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Feature Papers for Drones in Agriculture and Forestry Section: 2nd...
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Feature Papers for Drones in Agriculture and Forestry Section: 2nd Edition

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drones in Agriculture and Forestry".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 4647

Special Issue Editors

Prof. Dr. Yangquan Chen

grade E-Mail Website
Guest Editor
Department of Mechanical Engineering (ME), University of California, Merced, CA 95343, USA
Interests: mechatronics for sustainability; cognitive process control; small multi-UAV-based cooperative multi-spectral “personal remote sensing”; applied fractional calculus in controls, modeling, and complex signal processing; distributed measurements; control of distributed parameter systems with mobile actuators and sensor networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Fei Liu

E-Mail Website
Guest Editor
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Interests: smart agriculture; UAS; remote sensing; plant phenotype and disease-pest monitoring; crop yield prediction; variable spraying system; deep learning; imaging processing technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As the Section Editor-in-Chief, I am pleased to announce a Special Issue entitled “Feature Papers for Section Drones in Agriculture and Forestry 2nd Edition”. This Special Issue welcomes high-quality papers from the latest research and application results related to the use of drones in agriculture and forestry. Manuscripts can be theoretical, applied, or review articles. Interdisciplinary manuscripts are particularly welcome. For more scope information, you may check https://www.mdpi.com/journal/drones/sections/drones_in_agriculture_forestry.

Prof. Dr. Yangquan Chen
Prof. Dr. Fei Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • drones for agriculture:
  • personal remote sensing (PRS)
  • smart farming (crops, dairy, grazing, etc.)
  • site-specific management (SSM) of water, pesticide, fertilizer, etc.
  • integrated pest management (IPM)
  • phenotyping
  • yield prediction
  • environmental emissions (GHG, dust, etc.)
  • water-saving agriculture and irrigation management
  • applications (crop dusting, seeding, etc.)
  • soil moisture, soil health, soil variability, and carbon-negative practices
  • smart sensing and smart big data analytics
  • drones for forestry:
  • forest fire sensing and management
  • precision forestry and forestry management
  • forest mapping and biodiversity
  • mapping of canopy distribution and gaps
  • capturing forestry informatics (biomass, stockpile, fuel load, etc.)
  • three-dimensional mapping for carbon storage
  • tree planting and reforesting with drones
  • applications (surface fertilizer, growth stimulant, pesticides, etc.)
  • monitoring illegal logging and quarrying (forest security)
  • combating deforestation and desert forestation dynamics
  • smart sensing and smart big data analytics

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue polices can be found here.

Related Special Issue

Published Papers (5 papers)

