Nugroho, B.D.A.; Maftukha, R.; Suryandika, F.; Hapsari, U.; Nihayah, B.; Naititi, N.P.P.E.; Sain, R.I.A. Performance of agro-environmental monitoring for optimum water and crop management: A case study for East Nusa Tenggara, Indonesia. In IOP Conference Series: Earth and Environmental Science; IOP Publishing: Bristol, UK, 2021. [Google Scholar]
- El-Naggar, A.; Jolly, B.; Hedley, C.B.; Horne, D.; Roudier, P.; Clothier, B.E. The use of terrestrial LiDAR to monitor crop growth and account for within-field variability of crop coefficients and water use. Comput. Electron. Agric. 2021, 190, 106416. [Google Scholar] [CrossRef]
- Eyre, R.; Lindsay, J.; Laamrani, A.; Berg, A. Within-Field Yield Prediction in Cereal Crops Using LiDAR-Derived Topographic Attributes with Geographically Weighted Regression Models. Remote Sens. 2021, 13, 4152. [Google Scholar] [CrossRef]
- Golombek, Y. Lidar Applications for Measuring and Quantifying Streetscape and Streetscape Features; University of Colorado at Denver: Denver, CO, USA, 2020. [Google Scholar]
- Estrada, J.; Sánchez, H.; Hernanz, L.; Checa, M.J.; Roman, D. Enabling the Use of Sentinel-2 and LiDAR Data for Common Agriculture Policy Funds Assignment. ISPRS Int. J. Geo-Inf. 2017, 6, 255. [Google Scholar] [CrossRef]
- Xue, X.; Zhou, Q.; Chen, C.; Cai, C. Design and test of variable spray model based on leaf wall area in orchards. Trans. Chin. Soc. Agric. Eng. 2020, 36, 16–22. [Google Scholar]
- Botta, A.; Cavallone, P.; Baglieri, L.; Colucci, G.; Tagliavini, L.; Quaglia, G.A. A review of robots, perception, and tasks in precision agriculture. Appl. Mech. 2022, 3, 830–854. [Google Scholar] [CrossRef]
- Roten, R.; Fourie, J.; Owens, J.; Trethewey, J.; Ekanayake, D.; Werner, A. Urine patch detection using LiDAR technology to improve nitrogen use efficiency in grazed pastures. Comput. Electron. Agric. 2017, 135, 128–133. [Google Scholar] [CrossRef]
- Feng, G.; Wang, C.; Wang, A.; Gao, Y.; Zhou, Y.; Huang, S.; Luo, B. Segmentation of Wheat Lodging Areas from UAV Imagery Using an Ultra-Lightweight Network. Agriculture 2024, 14, 244. [Google Scholar] [CrossRef]
- Lednev, V.N.; Zavozin, V.A.; Grishin, M.Y.; Grigorieva, D.V.; Sdvizhenskii, P.A. Drone-Based Fluorescence Lidar for Agricultural Applications. In BIO Web of Conferences; EDP Sciences: Les Ulis, France, 2023. [Google Scholar]
- Wu, D.; Phinn, S.; Johansen, K.; Robson, A.; Muir, J.; Searle, C. Estimating changes in leaf area, leaf area density, and vertical leaf area profile for mango, avocado, and macadamia tree crowns using terrestrial laser scanning. Remote Sens. 2018, 10, 1750. [Google Scholar] [CrossRef]
- LeVoir, S.J.; Farley, P.A.; Sun, T.; Xu, C. High-Accuracy adaptive low-cost location sensing subsystems for autonomous rover in precision agriculture. IEEE Open J. Ind. Appl. 2020, 1, 74–94. [Google Scholar] [CrossRef]
- Walklate, P.J.; Richardson, G.M.; Baker, D.E.; Richards, P.A.; Cross, J.V. Short-range Lidar measurement of top fruit tree canopies for pesticide applications research in the United Kingdom. In Advances in Laser Remote Sensing for Terrestrial and Oceanographic Applications; Society of Photo Optical: Bellingham, WA, USA, 1997. [Google Scholar]
- Holmén, B.A.; Eichinger, W.E.; Flocchini, R.G. Application of elastic lidar to PM10 emissions from agricultural nonpoint sources. Environ. Sci. Technol. 1998, 32, 3068–3076. [Google Scholar] [CrossRef]
- Ilari, A.; Piancatelli, S.; Centorame, L.; Moumni, M.; Romanazzi, G.; Pedretti, E.F. Distribution Quality of Agrochemicals for the Revamping of a Sprayer System Based on Lidar Technology and Grapevine Disease Management. Appl. Sci. 2023, 13, 2222. [Google Scholar] [CrossRef]
- Shan, J.; Toth, C.K. Topographic Laser Ranging and Scanning: Principles and Processing; CRC Press: Boca Raton, FL, USA, 2018. [Google Scholar]
- Raj, T.; Hashim, F.H.; Huddin, A.B.; Ibrahim, M.F.; Hussain, A. A survey on LiDAR scanning mechanisms. Electronics 2020, 9, 741. [Google Scholar] [CrossRef]
- Ferraz, A.; Bretar, F.; Jacquemoud, S.; Gonclave, G.R. The role of Lidar systems in fuel mapping. In Proceedings of the 27th International Conference on Optical Network Design and Modelling, Braunschweig, Germany, 18–20 February 2009. [Google Scholar]
- Ziębiński, A.; Cupek, R.; Kruk, M.; Drewniak, M. Lidar technology in general purpose applications. Stud. Inform. 2016, 37, 15–32. [Google Scholar]
- Wang, R.; Peethambaran, J.; Chen, D. Lidar point clouds to 3-D urban models $: $ A review. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2018, 11, 606–627. [Google Scholar] [CrossRef]
- Bates, J.S.; Montzka, C.; Schmidt, M.; Jonard, F. Estimating canopy density parameters time-series for winter wheat using UAS Mounted LiDAR. Remote Sens. 2021, 13, 710. [Google Scholar] [CrossRef]
- Lin, Y. LiDAR: An important tool for next-generation phenotyping technology of high potential for plant phenomics. Comput. Electron. Agric. 2015, 119, 61–73. [Google Scholar] [CrossRef]
- Zhang, H.; Yemoto, K. UAS-based remote sensing applications on the Northern Colorado Limited Irrigation Research Farm. Int. J. Precis. Agric. Aviat. 2019, 2, 20190202.50. [Google Scholar] [CrossRef]
- García-Cimarras, A.; Manzanera, J.A.; Valbuena, R. LiDAR Scan Density and Spatial Resolution Effects on Vegetation Fuel Type Mapping. Croat. J. For. Eng. J. Theory Appl. For. Eng. 2023, 44, 189–201. [Google Scholar] [CrossRef]
- Nurcholis, M.; Himawan, I.Q.; Wijayanti, S.I.; Darmaristianti, A. Tropical Vegetation and Land Cover Mapping Using LiDAR. Planta Trop. 2019, 7, 8–18. [Google Scholar] [CrossRef]
- Zheng, C.; Abd-Elrahman, A.; Whitaker, V. Remote sensing and machine learning in crop phenotyping and management, with an emphasis on applications in strawberry farming. Remote Sens. 2021, 13, 531. [Google Scholar] [CrossRef]
- Jin, S.; Sun, X.; Wu, F.; Su, Y.; Li, Y.; Song, S.; Xu, K.; Ma, Q.; Baret, F.; Jiang, D.; et al. Lidar sheds new light on plant phenomics for plant breeding and management: Recent advances and future prospects. ISPRS J. Photogramm. Remote Sens. 2021, 171, 202–223. [Google Scholar] [CrossRef]
- Büyüksalih, İ. Building Zone Regulation Compliance Using LIDAR Data: Real-Life Tests in İstanbul. Int. J. Environ. Geoinform. 2012, 3, 48–55. [Google Scholar] [CrossRef]
- Chen, F.-q. Advantages and Application Prospects Study of Airborne LIDAR technology. Beijing Surv. Mapp. 2013, 2, 12–14. [Google Scholar]
- Chase, A.S.; Weishampel, J. Using LiDAR and GIS to investigate water and soil management in the agricultural terracing at Caracol, Belize. Adv. Archaeol. Pract. 2016, 4, 357–370. [Google Scholar] [CrossRef]
- Neuville, R.; Bates, J.S.; Jonard, F. Estimating forest structure from UAV-mounted LiDAR point cloud using machine learning. Remote Sens. 2021, 13, 352. [Google Scholar] [CrossRef]
- Rakesh, D.; Kumar, N.A.; Sivaguru, M.; Keerthivaasan, K.V.R.; Janaki, B.R.; Raffik, R. Role of UAVs in innovating agriculture with future applications: A review. In Proceedings of the 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), Virtual, 8–9 October 2021. [Google Scholar]
- Fareed, N.; Flores, J.P.; Das, A.K. Analysis of UAS-LiDAR Ground Points Classification in Agricultural Fields Using Traditional Algorithms and PointCNN. Remote Sens. 2023, 15, 483. [Google Scholar] [CrossRef]
- Aslan, M.F.; Durdu, A.; Sabanci, K.; Ropelewska, E.; Gültekin, S.S. A comprehensive survey of the recent studies with UAV for precision agriculture in open fields and greenhouses. Appl. Sci. 2022, 12, 1047. [Google Scholar] [CrossRef]
- Dowling, L.; Poblete, T.; Hook, I.; Tang, H.; Tan, Y.; Glenn, W.; Unnithan, R.R. Accurate indoor mapping using an autonomous unmanned aerial vehicle (UAV). arXiv 2018, arXiv:1808.01940. [Google Scholar]
- Qin, H.; Meng, Z.; Meng, W.; Chen, X.; Sun, H.; Lin, F.; Ang, M.H. Autonomous exploration and mapping system using heterogeneous UAVs and UGVs in GPS-denied environments. IEEE Trans. Veh. Technol. 2019, 68, 1339–1350. [Google Scholar] [CrossRef]
- Turner, R.; Panciera, R.; Tanase, M.A.; Lowell, K.; Hacker, J.M.; Walker, J.P. Estimation of soil surface roughness of agricultural soils using airborne LiDAR. Remote Sens. Environ. 2014, 140, 107–117. [Google Scholar] [CrossRef]
- Ladefoged, T.N.; McCoy, M.D.; Asner, G.P.; Kirch, P.V.; Puleston, C.O.; Chadwick, O.A.; Vitousek, P.M. Agricultural potential and actualized development in Hawai’i: An airborne LiDAR survey of the leeward Kohala field system (Hawai’i Island). J. Archaeol. Sci. 2011, 38, 3605–3619. [Google Scholar] [CrossRef]
- Zhang, X.; Bao, Y.; Wang, D.; Xin, X.; Ding, L.; Xu, D.; Hou, L.; Shen, J. Using UAV Lidar to extract vegetation parameters of inner Mongolian grassland. Remote Sens. 2021, 13, 656. [Google Scholar] [CrossRef]
- Liu, X.; Bo, Y. Object-Based Crop Species Classification Based on the Combination of Airborne Hyperspectral Images and LiDAR Data. Remote Sens. 2015, 7, 922–950. [Google Scholar] [CrossRef]
- Maimaitijiang, M.; Sagan, V.; Erkbol, H.; Adrian, J.; Newcomb, M.; LeBauer, D.; Pauli, D.; Shakoor, N.; Mockler, T. UAV-Based sorghum growth monitoring: A comparative analysis of LIDAR and photogrammetry. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2020, 3, 489–496. [Google Scholar] [CrossRef]
- Zhang, C.; Craine, W.A.; McGee, R.J.; Vandemark, G.J.; Davis, J.B.; Brown, J.; Hulbert, S.H.; Sankaran, S. High-throughput phenotyping of canopy height in cool-season crops using sensing techniques. Agron. J. 2021, 113, 3269–3280. [Google Scholar] [CrossRef]
- Ivushkin, K.; Bartholomeus, H.; Bregt, A.K.; Pulatov, A.; Franceschini, M.H.; Kramer, H.; van Loo, E.N.; Roman, V.J.; Finkers, R. UAV based soil salinity assessment of cropland. Geoderma 2019, 338, 502–512. [Google Scholar] [CrossRef]
- Trepekli, K.; Westergaard-Nielsen, A.; Friborg, T. Application of drone borne LiDAR technology for monitoring agricultural biomass and plant growth. In EGU General Assembly Conference Abstracts; European Geosciences Union: Munich, Germany, 2020. [Google Scholar]
- Shendryk, Y.; Sofonia, J.; Garrard, R.; Rist, Y.; Skocaj, D.; Thorburn, P. Fine-scale prediction of biomass and leaf nitrogen content in sugarcane using UAV LiDAR and multispectral imaging. Int. J. Appl. Earth Obs. Geoinf. 2020, 92, 102177. [Google Scholar] [CrossRef]
- Xu, J.X.; Ma, J.; Tang, Y.N.; Wu, W.X.; Shao, J.H.; Wu, W.B.; Wei, S.Y.; Liu, Y.F.; Wang, Y.C.; Guo, H.Q. Estimation of sugarcane yield using a machine learning approach based on UAV-lidar data. Remote Sens. 2020, 12, 2823. [Google Scholar] [CrossRef]
- Masjedi, A.; Zhao, J.; Thompson, A.M.; Yang, K.W.; Flatt, J.E.; Crawford, M.M.; Ebert, D.S.; Tuinstra, M.R.; Hammer, G.; Chapman, S. Sorghum biomass prediction using UAV-based remote sensing data and crop model simulation. In Proceedings of the IGARSS 2018—2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–27 July 2018. [Google Scholar]
- Wu, D.; Johansen, K.; Phinn, S.; Robson, A. Suitability of airborne and terrestrial laser scanning for mapping tree crop structural metrics for improved orchard management. Remote Sens. 2020, 12, 1647. [Google Scholar] [CrossRef]
- Hütt, C.; Bolten, A.; Hüging, H.; Bareth, G. UAV Lidar metrics for monitoring crop height, biomass and nitrogen uptake: A case study on a winter wheat field trial. PFG–J. Photogramm. Remote Sens. Geoinf. Sci. 2023, 91, 65–76. [Google Scholar] [CrossRef]
- Masjedi, A.; Crawford, M.M.; Carpenter, N.R.; Tuinstra, M.R. Multi-temporal predictive modelling of sorghum biomass using UAV-based hyperspectral and LiDAR data. Remote Sens. 2020, 12, 3587. [Google Scholar] [CrossRef]
- Terrestrial Lidar Scanning Research. Available online: https://sites.bu.edu/lidar/3d-reconstruction/ (accessed on 20 February 2024).
- Westling, F.; Bryson, M.; Underwood, J. SimTreeLS: Simulating aerial and terrestrial laser scans of trees. Comput. Electron. Agric. 2021, 187, 106277. [Google Scholar] [CrossRef]
- Hosoi, F.; Omasa, K. Estimating vertical plant area density profile and growth parameters of a wheat canopy at different growth stages using three-dimensional portable lidar imaging. ISPRS J. Photogramm. Remote Sens. 2009, 64, 151–158. [Google Scholar] [CrossRef]
- Martínez-Casasnovas, J. Mapping the leaf area index in vineyard using a ground based LiDAR scanner. In Proceedings of the 11th International Conference on Precision Agriculture (ICPA 2012), Indianapolis, IN, USA, 15–18 July 2012. [Google Scholar]
- Hobart, M.; Pflanz, M.; Weltzien, C.; Schirrmann, M. Growth height determination of tree walls for precise monitoring in apple fruit production using UAV photogrammetry. Remote Sens. 2020, 12, 1656. [Google Scholar] [CrossRef]
- Höfle, B. Radiometric correction of terrestrial LiDAR point cloud data for individual maize plant detection. IEEE Geosci. Remote Sens. Lett. 2013, 11, 94–98. [Google Scholar] [CrossRef]
- Xu, L.; Xu, T.; Li, X. Corn Seedling Monitoring Using 3-D Point Cloud Data from Terrestrial Laser Scanning and Registered Camera Data. IEEE Geosci. Remote Sens. Lett. 2019, 17, 137–141. [Google Scholar] [CrossRef]
- Koenig, K.; Höfle, B.; Hämmerle, M.; Jarmer, T.; Siegmann, B.; Lilienthal, H. Comparative classification analysis of post-harvest growth detection from terrestrial LiDAR point clouds in precision agriculture. ISPRS J. Photogramm. Remote Sens. 2015, 104, 112–125. [Google Scholar] [CrossRef]
- Bareth, G.; Bendig, J.; Tilly, N.; Hoffmeister, D.; Aasen, H.; Bolten, A. A comparison of UAV-and TLS-derived plant height for crop monitoring: Using polygon grids for the analysis of crop surface models (CSMs). Photogramm. Fernerkund. Geoinf. 2016, 2016, 85–94. [Google Scholar] [CrossRef]
- Madec, S.; Baret, F.; De Solan, B.; Thomas, S.; Dutartre, D.; Jezequel, S.; Hemmerlé, M.; Colombeau, G.; Comar, A. High-throughput phenotyping of plant height: Comparing unmanned aerial vehicles and ground LiDAR estimates. Front. Plant Sci. 2017, 8, 2002. [Google Scholar] [CrossRef] [PubMed]
- Li, P.; Zhang, X.; Wang, W.; Zheng, H.; Yao, X.; Tian, Y.; Zhu, Y.; Cao, W.; Chen, Q.; Cheng, T. Estimating aboveground and organ biomass of plant canopies across the entire season of rice growth with terrestrial laser scanning. Int. J. Appl. Earth Obs. Geoinf. 2020, 91, 102132. [Google Scholar] [CrossRef]
- Jimenez-Berni, J.A.; Deery, D.M.; Rozas-Larraondo, P.; Condon AT, G.; Rebetzke, G.J.; James, R.A.; Bovill, D.; Furbank, R.T.; Sirault, X.R. High throughput determination of plant height, ground cover, and above-ground biomass in wheat with LiDAR. Front. Plant Sci. 2018, 9, 237. [Google Scholar] [CrossRef] [PubMed]
- Llorens, J.; Gil, E.; Llop, J.; Escolà, A. Ultrasonic and LIDAR sensors for electronic canopy characterization in vineyards: Advances to improve pesticide application methods. Sensors 2011, 11, 2177–2194. [Google Scholar] [CrossRef] [PubMed]
- Rinaldi, M.; Llorens, J.; Gil, E. Electronic characterization of the phenological stages of grapevine using a LIDAR sensor. In Precision Agriculture; Springer: Berlin/Heidelberg, Germany, 2013. [Google Scholar]
- Polo, J.R.R.; Sanz, R.; Llorens, J.; Arnó, J.; Escola, A.; Ribes-Dasi, M.; Masip, J.; Camp, F.; Gràcia, F.; Solanelles, F.; et al. A tractor-mounted scanning LIDAR for the non-destructive measurement of vegetative volume and surface area of tree-row plantations: A comparison with conventional destructive measurements. Biosyst. Eng. 2009, 102, 128–134. [Google Scholar] [CrossRef]
- Andújar, D.; Rueda-Ayala, V.; Moreno, H.; Rosell-Polo, J.R.; Escolá, A.; Valero, C.; Gerhards, R.; Fernández-Quintanilla, C.; Dorado, J.; Griepentrog, H.W. Discriminating crop, weeds and soil surface with a terrestrial LIDAR sensor. Sensors 2013, 13, 14662–14675. [Google Scholar] [CrossRef] [PubMed]
- Gregorio, E.; Rocadenbosch, F.; Sanz, R.; Rosell-Polo, J.R. Eye-safe lidar system for pesticide spray drift measurement. Sensors 2015, 15, 3650–3670. [Google Scholar] [CrossRef]
- Gil, E.; Llorens, J.; Llop, J.; Fàbregas, X.; Gallart, M. Use of a terrestrial LIDAR sensor for drift detection in vineyard spraying. Sensors 2013, 13, 516–534. [Google Scholar] [CrossRef]
- Su, Y.; Guo, Q.; Jin, S.; Guan, H.; Sun, X.; Ma, Q.; Hu, T.; Wang, R.; Li, Y. The development and evaluation of a backpack LiDAR system for accurate and efficient forest inventory. IEEE Geosci. Remote Sens. Lett. 2020, 18, 1660–1664. [Google Scholar] [CrossRef]
- Proudman, A.; Ramezani, M.; Digumarti, S.T.; Chebrolu, N.; Fallon, M. Towards real-time forest inventory using handheld LiDAR. Robot. Auton. Syst. 2022, 157, 104240. [Google Scholar] [CrossRef]
- Wang, Y.; Fang, H. Estimation of LAI with the LiDAR technology: A review. Remote Sens. 2020, 12, 3457. [Google Scholar] [CrossRef]
- Zhou, H.; Zhang, J.; Ge, L.; Yu, X.; Wang, Y.; Zhang, C. Research on volume prediction of single tree canopy based on three-dimensional (3D) LiDAR and clustering segmentation. Int. J. Remote Sens. 2021, 42, 738–755. [Google Scholar] [CrossRef]
- Underwood, J.P.; Hung, C.; Whelan, B.; Sukkarieh, S. Mapping almond orchard canopy volume, flowers, fruit and yield using lidar and vision sensors. Comput. Electron. Agric. 2016, 130, 83–96. [Google Scholar] [CrossRef]
- Arnó Satorra, J.; Escolà i Agustí, A.; Vallès Petit, J.M.; Llorens Calveras, J.; Sanz Cortiella, R.; Masip Vilalta, J.; Palacín Roca, J.; Rosell Polo, J.R. Leaf area index estimation in vineyards using a ground-based LiDAR scanner. Precis. Agric. 2013, 14, 290–306. [Google Scholar] [CrossRef]
- Deery, D.M.; Rebetzke, G.J.; Jimenez-Berni, J.A.; Condon, A.G.; Smith, D.J.; Bechaz, K.M.; Bovill, W.D. Ground-based LiDAR improves phenotypic repeatability of above-ground biomass and crop growth rate in wheat. Plant Phenom. 2020, 2020, 290–306. [Google Scholar] [CrossRef] [PubMed]
- Ruhan, A.; Du, W.; Ying, H.; Wei, B.; Shan, Y.; Dai, H. Estimation of aboveground biomass of individual trees by backpack LiDAR based on parameter-optimized quantitative structural models (AdQSM). Forests 2023, 14, 475. [Google Scholar] [CrossRef]
- Zhu, Y.; Sun, G.; Ding, G.; Zhou, J.; Wen, M.; Jin, S.; Zhao, Q.; Colmer, J.; Ding, Y.; Ober, E.S.; et al. Large-scale field phenotyping using backpack LiDAR and GUI-based CropQuant-3D to measure structural responses to different nitrogen treatments in wheat. Plant Physiol. 2021, 187, 716–738. [Google Scholar] [CrossRef]