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Research

23 pages, 6609 KiB  
Article
Improved Estimation of Aboveground Biomass in Rubber Plantations Using Deep Learning on UAV Multispectral Imagery
by Hongjian Tan, Weili Kou, Weiheng Xu, Leiguang Wang, Huan Wang and Ning Lu
Drones 2025, 9(1), 32; https://doi.org/10.3390/drones9010032 - 6 Jan 2025
Viewed by 399
Abstract
The accurate estimation of aboveground biomass (AGB) in rubber plantations is essential for predicting rubber production and assessing carbon storage. Multispectral sensors mounted on unmanned aerial vehicles (UAVs) can obtain high spatiotemporal resolution imagery of rubber plantations, offering significant advantages in capturing fine [...] Read more.
The accurate estimation of aboveground biomass (AGB) in rubber plantations is essential for predicting rubber production and assessing carbon storage. Multispectral sensors mounted on unmanned aerial vehicles (UAVs) can obtain high spatiotemporal resolution imagery of rubber plantations, offering significant advantages in capturing fine structural details and heterogeneity. However, most previous studies primarily focused on developing biomass estimation models for rubber using machine learning (ML) algorithms in conjunction with feature selection methods based on UAV-acquired multispectral imagery. The reliance on feature selection methods limits the model’s generalizability, robustness, and predictive accuracy. In contrast, deep learning (DL) exhibits considerable promise in extracting features from high-resolution UAV-based multispectral imagery without the need for manual selection. Nonetheless, it remains unclear whether DL can surpass traditional ML methods in improving the AGB estimation accuracy in rubber plantations. To address this, our study evaluated the performance of three ML algorithms (random forest regression, RFR; XGBoost regression, XGBR; categorical boosting regression, CatBoost) combined with feature selection techniques and a deep convolutional neural network (DCNN) using multispectral imagery obtained from UAV for the AGB estimation of rubber plantations. The results indicate that the RFR combined with a principal component analysis (PCA) for feature selection yielded the best performance (R2 = 0.81, RMSE = 11.63 t/ha, MAE = 9.27 t/ha) between the three ML algorithms. Meanwhile, the DCNN model derived from the G, R, and NIR spectral bands achieved the highest estimation accuracy (R2 = 0.89, RMSE = 6.44 t/ha, MAE = 5.72 t/ha), where it outperformed the other ML methods. Our study highlights the great potential of combining UAV-based multispectral imagery with DL techniques to improve AGB estimation in rubber plantations, offering a new perspective for estimating the physiological and biochemical growth parameters of forests. Full article
(This article belongs to the Special Issue Feature Papers for Drones in Agriculture and Forestry Section: 2nd Edition)
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18 pages, 3110 KiB  
Article
Accurate Prediction of 327 Rice Variety Growth Period Based on Unmanned Aerial Vehicle Multispectral Remote Sensing
by Zixuan Qiu, Hao Liu, Lu Wang, Shuaibo Shao, Can Chen, Zijia Liu, Song Liang, Cai Wang and Bing Cao
Drones 2024, 8(11), 665; https://doi.org/10.3390/drones8110665 - 10 Nov 2024
Viewed by 934
Abstract
Most rice growth stage predictions are currently based on a few rice varieties for prediction method studies, primarily using linear regression, machine learning, and other methods to build growth stage prediction models that tend to have poor generalization ability, low accuracy, and face [...] Read more.
Most rice growth stage predictions are currently based on a few rice varieties for prediction method studies, primarily using linear regression, machine learning, and other methods to build growth stage prediction models that tend to have poor generalization ability, low accuracy, and face various challenges. In this study, multispectral images of rice at various growth stages were captured using an unmanned aerial vehicle, and single-plant rice silhouettes were identified for 327 rice varieties by establishing a deep-learning algorithm. A growth stage prediction method was established for the 327 rice varieties based on the normalized vegetation index combined with cubic polynomial regression equations to simulate their growth changes, and it was first proposed that the growth stages of different rice varieties were inferred by analyzing the normalized difference vegetation index growth rate. Overall, the single-plant rice contour recognition model showed good contour recognition ability for different rice varieties, with most of the prediction accuracies in the range of 0.75–0.93. The accuracy of the rice growth stage prediction model in recognizing different rice varieties also showed some variation, with the root mean square error between 0.506 and 3.373 days, the relative root mean square error between 2.555% and 14.660%, the Bias between1.126 and 2.358 days, and the relative Bias between 0.787% and 9.397%; therefore, the growth stage prediction model of rice varieties can be used to effectively improve the prediction accuracy of the growth stage periods of rice. Full article
(This article belongs to the Special Issue Feature Papers for Drones in Agriculture and Forestry Section: 2nd Edition)
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11 pages, 5384 KiB  
Article
Visualization of Aerial Droplet Distribution for Unmanned Aerial Spray Systems Based on Laser Imaging
by Zhichong Wang, Peng Qi, Yangfan Li and Xiongkui He
Drones 2024, 8(11), 613; https://doi.org/10.3390/drones8110613 - 26 Oct 2024
Cited by 1 | Viewed by 682
Abstract
Unmanned aerial spray systems (UASSs) are a commonly used spraying method for plant protection operations. However, their spraying parameters have complex effects on droplet distribution. The large-scale 3D droplet density distribution measurement method is insufficient, especially since the downwash wind is easily affected [...] Read more.