- Guo, Q.; Wu, F.; Pang, S.; Zhao, X.; Chen, L.; Liu, J.; Xue, B.; Xu, G.; Li, L.; Jing, H.; et al. Crop 3D—A LiDAR based platform for 3D high-throughput crop phenotyping. Sci. China Life Sci. 2018, 61, 328–339. [Google Scholar] [CrossRef]
- Sultan, M.; He, L. Measuring tree canopy density using a LiDAR-guided system for precision spraying. In Proceedings of the Annual International Virtual Meeting, 1. American Society of Agricultural and Biological Engineers, Virtual Meeting, 13–15 July 2020. [Google Scholar]
- Colaço, A.F.; Schaefer, M.; Bramley, R.G. Broadacre mapping of wheat biomass using ground-based LiDAR technology. Remote Sens. 2021, 13, 3218. [Google Scholar] [CrossRef]
- Gu, C.; Zhao, C.; Zou, W.; Yang, S.; Dou, H.; Zhai, C. Innovative leaf area detection models for orchard tree thick canopy based on LiDAR point cloud data. Agriculture 2022, 12, 1241. [Google Scholar] [CrossRef]
- Pagliai, A.; Ammoniaci, M.; Sarri, D.; Lisci, R.; Perria, R.; Vieri, M.; D’Arcangelo, M.E.M.; Storchi, P.; Kartsiotis, S.P. Comparison of Aerial and Ground 3D Point Clouds for Canopy Size Assessment in Precision Viticulture. Remote Sens. 2022, 14, 1145. [Google Scholar] [CrossRef]
- Malambo, L.; Popescu, S.C.; Horne, D.W.; Pugh, N.A.; Rooney, W.L. Automated detection and measurement of individual sorghum panicles using density-based clustering of terrestrial lidar data. ISPRS J. Photogramm. Remote Sens. 2019, 149, 1–13. [Google Scholar] [CrossRef]
- Krus, A.; Van Apeldoorn, D.; Valero, C.; Ramirez, J.J. Acquiring plant features with optical sensing devices in an organic strip-cropping system. Agronomy 2020, 10, 197. [Google Scholar] [CrossRef]
- George, R.; Barrett, B.; Ghamkhar, K. Evaluation of LiDAR scanning for measurement of yield in perennial ryegrass. J. New Zealand Grassl. 2020, 81, 414. [Google Scholar] [CrossRef]
- Zhang, L.; Grift, T.E. A LIDAR-based crop height measurement system for Miscanthus giganteus. Comput. Electron. Agric. 2012, 85, 70–76. [Google Scholar] [CrossRef]
- Tsolakis, N.; Bechtsis, D.; Bochtis, D. Agros: A robot operating system based emulation tool for agricultural robotics. Agronomy 2019, 9, 403. [Google Scholar] [CrossRef]
- Torres-Sánchez, J.; López-Granados, F.; Serrano, N.; Arquero, O.; Peña, J.M. High-throughput 3-D monitoring of agricultural-tree plantations with unmanned aerial vehicle (UAV) technology. PLoS ONE 2015, 10, 0130479. [Google Scholar] [CrossRef]
- Moreno, H.; Valero, C.; Bengochea-Guevara, J.M.; Ribeiro, Á.; Garrido-Izard, M.; Andújar, D. On-ground vineyard reconstruction using a LiDAR-based automated system. Sensors 2020, 20, 1102. [Google Scholar] [CrossRef]
- Willers, J.L.; Wu, J.; O’Hara, C.; Jenkins, J.N. A categorical, improper probability method for combining NDVI and LiDAR elevation information for potential cotton precision agricultural applications. Comput. Electron. Agric. 2012, 82, 15–22. [Google Scholar] [CrossRef]
- Fagua, J.C.; Jantz, P.; Rodriguez-Buritica, S.; Duncanson, L.; Goetz, S.J. Integrating LiDAR, multispectral and SAR data to estimate and map canopy height in tropical forests. Remote Sens. 2019, 11, 2697. [Google Scholar] [CrossRef]
- Wu, X.; Shen, X.; Cao, L.; Wang, G.; Cao, F. Assessment of individual tree detection and canopy cover estimation using unmanned aerial vehicle based light detection and ranging (UAV-LiDAR) data in planted forests. Remote Sens. 2019, 11, 908. [Google Scholar] [CrossRef]
- Jin, S.; Su, Y.; Song, S.; Xu, K.; Hu, T.; Yang, Q.; Wu, F.; Xu, G.; Ma, Q.; Guan, H.; et al. Non-destructive estimation of field maize biomass using terrestrial lidar: An evaluation from plot level to individual leaf level. Plant Methods 2020, 16, 1–19. [Google Scholar] [CrossRef]
- Wiering, N.P.; Ehlke, N.J.; Sheaffer, C.C. Lidar and RGB image analysis to predict hairy vetch biomass in breeding nurseries. Plant Phenome J. 2019, 2, 1–8. [Google Scholar] [CrossRef]
- Duncanson, L.I.; Dubayah, R.O.; Cook, B.D.; Rosette, J.; Parker, G. The importance of spatial detail: Assessing the utility of individual crown information and scaling approaches for lidar-based biomass density estimation. Remote Sens. Environ. 2015, 168, 102–112. [Google Scholar] [CrossRef]
- Brede, B.; Calders, K.; Lau, A.; Raumonen, P.; Bartholomeus, H.M.; Herold, M.; Kooistra, L. Non-destructive tree volume estimation through quantitative structure modelling: Comparing UAV laser scanning with terrestrial LIDAR. Remote Sens. Environ. 2019, 233, 111355. [Google Scholar] [CrossRef]
- Moorthy, S.M.K.; Calders, K.; Vicari, M.B.; Verbeeck, H. Improved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests. IEEE Trans. Geosci. Remote Sens. 2019, 58, 3057–3070. [Google Scholar] [CrossRef]
- Montzka, C.; Donat, M.; Raj, R.; Welter, P.; Bates, J.S. Sensitivity of LiDAR Parameters to Aboveground Biomass in Winter Spelt. Drones 2023, 7, 121. [Google Scholar] [CrossRef]
- Coops, N.C.; Hilker, T.; Wulder, M.A.; St-Onge, B.; Newnham, G.; Siggins, A.; Trofymow, J.A. Estimating canopy structure of Douglas-fir forest stands from discrete-return LiDAR. Trees 2007, 21, 295–310. [Google Scholar] [CrossRef]
- Lefsky, M.A. Lidar remote sensing for ecosystem studies. BioScience 2002, 52, 19–30. [Google Scholar] [CrossRef]
- Liu, S.; Baret, F.; Abichou, M.; Boudon, F.; Thomas, S.; Zhao, K.; Fournier, C.; Andrieu, B.; Irfan, K.; Hemmerlé, M.; et al. Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model. Agric. For. Meteorol. 2017, 247, 12–20. [Google Scholar] [CrossRef]
- Omasa, K.; Hosoi, F.; Konishi, A. 3D lidar imaging for detecting and understanding plant responses and canopy structure. J. Exp. Bot. 2007, 58, 881–898. [Google Scholar] [CrossRef] [PubMed]
- Eitel, J.U.; Magney, T.S.; Vierling, L.A.; Greaves, H.E.; Zheng, G. An automated method to quantify crop height and calibrate satellite-derived biomass using hypertemporal lidar. Remote Sens. Environ. 2016, 187, 414–422. [Google Scholar] [CrossRef]
- Hosoi, F.; Omasa, K. Estimation of vertical plant area density profiles in a rice canopy at different growth stages by high-resolution portable scanning lidar with a lightweight mirror. ISPRS J. Photogramm. Remote Sens. 2012, 74, 11–19. [Google Scholar] [CrossRef]
- Simonson, W.D.; Allen, H.D.; Coomes, D.A. Use of an airborne lidar system to model plant species composition and diversity of Mediterranean oak forests. Conserv. Biol. 2012, 26, 840–850. [Google Scholar] [CrossRef] [PubMed]
- Cao, L.; Coops, N.C.; Sun, Y.; Ruan, H.; Wang, G.; Dai, J.; She, G. Estimating canopy structure and biomass in bamboo forests using airborne LiDAR data. ISPRS J. Photogramm. Remote Sens. 2019, 148, 114–129. [Google Scholar] [CrossRef]
- Eitel, J.U.; Magney, T.S.; Vierling, L.A.; Brown, T.T.; Huggins, D.R. LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status. Field Crops Res. 2014, 159, 21–32. [Google Scholar] [CrossRef]
- Walter, J.D.; Edwards, J.; McDonald, G.; Kuchel, H. Estimating biomass and canopy height with LiDAR for field crop breeding. Front. Plant Sci. 2019, 10, 1145. [Google Scholar] [CrossRef]
- Wu, J.; Xue, X.; Zhang, S.; Qin, W.; Chen, C.; Sun, T. Plant 3D reconstruction based on LiDAR and multi-view sequence images. Int. J. Precis. Agric. Aviat. 2018, 1, 37–43. [Google Scholar] [CrossRef]
- Jin, S.; Su, Y.; Wu, F.; Pang, S.; Gao, S.; Hu, T.; Liu, J.; Guo, Q. Stem–leaf segmentation and phenotypic trait extraction of individual maize using terrestrial LiDAR data. IEEE Trans. Geosci. Remote Sens. 2018, 57, 1336–1346. [Google Scholar] [CrossRef]
- Escolà, A.; Martínez-Casasnovas, J.A.; Rufat, J.; Arnó, J.; Arbonés, A.; Sebé, F.; Pascual, M.; Gregorio, E.; Rosell-Polo, J.R. Mobile terrestrial laser scanner applications in precision fruticulture/horticulture and tools to extract information from canopy point clouds. Precis. Agric. 2017, 18, 111–132. [Google Scholar] [CrossRef]
- Sandonís-Pozo, L.; Llorens, J.; Escolà, A.; Arnó, J.; Pascual, M.; Martínez-Casasnovas, J.A. Satellite multispectral indices to estimate canopy parameters and within-field management zones in super-intensive almond orchards. Precis. Agric. 2022, 23, 2040–2062. [Google Scholar] [CrossRef]
- Anken, T.; Battiato, A.; Seatovic, D.; Meiser, V.; Selbeck, J.; Pforte, F. Canopy-Area Measurement of Plum Trees Using Laser and Near-Infrared Imaging; Agroscope: Bern, Switzerland, 2013. [Google Scholar]
- Dhami, H.; Yu, K.; Xu, T.; Zhu, Q.; Dhakal, K.; Friel, J.; Li, S.; Tokekar, P. Crop height and plot estimation for phenotyping from unmanned aerial vehicles using 3D LiDAR. In Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, 24–30 October 2020. [Google Scholar]
- Yuan, W.; Li, J.; Bhatta, M.; Shi, Y.; Baenziger, P.S.; Ge, Y. Wheat height estimation using LiDAR in comparison to ultrasonic sensor and UAS. Sensors 2018, 18, 3731. [Google Scholar] [CrossRef]
- Ziliani, M.G.; Parkes, S.D.; Hoteit, I.; McCabe, M.F. Intra-season crop height variability at commercial farm scales using a fixed-wing UAV. Remote Sens. 2018, 10, 2007. [Google Scholar] [CrossRef]
- Gao, M.; Yang, F.; Wei, H.; Liu, X. Individual maize location and height estimation in field from UAV-borne LiDAR and RGB images. Remote Sens. 2022, 14, 2292. [Google Scholar] [CrossRef]
- Zhou, Y.; Shi, Z.; Qiu, T.; Yu, S.; Zhang, Q.; Shen, D. Experimental study on morphological characteristics of landslide dams in different shaped valleys. Geomorphology 2022, 400, 108081. [Google Scholar] [CrossRef]
- Sofonia, J.; Shendryk, Y.; Phinn, S.; Roelfsema, C.; Kendoul, F.; Skocaj, D. Monitoring sugarcane growth response to varying nitrogen application rates: A comparison of UAV SLAM LiDAR and photogrammetry. Int. J. Appl. Earth Obs. Geoinf. 2019, 82, 101878. [Google Scholar] [CrossRef]
- Ghamkhar, K.; Irie, K.; Hagedorn, M.; Hsiao, J.; Fourie, J.; Gebbie, S.; Hoyos-Villegas, V.; George, R.; Stewart, A.; Inch, C.; et al. Real-time, non-destructive and in-field foliage yield and growth rate measurement in perennial ryegrass (Lolium perenne L.). Plant Methods 2019, 15, 1–12. [Google Scholar] [CrossRef]
- Tamás, J.; Lehoczky, É.; Fehér, J.; Fórián, T.; Nagy, A.; Bozsik, É.; Gálya, B.; Riczu, P. Airborne hyperspectral and LiDAR data integration for weed detection. In Proceedings of the EGU General Assembly Conference Abstracts, Vienna, Austria, 27 April–2 May 2014. [Google Scholar]
- Pretto, A.; Aravecchia, S.; Burgard, W.; Chebrolu, N.; Dornhege, C.; Falck, T.; Fleckenstein, F.; Fontenla, A.; Imperoli, M.; Khanna, R.; et al. Building an aerial–ground robotics system for precision farming: An adaptable solution. IEEE Robot. Autom. Mag. 2020, 28, 29–49. [Google Scholar] [CrossRef]
- Liu, K.; Dong, X.; Qiu, B. Analysis of cotton height spatial variability based on UAV-LiDAR. Int. J. Precis. Agric. Aviat. 2020, 3, 72–76. [Google Scholar] [CrossRef]
- Nguyen, P.; Badenhorst, P.E.; Shi, F.; Spangenberg, G.C.; Smith, K.F.; Daetwyler, H.D. Design of an unmanned ground vehicle and lidar pipeline for the high-throughput phenotyping of biomass in perennial ryegrass. Remote Sens. 2020, 13, 20. [Google Scholar] [CrossRef]
- Cruz Ulloa, C.; Krus, A.; Barrientos, A.; Del Cerro, J.; Valero, C. Robotic fertilisation using localisation systems based on point clouds in strip-cropping fields. Agronomy 2020, 11, 11. [Google Scholar] [CrossRef]
- Reiser, D.; Vázquez-Arellano, M.; Paraforos, D.S.; Garrido-Izard, M.; Griepentrog, H.W. Iterative individual plant clustering in maize with assembled 2D LiDAR data. Comput. Ind. 2018, 99, 42–52. [Google Scholar] [CrossRef]
- Tsoulias, N.; Paraforos, D.S.; Xanthopoulos, G.; Zude-Sasse, M. Apple shape detection based on geometric and radiometric features using a LiDAR laser scanner. Remote Sens. 2020, 12, 2481. [Google Scholar] [CrossRef]
- Itakura, K.; Hosoi, F. Automatic individual tree detection and canopy segmentation from three-dimensional point cloud images obtained from ground-based lidar. J. Agric. Meteorol. 2018, 74, 109–113. [Google Scholar] [CrossRef]
- Tulldahl, H.M.; Bissmarck, F.; Larsson, H.; Grönwall, C.; Tolt, G. Accuracy evaluation of 3D lidar data from small UAV. In Proceedings of the Electro-Optical Remote Sensing, Photonic Technologies, and Applications IX, Amsterdam, The Netherlands, 22–23 September 2014. [Google Scholar]
- Li, M.; Shamshiri, R.R.; Schirrmann, M.; Weltzien, C.; Shafian, S.; Laursen, M.S. UAV oblique imagery with an adaptive micro-terrain model for estimation of leaf area index and height of maize canopy from 3D point clouds. Remote Sens. 2022, 14, 585. [Google Scholar] [CrossRef]
- Ji, X.; Ding, S.; Wei, X.; Cui, B. Path tracking of unmanned agricultural tractors based on a novel adaptive second-order sliding mode control. J. Frankl. Inst. 2023, 360, 5811–5831. [Google Scholar] [CrossRef]
- Cui, B.; Zhang, J.; Wei, X.; Cui, X.; Sun, Z.; Zhao, Y.; Liu, Y. Improved Information Fusion for Agricultural Machinery Navigation Based on Context-Constrained Kalman Filter and Dual-Antenna RTK. Actuators 2024, 13, 160. [Google Scholar] [CrossRef]
- Mousazadeh, H. A technical review on navigation systems of agricultural autonomous off-road vehicles. J. Terramechan. 2013, 50, 211–232. [Google Scholar] [CrossRef]
- Mao, W.; Liu, H.; Hao, W.; Yang, F.; Liu, Z. Development of a Combined Orchard Harvesting Robot Navigation System. Remote Sens. 2022, 14, 675. [Google Scholar] [CrossRef]
- Wang, S.; Song, J.; Qi, P.; Yuan, C.; Wu, H.; Zhang, L.; Liu, W.; Liu, Y.; He, X. Design and development of orchard autonomous navigation spray system. Front. Plant Sci. 2022, 13, 960686. [Google Scholar] [CrossRef] [PubMed]
- Liu, L.; Liu, Y.; He, X.; Liu, W. Precision Variable-Rate Spraying Robot by Using Single 3D LIDAR in Orchards. Agronomy 2022, 12, 2509. [Google Scholar] [CrossRef]
- Bertoglio, R.; Carini, V.; Arrigoni, S.; Matteucci, M.A. Map-Free LiDAR-Based System for Autonomous Navigation in Vineyards. In Proceedings of the 2023 IEEE European Conference on Mobile Robots (ECMR), Coimbra, Portugal, 27 September 2023. [Google Scholar]
- Hu, X.; Wang, M.; Qian, C.; Huang, C.; Xia, Y.; Song, M. Lidar-based SLAM and autonomous navigation for forestry quadrotors. In Proceedings of the 2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC), Xiamen, China, 10–12 August 2018. [Google Scholar]
- Malavazi, F.B.; Guyonneau, R.; Fasquel, J.B.; Lagrange, S.; Mercier, F. LiDAR-only based navigation algorithm for an autonomous agricultural robot. Comput. Electron. Agric. 2018, 154, 71–79. [Google Scholar] [CrossRef]
- Velasquez, A.E.B.; Higuti, V.A.H.; Guerrero, H.B.; Gasparino, M.V.; Magalhaes, D.V.; Aroca, R.V.; Becker, M. Reactive navigation system based on H∞ control system and LiDAR readings on corn crops. Precis. Agric. 2020, 21, 349–368. [Google Scholar] [CrossRef]
- Byun, S.-W.; Noh, D.; Lee, H.-M. Design of obstacle detection method for autonomous driving in agricultural environments. In Proceedings of the 2022 Thirteenth International Conference on Ubiquitous and Future Networks (ICUFN), Barcelona, Spain, 5–8 July 2022. [Google Scholar]
- Qin, J.; Sun, R.; Zhou, K.; Xu, Y.; Lin, B.; Yang, L.; Chen, Z.; Wen, L.; Wu, C. Lidar-Based 3D Obstacle Detection Using Focal Voxel R-CNN for Farmland Environment. Agronomy 2023, 13, 650. [Google Scholar] [CrossRef]
- Kong, W.Y.; Hu, G.R.; Zhang, S.; Zhou, J.G.; Gao, Z.N.; Chen, J. Research on agricultural vehicle safety warning system based on LiDAR. INMATEH Agric. Eng. 2022, 68, 230–242. [Google Scholar] [CrossRef]
- Jiang, A.; Ahamed, T. Navigation of an Autonomous Spraying Robot for Orchard Operations Using LiDAR for Tree Trunk Detection. Sensors 2023, 23, 4808. [Google Scholar] [CrossRef] [PubMed]
- McGlinchy, J.; Van Aardt, J.A.; Erasmus, B.; Asner, G.P.; Mathieu, R.; Wessels, K.; Knapp, D.; Kennedy-Bowdoin, T.; Rhody, H.; Kerekes, J.P.; et al. Extracting structural vegetation components from small-footprint waveform lidar for biomass estimation in savanna ecosystems. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2013, 7, 480–490. [Google Scholar] [CrossRef]
- Zhu, Y.; Zhao, C.; Yang, H.; Yang, G.; Han, L.; Li, Z.; Feng, H.; Xu, B.; Wu, J.; Lei, L. Estimation of maize above-ground biomass based on stem-leaf separation strategy integrated with LiDAR and optical remote sensing data. Plant Biol. 2019, 7, e7593. [Google Scholar] [CrossRef] [PubMed]
- Luo, S.; Wang, C.; Xi, X.; Nie, S.; Fan, X.; Chen, H.; Yang, X.; Peng, D.; Lin, Y.; Zhou, G. Combining hyperspectral imagery and LiDAR pseudo-waveform for predicting crop LAI, canopy height and above-ground biomass. Ecol. Indic. 2019, 102, 801–812. [Google Scholar] [CrossRef]
- De Almeida, C.T.; Galvao, L.S.; Ometto, J.P.H.B.; Jacon, A.D.; de Souza Pereira, F.R.; Sato, L.Y.; Lopes, A.P.; de Alencastro Graça, P.M.L.; de Jesus Silva, C.V.; Longo, M.; et al. Combining LiDAR and hyperspectral data for aboveground biomass modeling in the Brazilian Amazon using different regression algorithms. Remote Sens. Environ. 2019, 232, 111323. [Google Scholar] [CrossRef]
- Zhu, W.; Sun, Z.; Peng, J.; Huang, Y.; Li, J.; Zhang, J.; Yang, B.; Liao, X. Estimating maize above-ground biomass using 3D point clouds of multi-source unmanned aerial vehicle data at multi-spatial scales. Remote Sens. 2019, 11, 2678. [Google Scholar] [CrossRef]
- Anderson, S.L.; Murray, S.C.; Malambo, L.; Ratcliff, C.; Popescu, S.; Cope, D.; Chang, A.; Jung, J.; Thomasson, J.A. Prediction of maize grain yield before maturity using improved temporal height estimates of unmanned aerial systems. Plant Phenome J. 2019, 2, 1–15. [Google Scholar] [CrossRef]
- Nguyen, C.; Sagan, V.; Bhadra, S.; Moose, S. UAV Multisensory Data Fusion and Multi-Task Deep Learning for High-Throughput Maize Phenotyping. Sensors 2023, 23, 1827. [Google Scholar] [CrossRef] [PubMed]
- Rodriguez Padilla, A.M.; Quintana, M.A.; Prado, R.M.; Aguilar, B.J.; Shea, T.A.; Oskin, M.E.; Garcia, L. Near-Field High-Resolution Maps of the Ridgecrest Earthquakes from Aerial Imagery. Seismol. Soc. Am. 2022, 93, 494–499. [Google Scholar] [CrossRef]