Unmanned aerial spray systems (UASSs) are a commonly used spraying method for plant protection operations. However, their spraying parameters have complex effects on droplet distribution. The large-scale 3D droplet density distribution measurement method is insufficient, especially since the downwash wind is easily affected by the environment. Therefore, there is a need to develop a technique that can quickly visualize 3D droplet distribution. In this study, a laser imaging method was proposed to quickly scan moving droplets in the air, and a test method that can visualize 3D droplet distribution was constructed by using the traveling mode of the machine perpendicular to the scanning plane. The 3D droplet distribution of targeted and conventional UAVs was tested, and the methods for signal processing, noise reduction, and point cloud rebuilding for laser imaging were developed. Compared with the simulation results, laser imaging showed the pattern of droplet distribution from the two UAV structures well. The results showed that the laser imaging based method for detecting 3D droplet distribution is feasible, fast, and environmentally friendly. Full article
(This article belongs to the Special Issue Feature Papers for Drones in Agriculture and Forestry Section: 2nd Edition)
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11 pages, 2851 KiB  
Article
Granular Bait Applications for Management of Rangeland Grasshoppers Using a Remotely Piloted Aerial Application System
by Roberto Rodriguez, Derek A. Woller, Daniel E. Martin, K. Chris Reuter, Lonnie R. Black, Mohamed A. Latheef, Kiara M. López Colón and Mason Taylor
Drones 2024, 8(10), 535; https://doi.org/10.3390/drones8100535 - 30 Sep 2024
Viewed by 778
Abstract
Rangeland grasshoppers are an endemic species that play an essential role in the rangeland ecosystem but can cause severe economic damage when populations reach outbreak levels. Remotely piloted aerial application systems (RPAASs) offer an alternative method to carry out aerial insecticide applications in [...] Read more.
Rangeland grasshoppers are an endemic species that play an essential role in the rangeland ecosystem but can cause severe economic damage when populations reach outbreak levels. Remotely piloted aerial application systems (RPAASs) offer an alternative method to carry out aerial insecticide applications in relatively small areas. The objective of this study was to investigate the efficacy of a granular bait, 2% Sevin (with the active ingredient carbaryl), applied by an RPAAS. The bait was applied on four replicated 4.05-hectare (10-acre) plots at a rate of 2.27 kg/ha (5 lbs/acre) with an RPAAS on a private ranch in New Mexico. Applications resulted in a normalized population reduction of 70.32% ± 16.54% standard error of the mean (SEM) of bait-susceptible species. Although some of the observed reduction in population may be attributed to aging, the net effect was most likely due to the ingestion of bait based on field observations of rapid mortality after ingestion and other factors, like past experience with carbaryl bait treatments on grasshoppers. Plots required at least two flights due to the Federal Aviation Administration’s (FAA) maximum takeoff weight requirement for small RPAASs. Combined, these results indicate that RPAASs can provide treatment capabilities in relatively small areas, i.e., population hotspots, preferably before outbreak levels are reached. Full article
(This article belongs to the Special Issue Feature Papers for Drones in Agriculture and Forestry Section: 2nd Edition)
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23 pages, 11793 KiB  
Article
Detecting Canopy Gaps in Uneven-Aged Mixed Forests through the Combined Use of Unmanned Aerial Vehicle Imagery and Deep Learning
by Nyo Me Htun, Toshiaki Owari, Satoshi Tsuyuki and Takuya Hiroshima
Drones 2024, 8(9), 484; https://doi.org/10.3390/drones8090484 - 13 Sep 2024
Viewed by 1191
Abstract
Canopy gaps and their associated processes play an important role in shaping forest structure and dynamics. Understanding the information about canopy gaps allows forest managers to assess the potential for regeneration and plan interventions to enhance regeneration success. Traditional field surveys for canopy [...] Read more.
Canopy gaps and their associated processes play an important role in shaping forest structure and dynamics. Understanding the information about canopy gaps allows forest managers to assess the potential for regeneration and plan interventions to enhance regeneration success. Traditional field surveys for canopy gaps are time consuming and often inaccurate. In this study, canopy gaps were detected using unmanned aerial vehicle (UAV) imagery of two sub-compartments of an uneven-aged mixed forest in northern Japan. We compared the performance of U-Net and ResU-Net (U-Net combined with ResNet101) deep learning models using RGB, canopy height model (CHM), and fused RGB-CHM data from UAV imagery. Our results showed that the ResU-Net model, particularly when pre-trained on ImageNet (ResU-Net_2), achieved the highest F1-scores—0.77 in Sub-compartment 42B and 0.79 in Sub-compartment 16AB—outperforming the U-Net model (0.52 and 0.63) and the non-pre-trained ResU-Net model (ResU-Net_1) (0.70 and 0.72). ResU-Net_2 also achieved superior overall accuracy values of 0.96 and 0.97, outperforming previous methods that used UAV datasets with varying methodologies for canopy gap detection. These findings underscore the effectiveness of the ResU-Net_2 model in detecting canopy gaps in uneven-aged mixed forests. Furthermore, when these trained models were applied as transfer models to detect gaps specifically caused by selection harvesting using pre- and post-UAV imagery, they showed considerable potential, achieving moderate F1-scores of 0.54 and 0.56, even with a limited training dataset. Overall, our study demonstrates that combining UAV imagery with deep learning techniques, particularly pre-trained models, significantly improves canopy gap detection accuracy and provides valuable insights for forest management and future research. Full article
(This article belongs to the Special Issue Feature Papers for Drones in Agriculture and Forestry Section: 2nd Edition)
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