- Sun, S.; Li, C. In-field high throughput phenotyping and phenotype data analysis for cotton plant growth using LiDAR. In Proceedings of the Annual International Meeting American Society of Agricultural and Biological Engineers, Spokane, DC, USA, 16–19 July 2017. [Google Scholar]
- Vidoni, R.; Gallo, R.; Ristorto, G.; Carabin, G.; Mazzetto, F.; Scalera, L.; Gasparetto, A. ByeLab: An agricultural mobile robot prototype for proximal sensing and precision farming. In Proceedings of the ASME International Mechanical Engineering Congress and Exposition, Tampa, FL, USA, 3–9 November 2017. [Google Scholar]
- Lozic, E. Application of Airborne LiDAR Data to the Archaeology of Agrarian Land Use: The Case Study of the Early Medieval Microregion of Bled (Slovenia). Remote Sens. 2021, 13, 3228. [Google Scholar] [CrossRef]
- Alijani, Z.; Meloche, J.; McLaren, A.; Lindsay, J.; Roy, A.; Berg, A. A comparison of three surface roughness characterization techniques: Photogrammetry, pin profiler, and smartphone-based LiDAR. Int. J. Digit. Earth 2022, 15, 2422–2439. [Google Scholar] [CrossRef]
- Davenport, I.J.; Holden, N.; Gurney, R.J. Gurney Characterizing errors in airborne laser altimetry data to extract soil roughness. IEEE Trans. Geosci. Remote Sens. 2004, 42, 2130–2141. [Google Scholar] [CrossRef]
- Foldager, F.F.; Pedersen, J.M.; Haubro Skov, E.; Evgrafova, A.; Green, O. Lidar-based 3d scans of soil surfaces and furrows in two soil types. Sensors 2019, 19, 661. [Google Scholar] [CrossRef] [PubMed]
- Hollaus, M.; Aubrecht, C.; Höfle, B.; Steinnocher, K.; Wagner, W. Roughness mapping on various vertical scales based on full-waveform airborne laser scanning data. Remote Sens. 2011, 3, 503–523. [Google Scholar] [CrossRef]
- Cassidy, R.; Thomas, I.A.; Higgins, A.; Bailey, J.S.; Jordan, P. A carrying capacity framework for soil phosphorus and hydrological sensitivity from farm to catchment scales. Sci. Total Environ. 2019, 687, 277–286. [Google Scholar] [CrossRef] [PubMed]
- Southee, F.M.; Treitz, P.M.; Scott, N.A. Application of lidar terrain surfaces for soil moisture modeling. Photogramm. Eng. Remote Sens. 2012, 78, 1241–1251. [Google Scholar] [CrossRef]
- Demelezi, F.; Gálya, B.; Tamás, J.; Demelezi, I.; Nagy, A. Evaluation of soil water management properties based on LiDAR data and soil analyses at farm level. Nat. Resour. Sustain. Dev. 2019, 2, 160–173. [Google Scholar] [CrossRef]
- Kemppinen, J.; Niittynen, P.; Riihimäki, H.; Luoto, M. Modelling soil moisture in a high-latitude landscape using LiDAR and soil data. Earth Surf. Process. Landf. 2018, 43, 1019–1031. [Google Scholar] [CrossRef]
- Saeys, W.; Lenaerts, B.; Craessaerts, G.; De Baerdemaeker, J. Estimation of the crop density of small grains using LiDAR sensors. Biosyst. Eng. 2009, 102, 22–30. [Google Scholar] [CrossRef]
- Selbeck, J.; Dworak, V.; Ehlert, D. Testing a vehicle-based scanning lidar sensor for crop detection. Can. J. Remote Sens. 2010, 36, 24–35. [Google Scholar] [CrossRef]
- Deremetz, M.; Lenain, R.; Laneurit, J.; Debain, C.; Peynot, T. Autonomous Human Tracking using UWB sensors for mobile robots: An Observer-Based approach to follow the human path. In Proceedings of the 2020 IEEE Conference on Control Technology and Applications (CCTA), Montreal, QC, Canada, 24–26 August 2020. [Google Scholar]
- Prins, A.J.; Van Niekerk, A. Crop type mapping using LiDAR, Sentinel-2 and aerial imagery with machine learning algorithms. Geo-Spat. Inf. Sci. 2021, 24, 215–227. [Google Scholar] [CrossRef]
- Canata, T.F.; Molin, J.P.; Sousa, R.V.d. A measurement system based on lidar technology to characterize the canopy of sugarcane plants. Eng. Agríc. 2019, 39, 240–247. [Google Scholar] [CrossRef]
- Sun, Z.; Li, Q.; Jin, S.; Song, Y.; Xu, S.; Wang, X.; Cai, J.; Zhou, Q.; Ge, Y.; Zhang, R.; et al. Simultaneous prediction of wheat yield and grain protein content using multitask deep learning from time-series proximal sensing. Plant Phenom. 2022, 2022, 9757948. [Google Scholar] [CrossRef]
- Gené-Mola, J.; Gregorio, E.; Cheein, F.A.; Guevara, J.; Llorens, J.; Sanz-Cortiella, R.; Escolà, A.; Rosell-Polo, J.R. Fruit detection, yield prediction and canopy geometric characterization using LiDAR with forced air flow. Comput. Electron. Agric. 2020, 168, 105121. [Google Scholar] [CrossRef]
- Gebbers, R.; Ehlert, D.; Adamek, R. Rapid mapping of the leaf area index in agricultural crops. Agron. J. 2011, 103, 1532–1541. [Google Scholar] [CrossRef]
- Tilly, N.; Aasen, H.; Bareth, G. Fusion of plant height and vegetation indices for the estimation of barley biomass. Remote Sens. 2015, 7, 11449–11480. [Google Scholar] [CrossRef]
- Li, W.; Niu, Z.; Huang, N.; Wang, C.; Gao, S.; Wu, C. Airborne LiDAR technique for estimating biomass components of maize: A case study in Zhangye City, Northwest China. Ecol. Indic. 2015, 57, 486–496. [Google Scholar] [CrossRef]
- Iqbal, J.; Xu, R.; Sun, S.; Li, C. Simulation of an autonomous mobile robot for LiDAR-based in-field phenotyping and navigation. Robotics 2020, 9, 46. [Google Scholar] [CrossRef]
- Shang, Y.; Wang, H.; Qin, W.; Wang, Q.; Liu, H.; Yin, Y.; Song, Z.; Meng, Z. Design and Test of Obstacle Detection and Harvester Pre-Collision System Based on 2D Lidar. Agronomy 2023, 13, 388. [Google Scholar] [CrossRef]
- Geer, L.; Gu, D.; Wang, F.; Mohan, V.; Dowling, R. Novel Software Architecture for an Autonomous Agricultural Robotic Fruit Harvesting System. In Proceedings of the 2022 27th International Conference on Automation and Computing (ICAC), Bristol, UK, 1–3 September 2022. [Google Scholar]
- Vrochidou, E.; Tziridis, K.; Nikolaou, A.; Kalampokas, T.; Papakostas, G.A.; Pachidis, T.P.; Mamalis, S.; Koundouras, S.; Kaburlasos, V.G. An autonomous grape-harvester robot: Integrated system architecture. Electronics 2021, 10, 1056. [Google Scholar] [CrossRef]
- Xiong, Y.; Ge, Y.; Grimstad, L.; From, P.J. An autonomous strawberry-harvesting robot: Design, development, integration, and field evaluation. J. Field Robot. 2020, 37, 202–224. [Google Scholar] [CrossRef]
- Long, D.S.; McCallum, J.D. Mapping straw yield using on-combine light detection and ranging (lidar). Int. J. Remote Sens. 2013, 34, 6121–6134. [Google Scholar] [CrossRef]
- Ehlert, D.; Adamek, R.; Horn, H.-J. Laser rangefinder-based measuring of crop biomass under field conditions. Precis. Agric. 2009, 10, 395–408. [Google Scholar] [CrossRef]
- Long, D.S.; McCallum, J.D. On-combine, multi-sensor data collection for post-harvest assessment of environmental stress in wheat. Precis. Agric. 2015, 16, 492–504. [Google Scholar] [CrossRef]
- Mulley, M.; Kooistra, L.; Bierens, L. High-resolution multisensor remote sensing to support date palm farm management. Agriculture 2019, 9, 26. [Google Scholar] [CrossRef]
- Tilly, N.; Hoffmeister, D.; Cao, Q.; Huang, S.; Lenz-Wiedemann, V.; Miao, Y.; Bareth, G. Multitemporal crop surface models: Accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice. J. Appl. Remote Sens. 2014, 8, 083671. [Google Scholar] [CrossRef]
- Loudermilk, E.L.; Hiers, J.K.; O’Brien, J.J.; Mitchell, R.J.; Singhania, A.; Fernandez, J.C.; Cropper, W.P.; Slatton, K.C. Ground-based LIDAR: A novel approach to quantify fine-scale fuelbed characteristics. Int. J. Wildland Fire 2009, 18, 676–685. [Google Scholar] [CrossRef]
- Guo, T.; Fang, Y.; Cheng, T.; Tian, Y.; Zhu, Y.; Chen, Q.; Qiu, X.; Yao, X. Detection of wheat height using optimized multi-scan mode of LiDAR during the entire growth stages. Comput. Electron. Agric. 2019, 165, 104959. [Google Scholar] [CrossRef]
- Luo, S.; Liu, W.; Zhang, Y.; Wang, C.; Xi, X.; Nie, S.; Ma, D.; Lin, Y.; Zhou, G. Maize and soybean heights estimation from unmanned aerial vehicle (UAV) LiDAR data. Comput. Electron. Agric. 2021, 182, 106005. [Google Scholar] [CrossRef]
- Wang, C.; Nie, S.; Xi, X.; Luo, S.; Sun, X. Estimating the biomass of maize with hyperspectral and LiDAR data. Remote Sens. 2016, 9, 11. [Google Scholar] [CrossRef]
- Zhou, C.; Ye, H.; Sun, D.; Yue, J.; Yang, G.; Hu, J. An automated, high-performance approach for detecting and characterizing broccoli based on UAV remote-sensing and transformers: A case study from Haining, China. Int. J. Appl. Earth Obs. Geoinf. 2022, 114, 103055. [Google Scholar] [CrossRef]
- Canata, T.F.; Molin, J.P.; Sousa, R.V.D. LiDAR technology for monitoring sugarcane production. Eng. Agric. 2019, 39, 4040. [Google Scholar]
- Huang, X.; Zheng, S.; Zhu, N. High-throughput legume seed phenotyping using a handheld 3D laser scanner. Remote Sens. 2022, 14, 431. [Google Scholar] [CrossRef]
- Harkel, J.; Bartholomeus, H.; Kooistra, L. Biomass and Crop Height Estimation of Different Crops Using UAV-Based Lidar. Remote Sens. 2020, 12, 17. [Google Scholar] [CrossRef]
- Thapa, S.; Zhu, F.; Walia, H.; Yu, H.; Ge, Y. A Novel LiDAR-Based Instrument for High-Throughput, 3D Measurement of Morphological Traits in Maize and Sorghum. Sensors 2018, 18, 1187. [Google Scholar] [CrossRef]
- Lin, C.; Hu, F.; Peng, J.; Wang, J.; Zhai, R. Segmentation and stratification methods of field maize terrestrial LiDAR point cloud. Agriculture 2022, 12, 1450. [Google Scholar] [CrossRef]
- Hoffmeister, D.; Waldhoff, G.; Curdt, C.; Tilly, N.; Bendig, J.; Bareth, G. Spatial variability detection of crop height in a single field by terrestrial laser scanning. In Precision Agriculture ’13; Springer: Berlin/Heidelberg, Germany, 2013. [Google Scholar]
- Zhou, L.; Gu, X.; Cheng, S.; Yang, G.; Shu, M.; Sun, Q. Analysis of plant height changes of lodged maize using UAV-LiDAR data. Agriculture 2020, 10, 146. [Google Scholar] [CrossRef]
- Chiappini, S.; Giorgi, V.; Neri, D.; Galli, A.; Marcheggiani, E.; Malinverni, E.S.; Pierdicca, R.; Balestra, M. Innovation in olive-growing by Proximal sensing LiDAR for tree volume estimation. In Proceedings of the 2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Perugia, Italy, 3–5 November 2022. [Google Scholar]
- Vescovo, L.; Gianelle, D.; Dalponte, M.; Miglietta, F.; Carotenuto, F.; Torresan, C. Hail defoliation assessment in corn (Zea mays L.) using airborne LiDAR. Field Crops Res. 2016, 196, 426–437. [Google Scholar] [CrossRef]
- Weiss, U.; Biber, P. Plant detection and mapping for agricultural robots using a 3D LIDAR sensor. Robot. Auton. Syst. 2011, 59, 265–273. [Google Scholar] [CrossRef]
- Bin Mat Seri, A.D.I.; bin Mohd Kassim, M.S.; Sajak, A.A.B. Development of Virescens Fresh Fruit Bunch Ripeness Prediction Using LiDAR for Smart Agriculture. In Proceedings of the 2021 IEEE Region 10 Symposium (TENSYMP), Jeju, Republic of Korea, 23–25 August 2021; pp. 1–8. [Google Scholar]
- Su, Y.; Wu, F.; Ao, Z.; Jin, S.; Qin, F.; Liu, B.; Pang, S.; Liu, L.; Guo, Q. Evaluating maize phenotype dynamics under drought stress using terrestrial lidar. Plant Methods 2019, 15, 11. [Google Scholar] [CrossRef] [PubMed]
- Goetz, K.T.; Soitinaho, R.; Oksanen, T. Ploughing furrow recognition for onland ploughing using a 3D-LiDAR sensor. Comput. Electron. Agric. 2023, 210, 107941. [Google Scholar] [CrossRef]
- Panjvani, K.; Dinh, A.V.; Wahid, K.A. LiDARPheno–A low-cost LiDAR-based 3D scanning system for leaf morphological trait extraction. Front. Plant Sci. 2019, 10, 147. [Google Scholar] [CrossRef]
- Hosoi, F.; Nakabayashi, K.; Omasa, K. 3-D modeling of tomato canopies using a high-resolution portable scanning lidar for extracting structural information. Sensors 2011, 11, 2166–2174. [Google Scholar] [CrossRef]
- Wang, H.; Zhang, W.; Yang, G.; Lei, L.; Han, S.; Xu, W.; Chen, R.; Zhang, C.; Yang, H. Maize Ear Height and Ear–Plant Height Ratio Estimation with LiDAR Data and Vertical Leaf Area Profile. Remote Sens. 2023, 15, 964. [Google Scholar] [CrossRef]
- Hadas, E.; Jozkow, G.; Walicka, A.; Borkowski, A. Apple orchard inventory with a LiDAR equipped unmanned aerial system. Int. J. Appl. Earth Obs. Geoinf. 2019, 82, 101911. [Google Scholar] [CrossRef]
- Estornell, J.; Ruiz, L.A.; Velázquez-Martí, B.; López-Cortés, I.; Salazar, D.; Fernández-Sarría, A. Estimation of pruning biomass of olive trees using airborne discrete-return LiDAR data. Biomass Bioenergy 2015, 81, 315–321. [Google Scholar] [CrossRef]
- Estornell, J.; Hadas, E.; Martí, J.; López-Cortés, I. Tree extraction and estimation of walnut structure parameters using airborne LiDAR data. Int. J. Appl. Earth Obs. Geoinf. 2021, 96, 102273. [Google Scholar] [CrossRef]
- Saha, K.K.; Tsoulias, N.; Weltzien, C.; Zude-Sasse, M. Estimation of vegetative growth in strawberry plants using mobile LiDAR laser scanner. Horticulturae 2022, 8, 90. [Google Scholar] [CrossRef]
- Murcia, H.F.; Tilaguy, S.; Ouazaa, S. Development of a Low-Cost System for 3D Orchard Mapping Integrating UGV and LiDAR. Plants 2021, 10, 2804. [Google Scholar] [CrossRef] [PubMed]
- Tsoulias, N.; Xanthopoulos, G.; Fountas, S.; Zude-Sasse, M. Effects of soil ECa and LiDAR-derived leaf area on yield and fruit quality in apple production. Biosyst. Eng. 2022, 223, 182–199. [Google Scholar] [CrossRef]
- Berk, P.; Stajnko, D.; Belsak, A.; Hocevar, M. Digital evaluation of leaf area of an individual tree canopy in the apple orchard using the LIDAR measurement system. Comput. Electron. Agric. 2020, 169, 105158. [Google Scholar] [CrossRef]
- Wang, M.; Dou, H.; Sun, H.; Zhai, C.; Zhang, Y.; Yuan, F. Calculation Method of Canopy Dynamic Meshing Division Volumes for Precision Pesticide Application in Orchards Based on LiDAR. Agronomy 2023, 13, 1077. [Google Scholar] [CrossRef]
- Sanz, R.; Llorens, J.; Escolà, A.; Arnó, J.; Planas, S.; Román, C.; Rosell-Polo, J.R. LIDAR and non-LIDAR-based canopy parameters to estimate the leaf area in fruit trees and vineyard. Agric. For. Meteorol. 2018, 260, 229–239. [Google Scholar] [CrossRef]
- Saha, K.K.; Zude-Sasse, M. Estimation of chlorophyll content in banana during shelf life using LiDAR laser scanner. Postharvest Biol. Technol. 2022, 192, 112011. [Google Scholar] [CrossRef]
- Yuan, W.; Choi, D.; Bolkas, D. GNSS-IMU-assisted colored ICP for UAV-LiDAR point cloud registration of peach trees. Comput. Electron. Agric. 2022, 197, 106966. [Google Scholar] [CrossRef]
- Colaço, A.F.; Trevisan, R.G.; Molin, J.P.; Rosell-Polo, J.R.; Escolà, A. Orange tree canopy volume estimation by manual and LiDAR-based methods. Adv. Anim. Biosci. 2017, 8, 477–480. [Google Scholar] [CrossRef]
- Bietresato, M.; Carabin, G.; Vidoni, R.; Gasparetto, A.; Mazzetto, F. Evaluation of a LiDAR-based 3D-stereoscopic vision system for crop-monitoring applications. Comput. Electron. Agric. 2016, 124, 1–13. [Google Scholar] [CrossRef]
- Keightley, K.E.; Bawden, G.W. 3D volumetric modeling of grapevine biomass using Tripod LiDAR. Comput. Electron. Agric. 2010, 74, 305–312. [Google Scholar] [CrossRef]
- Zhang, C.; Yang, G.; Jiang, Y.; Xu, B.; Li, X.; Zhu, Y.; Lei, L.; Chen, R.; Dong, Z.; Yang, H. Apple tree branch information extraction from terrestrial laser scanning and backpack-lidar. Remote Sens. 2020, 12, 3592. [Google Scholar] [CrossRef]
- Liu, X.; Wang, Y.; Kang, F.; Yue, Y.; Zheng, Y. Canopy parameter estimation of citrus grandis var. Longanyou based on Lidar 3d point clouds. Remote Sens. 2021, 13, 1859. [Google Scholar] [CrossRef]
- Knapp-Wilson, J.; Bohn Reckziegel, R.; Thapa Magar, S.; Bucksch, A.; Chavez, D.J. Three-dimensional phenotyping of peach tree-crown architecture utilizing terrestrial laser scanning. Plant Phenome J. 2023, 6, e20073. [Google Scholar] [CrossRef]
- Mahmud, M.S.; Zahid, A.; He, L.; Choi, D.; Krawczyk, G.; Zhu, H.; Heinemann, P. Development of a LiDAR-guided section-based tree canopy density measurement system for precision spray applications. Comput. Electron. Agric. 2021, 182, 106053. [Google Scholar] [CrossRef]
- You, A.; Grimm, C.; Silwal, A.; Davidson, J.R. Semantics-guided skeletonization of sweet cherry trees for robotic pruning. arXiv 2021, arXiv:2103.02833. [Google Scholar] [CrossRef]
- Hu, F.; Lin, C.; Peng, J.; Wang, J.; Zhai, R. Rapeseed leaf estimation methods at field scale by using terrestrial LiDAR point cloud. Agronomy 2022, 12, 2409. [Google Scholar] [CrossRef]
Crop | Crop Management Practices | Important Findings | Ref. |
---|---|---|---|
Sorghum | Growth monitoring | UAV–LiDAR and RGB photogrammetry accurately assessed canopy height (CH) and LAI in sorghum. LiDAR outperformed photogrammetry, achieving superior accuracy with an R2 of 0.975 and a RMSE of 5.94% for CH. | [71] |
Pea, Chickpea | Crop height estimation | Highly significant correlations (p < 0.0001) were found between LiDAR-estimated and manually measured plant heights, with correlation coefficients (r) of 0.74 for chickpeas and 0.91 for peas. | [72] |
Atlas, Red Carina, Pasto | Height, biomass, and grain estimation | The LiDAR-based hyperspectral physiological reflectance index (PRI) demonstrated superior performance, distinguishing between salt-affected and treated plants. The overall dataset achieved an R2 of 0.46, with specific subgroups reaching an R2 of 0.64. | [73] |
Potato | Monitoring agricultural biomass and plant growth | The LiDAR scanner demonstrated a strong correlation with field-measured parameters, with a high R2 of 0.89 and a low RMSE of 0.028 m for PH, and an R2 of 0.81 with an RMSE of 31.65% for AGB. | [74] |
Sugarcane | Prediction of biomass and leaf N2 content | Crop growth parameters were monitored, including height, density, and vegetation indices. Predictive models were evaluated based on multispectral predictors alone, LiDAR predictors alone, and a fusion of both, then compared against an NDVI benchmark. The multispectral model showed slightly superior performance (R2 = 0.57) compared to the LiDAR model (R2 = 0.52), with both models surpassing the NDVI benchmark (R2 = 0.34). | [75] |
Sugarcane | Estimation of yield and aboveground fresh weight | Six regression algorithms (MLR, SMR, GLM, GBM, KRLS, and RFR) were employed to construct the sugarcane aboveground fresh weight (AFW) model using LiDAR data. Results showed that RFR outperformed other models’ prediction accuracy (R2 = 0.96, RMSE = 1.27 kg m−2). The final sugarcane AFW distribution maps demonstrated strong agreement with detected values (R2 = 0.97, RMSE = 1.33 kg m−2). | [76] |
Sorghum | Biomass estimation and prediction | Two biomass prediction methods were explored: one utilized LSM to predict biomass from LiDAR point cloud data directly, and the other employed the APSIM crop simulation model. The LiDAR approach yielded R2 values of 0.48 and 0.55 for SVR and MLP, respectively, while the APSIM model achieved R2 of 0.31 and 0.67 for SVR and MLP, respectively. | [77] |
Avocado, Macadamia, Mango | Orchard and tree crown assessment | The study found that crown structure measurements, particularly those based on the top of the crown, exhibited strong consistency between ALS and TLS data. Crown area measurements showed the highest correlation (R2 = 0.997) between the two data sources. The linear model’s RMSE for maximum crown height derived from ALS and TLS data was 0.29 m, with an R2 of 0.99. | [78] |
Wheat | Monitoring of CH, biomass, and N2 uptake | The 95th percentile of normalized LiDAR points showed a strong correlation (R2 = 0.88) with manually measured crop heights and (R2 = 0.92) with crop heights obtained from a UAV system using optical imaging, out of the 57 UAV–LiDAR metrics that were analyzed. | [79] |
Sorghum | Biomass prediction | Geometric features extracted from LiDAR data yielded dependable and precise biomass predictions. The 750–1100 nm spectral range proved to be the most informative for biomass prediction, with R2 values for end-of-season biomass ranging from 0.64 to 0.89. | [80] |
Component | Description | Implementation | Limitations |
---|---|---|---|
LiDAR Sensors | Emits laser pulses and measures the time it takes for them to return | Mounted on aircraft (e.g., UAVs and planes) | Limited by battery life and flight duration (especially for UAVs) |
Global Positioning System (GPS) | Provides precise location information | Integrated with LiDAR sensor | Signal interference can affect accuracy, especially in dense vegetation |
Inertial Measurement Unit (IMU) | Measures the rate of acceleration and changes in rotational attributes | Works with GPS to provide accurate positioning data | Sensor drift over time can affect data accuracy |
Data Storage | Onboard storage system for capturing LiDAR data | High-capacity storage systems onboard | Storage capacity may limit the amount of data collected during a single flight. |
UAV (Unmanned Aerial Vehicle) | Aircraft without a human pilot onboard are used for carrying LiDAR sensors | Suitable for small to medium-scale areas | Limited flight time and payload capacity; subject to weather conditions |
Manned Aircraft | Airplanes or helicopters piloted by humans are used for carrying LiDAR sensors. | Suitable for large-scale mapping | Higher operational costs and regulatory restrictions |
Crop | Crop Management Practices | Important Findings | Ref. |
---|---|---|---|
Barley | Plant-height measurement | A comparison was made between TLS and UAV-based imaging for CSM-derived plant height in crops using analyses based on polygon grids. The results revealed a high correlation between TLS and UAV-derived plant height (R2 = 0.91), with a 4.81% higher coefficient of variance observed in TLS compared to UAV data. | [89] |
Wheat | High-throughput phenotyping of plant height | The comparison of PH derived from LiDAR and structure from motion revealed high consistency, with a strong correlation (R2 ≈ 0.98) and minimal RMSE values (RMSE = 8.4 cm). LiDAR and structure from motion exhibited high repeatability (H2) in plant height. | [90] |
Rice | Biomass estimation for individual organs and aboveground biomass (AGW) | Three regression approaches, SMLR, RF, and LME modeling, were assessed for biomass estimation using an extensive TLS. The LME model exhibited the most significant improvement in panicle biomass, showing a 0.74 increase in R2 (LME: R2 = 0.90, SMLR: R2 = 0.16) and a 1.15 t/ha decrease in RMSE (LME: RMSE = 0.79 t/ha, SMLR: RMSE = 2.94 t/ha). In comparison to SMLR and RF, LME modeling provided similar AGB estimation accuracies for pre-heading stages but notably higher accuracies for post-heading stages (LME: R2 = 0.63, RMSE = 2.27 t/ha; SMLR: R2 = 0.42, RMSE = 2.42 t/ha; RF: R2 = 0.57, RMSE = 2.80 t/ha). | [91] |
Wheat | Estimation of plant height, ground cover, and AGW | Canopy height strongly correlated with LiDAR (R2 = 0.99, RMSE = 0.017 m). In contrast to NDVI, LiDAR remained unaffected by saturation at high ground cover and exhibited a strong association (R2 = 0.92, slope = 1.02) at ground cover above 0.8. AGW estimation employed 3D voxel index (3DVI) and 3D profile index (3DPI), with the strongest associations with biomass observed for 3DPI (R2 = 0.93) and 3DVI (R2 = 0.92). | [92] |
Vineyard | Canopy characterization (crop height, width, volume, and leaf area) | Ultrasonic and LiDAR sensors were compared to manual canopy measurements. Strong correlations were found between crop volume values from ultrasonic sensors and leaf area index (R2 = 0.51) and canopy volumes measured by ultrasonic and LiDAR sensors (R2 = 0.52). LiDAR accurately predicted canopy volume. | [93] |
Grapevine | Canopy geometry characterization | Significant correlations emerged between LIDAR impacts and LAI during each growth stage. The estimated values of tree row volume (R2 = 0.99) and leaf wall area (R2 = 0.95) exhibited statistical significance relative to the vine’s growth stage. | [94] |
Apple, Pear, Vineyards | Measurement of non-destructive vegetative volume and surface area of tree row | Strong correlations were found between manual and LIDAR-based measurements of vegetative volume in tree-row plantations, especially with the tree area index (TAI) parameter. A significant correlation (R2 = 0.814) was also observed between LIDAR volume and foliar area. | [95] |
Maize | Detection and discrimination of plants and weeds | A high correlation (R2 = 0.75) existed between LiDAR-measured height and actual plant height. Achieving up to 95% accuracy, the LiDAR-based sensor effectively discriminated vegetation from the ground. Weed discrimination success was demonstrated through canonical discriminant analysis (CDA) with a rate of 72.2%. | [96] |
Vineyard, Apple | Monitoring of pesticide clouds | The LiDAR system recorded the mid-range spray drift with a 2.4 m distance resolution and a 100 ms temporal resolution at the highest pulse repetition frequency. | [97] |
Vineyard | Drift detection in vineyard spraying | At an airflow rate of 34,959 m3/h, correlation coefficients for conventional nozzles ranged from 0.87 to 0.91, while for air injection nozzles, the range was from 0.88 to 0.40. At 27,507 m3/h, conventional nozzles showed coefficients between 0.85 and 0.94, whereas air injection nozzles ranged from 0.07 to 0.88. Finally, at 6423 m3/h, conventional nozzles had coefficients from 0.93 to 0.98. | [98] |
Crop | Crop Management Practices | Important Findings | Ref. |
---|---|---|---|
Apple | Estimation of tree canopy density | The 2D algorithm identified peak points at the canopy center, correlating with apple variety, tree age, and tall spindle form. Notably, the middle section exhibited higher canopy points than the top and bottom. The 3D algorithm excelled in evaluating tree canopy point density, surpassing the 2D version. Ensuring precise alignment during scanning is vital to avoid experimental errors. | [109] |
Wheat | Crop biomass estimation | A proximal active reflectance sensor offering spectral indices and crop height estimates was compared to the LiDAR system. The correlation between LiDAR-derived crop height and crop biomass was 0.79, revealing substantial variability in biomass across the field. This suggested the potential of LiDAR technology for large-scale operations and site-specific management. | [110] |
Apple | Leaf area detection | A test platform was constructed to measure orchard tree canopy leaf areas manually. Polynomial regression, BPNN, and PLSR algorithms were employed to analyze the relationship between canopy point clouds and leaf areas. The BP neural network (86.1% test, 73.6% verification accuracy) and PLSR (78.46% test, 60.3% verification accuracy) outperformed the Fourier function in polynomial regression (59.73% accuracy). | [111] |
Vineyard | Estimation of canopy size parameters (thickness, height, and volume) and LAI | UAVs, MLSs, and mobile apps (MA) effectively estimated canopy size variations. Strong correlations (R2 > 0.7) were observed, with the highest at R2 = 0.78 (UAV vs. MLS) for canopy volumes. Height data showed robust correlations (R2 = 0.86, MA vs. MLS), while thickness data had weaker correlations (R2 = 0.48, UAV vs. MLS). LAI demonstrated moderate but consistent correlations with canopy volumes, ranging from R2 = 0.69 (LAI vs. UAV) to R2 = 0.74 (LAI vs. V MLS). | [112] |
Sorghum | Detection and measurement of estimation of individual sorghum panicles | Panicles were identified with 89.3% overall accuracy, encompassing a 10.7% omission and 14.3% commission rate. Estimated panicle dimensions demonstrated a strong correlation with LiDAR-derived measurements (panicle length: r = 0.88, RMSE = 3.10 cm; panicle width: r = 0.79, RMSE = 1.67 cm; plant height: r = 1.00, RMSE = 0.80 cm). Comparison with harvested panicle data revealed moderate-to-high correlations (panicle length: r = 0.79, RMSE = 2.48 cm; panicle width: r = 0.63, RMSE = 1.49 cm; plant height: r = 0.86, RMSE = 11.4 cm). | [113] |
Cabbage, Leek, Potato, Wheat | Canopy estimation | Soil and plant segregation was accomplished by calculating weighted sums, eliminating the need for additional sensor data. This dynamic method extracted vegetation from point clouds in strips with varying coverage and sizes. The resulting vegetation clouds were validated against drone imagery, confirming a precise match with all green areas. | [114] |
Ryegrass | Estimation of fresh weight and dry matter yield | R2 between FWY and seasons (winter, spring, summer, and autumn) were 0.81, 0.92, 0.94, and 0.90, respectively. Similarly, the R2 values between DMY and the seasons were 0.87, 0.73, 0.87, and 0.79, respectively. These results suggest that LIDAR estimation of DMY is accurate within seasons for paired-row breeding plots. However, it is sensitive to significant changes in dry matter content (%) among seasons, requiring seasonal algorithms for correction. | [115] |
Miscanthus giganteus | Measurement of crop height | The sensor assessed stem densities in static mode, yielding an average error of 5.08% (max 8%, min 1.8%). It also measured crop height in a 5 × 10 m field, showing a 4.2% error compared to manual measurements. The sensor traversed a field edge in dynamic mode, generating a three-dimensional crop structure. An ordinary least-squares surface-fitting algorithm produced top and ground surfaces, resulting in an average crop height. Dynamic measurements showed a 3.8% average error (max 6.5%, min 1.5%). | [116] |
LiDAR Types | LIDAR Sensor | Software System | Algorithm | Model | Crop | Feature | Ref. |
---|---|---|---|---|---|---|---|
TLS | RIEGL VZ-1000 | RiSCAN Pro 8.0 | ICPA IDW | MCSM, DEM | Paddy rice | Crop height | [213] |
TLS | ILRIS | TerraScan 16.0 Surfer 8 | ICP | QTM | Shrubs | Biomass detection | [214] |
TLS | RIEGL VZ-1000 | RiSCAN Pro 8.0 | ICP | CSM, DTM, CHM | Wheat | Canopy hight detection | [215] |
ALS | VUX-1UAV | TerraScan 16.0 R Studio | Random forest | CHM, DEM, DSM | Maize, Soybean | Crop heights estimation | [216] |
ALS | Leica ALS70 | TerraScan 16.0 ENVI 5.6 | TIN FLASH | DTM, BEM | Maize | Biomass estimation | [217] |
ALS | DJI Matrice M300 | CloudCompare 2.11.3 DJI Terra 3.0 Python 3.7 LiDAR 360 | TransUNet U-Net, FCN PCTA CXHA | CMM, DSM, DTM | Broccoli | Canopy and head estimation | [218] |
TLS | SICK LMS200 | CloudCompare 2.11.3 MATLAB R2013a R Studio 0.96 | - | DTM | Sugarcane | Monitoring production | [219] |
TLS | RigelScan Elite | PCL 1.9.1 | RANSAC PCPA CGA | - | Legume | Phenotyping | [220] |
ALS | RIEGL RiCOPTER | CloudCompare 2.10.2 RiPROCESS 4.0, RiPRECISION 4.0 | 3DPI | DTM, DSM, CHM | Potato, Sugar beet, Wheat | Crop height and biomass estimation | [221] |
TLS | LMS 511, SICK AG | LabVIEW 2.0 MATLAB 2016b | PCPA K-means clustering LOWESS | 3DLSM | Sorghum, Maize | Morphological traits detection | [222] |
TLS | FARO Focus S70 | SCENE 2022 PCL 1.12.0 | DBSCAN RNN KDTree | MSM MOSM | Maize | Segmentation and stratification | [223] |
TLS | LMS-Z420i | RiSCAN Pro | SDA | CSM, DTM, DSM, | Barley, Sugar beet | Crop heights estimation | [224] |
ALS | RIEGL VUX-1UAV | LiDAR360 | - | CHM, DEM, DTM, DSM | Maize | Lodged crop height estimation | [225] |
TLS | Kaarta Stencil 2 | CloudCompare 2.12.4 MATLAB 2022a | CXHA ASA | - | Olive | Tree volume estimation | [226] |
TLS | RIEGL LMS-Q680i | SideLook 1.1.01 R Studio 1.0 | Mean height | DTM, CMM | Corn | Hail defoliation assessment | [227] |
TLS | FX6 LIDAR | PCL 1.0 MATLAB R2011a | RANSAC | CRM | Maize | Plant detection and mapping | [228] |
TLS | LiDAR Lite V3 | R Studio PCL Arduino IDE 1.8.13 | - | IWM | Palm | Bunch ripeness prediction | [229] |
TLS | FARO Focus 3D X120 | FARO SCENE 5.4.4 LiDAR360 | - | DTM, | Maize | Monitoring phenotypes | [230] |
TLS | Velodyne VLP-16 | VeloView 3.5 MATLAB | RANSAC | LBM | - | Plowing furrow recognition | [231] |
TLS | LiDAR-Lite v3 LMS400 | CloudCompare Python 2.7 MATLAB 2017a | PCSA PCAA | 3D point cloud | Canola | Leaf morphology extraction | [232] |
TLS | TDS-130L 3D | Point Cloud MeshLab | RANSAC ICPA | - | Tomato | Leaf morphology analysis | [233] |
TLS ALS | FARO Focus 350 RIEGL VUX-1UAV | CloudCompare2.11.3 RiProcess 2.8 FARO SCENE POSPAC UAV 5.0 | - | LADE | Maize | Ear height estimation | [234] |
ALS | Leica Aibot X6V2 | CloudCompare 2.9.1 MATLAB R2019a | ASA PCAA | DTM, DEM | Apple | Crown Parameters detection | [235] |
ALS | Leica ALS50-II | FUSION/LDV 3.42 | Tree detection algorithm | DTM, CHM | Olive | Pruning biomass estimation | [236] |
ALS | LM7800 | CloudCompare, MATLAB, FUSION/LDV | ASA, k-means algorithms | DTM, DSM | Walnut | Crown parameters detection | [237] |
MLS | LMS511 pro | CloudCompare 2.10.2 MATLAB R2021a | ICPA CHA | - | Strawberry | Plant vegetative growth | [238] |
TLS | 3D Velodyne, 2D Sick LMS | PCL 1.11.1 CloudCompare 2.10.2 Pix4D 4.6.0 | ICPA RANSAC | KM, DEM | Citrus | Canopy height | [239] |
TLS | LMS-511 | CloudCompare 2.11.0 MATLAB R2022a | ICPA | 3D Point Cloud LRM, KNN | Apple | Tree canopy and fruit quality | [240] |
TLS | SICK LMS111 | LabVIEW 2020 MATLAB R2020a | ICPA | CSM, CHM, DSM | Apple | Canopy leaf area | [241] |
TLS | Sick LMS-111 | MATLAB R2022a | ASA | - | Peach | Tree canopy estimation | [242] |
TLS | Sick LMS200 | PCL 1.8.1 MATLAB R2018a | LRM | - | Apple, Pear, Vineyard | Canopy leaf area | [243] |
TLS | OMD10M-R2000-B23-V1V1D | Python 3.7 LabVIEW 2019 | SORA GNBA | URL, PLSRM | Banana | Fruit chlorophyll estimation | [244] |
ALS | Velodyne Puck LITE | PCL CloudCompare 2.11.3 MATLAB R2021a | ICPA SLAM | CHM, LAI | Peach | Plant parameter estimation | [245] |
TLS | Sick LMS 200 | CloudCompare 2.8.1 R Studio 1.1.x | ASA CXHA | - | Orange | Canopy volume estimation | [246] |
TLS | Sick AG LMS111 | SOPAS ET MATLAB R2016a | PCA | - | Plants | Plant monitoring | [247] |
TLS | Optech ILRIS 36D | Polyworks v10 | - | LLSVM | Grapevine | Biomass estimation | [248] |
MLS, TLS | Backpack DG50 FARO Focuss 350 | CloudCompare 2.6.1 MATLAB 2016a LiDAR 360 v4.1 | TreeQSM | QSM | Apple | Tree branch information | [249] |
MLS | VLP-16 LiDAR | CloudCompare 2.9.1 MATLAB 2016a | CXHA, CSF ASA VBS ASBS | DTM | Grapefruit | Canopy parameter estimation | [250] |
TLS | FARO Focus 3D | CloudCompare MATLAB TreeQSM FARO SCENE v2022.1.0 | - | QSM | Peach | 3D crown architecture estimation | [251] |
MLS | Velodyne VLP-16 | VeloView MATLAB 2020a | RANSAC ASA | LRM CFPD | Apple | Canopy density estimation | [252] |
MLS | Stereo vision | ROS | Skeletonization, CNN | TM | Cherry | Pruning | [253] |
TLS | FARO Focus S70 | PCL 1.11.1 Visual Studio 2022 FV-2200 | DTA LA-DT | LRM LAEM | Rapeseed | Leaf estimation | [254] |
Aspect | Airborne LiDAR System (ALS) | Terrestrial LiDAR System (TLS) | Mobile LiDAR System (MLS) |
---|---|---|---|
Platform | Aircraft (e.g., UAVs, planes, helicopters) | Stationary tripods or ground vehicles | Moving vehicles (e.g., tractors, ATVs) |
Coverage Area | Large-scale, extensive coverage | Small to medium-scale, site-specific | Medium-to-large-scale, linear coverage (e.g., along field rows) |
Data Collection | Rapid, wide-area scanning | High-detail, stationary, or limited-area scanning | Continuous, high-density scanning along paths of movement |
Altitude/Range | High altitude, large range | Ground level, limited range | Variable altitude, depending on vehicle height and field conditions |
Resolution | Moderate to high, depending on altitude and sensor | Very high due to proximity to crops | High, suitable for detailed mapping of crop rows and field features |
Applications | Large field surveys, topographic mapping, biomass estimation | Detailed plant structure analysis, canopy height measurement | Field condition monitoring, crop health assessment, yield estimation |
Advantages | Extensive coverage, rapid data collection over large areas | High precision and detail, minimal atmospheric interference | Mobility, ability to cover large areas quickly and repeatedly |
Limitations | Weather dependent, regulatory restrictions, cost | Limited coverage area, requires multiple setups for large fields | Limited by vehicle access, potential for obstructions in the field |
Crop management practices | Field topography, crop biomass, and health mapping | Detailed crop structure analysis, disease detection | Monitoring crop growth, assessing field variability, yield prediction |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Farhan, S.M.; Yin, J.; Chen, Z.; Memon, M.S. A Comprehensive Review of LiDAR Applications in Crop Management for Precision Agriculture. Sensors 2024, 24, 5409. https://doi.org/10.3390/s24165409
Farhan SM, Yin J, Chen Z, Memon MS. A Comprehensive Review of LiDAR Applications in Crop Management for Precision Agriculture. Sensors. 2024; 24(16):5409. https://doi.org/10.3390/s24165409
Chicago/Turabian StyleFarhan, Sheikh Muhammad, Jianjun Yin, Zhijian Chen, and Muhammad Sohail Memon. 2024. "A Comprehensive Review of LiDAR Applications in Crop Management for Precision Agriculture" Sensors 24, no. 16: 5409. https://doi.org/10.3390/s24165409