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Agriculture
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Recent Applications of Remote Sensing and Machine Learning in Smart...
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Recent Applications of Remote Sensing and Machine Learning in Smart Agriculture

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: 20 November 2024 | Viewed by 3728

Special Issue Editors

Dr. Zhenwang Li

E-Mail Website
Guest Editor
Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College, Yangzhou University, Yangzhou 225009, China
Interests: agriculture remote sensing; smart agriculture; big data; crop nutrition diagnosis; machine learning; spatial-temporal analysis; crop monitoring
Dr. Liujun Xiao

E-Mail Website
Guest Editor
National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, 1 Weigang Road, Nanjing 210095, China
Interests: crop system modelling; SOC; GHG emissions; crop and soil digital mapping; management practise optimization; climate change
Special Issues, Collections and Topics in MDPI journals
Dr. Yahui Guo

E-Mail Website
Guest Editor
College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
Interests: UAV; agriculture remote sensing; climate change; crop models; phenological extraction; machine learning and deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart Agriculture upgrades conventional farming methods and world agriculture strategies to an optimized value chain by integrating innovative information and communication technologies, such as remote sensing, machine learning, big data analysis, and the Internet of Things. This combination leads to improved yield prediction and water management, resulting in increased efficiency, enhanced yields, and more sustainable agricultural practices. Recent advances in remote sensing technology (platforms, sensors, algorithms) enable the low-cost, high-resolution, and flexible observation of crops and soils, and the obtainment of diagnostic information on crop growth, water stress, soil fertility, weed, disease, lodging, and 3D topography, greatly enhancing the efficiency of labor and material applications and profitability. Machine learning technologies exhibit a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems in order to create value from the ever-increasing volume of data originating from agricultural fields. In this context, the aim of this Special Issue is to seek high-quality papers related to recent progress in remote sensing (ground-based, drone-based, and satellite-based), artificial intelligence (deep learning and machine learning), and big data analysis for the application of smart agriculture, especially UAV-based high-throughput phenotyping, crop growth status and nutrition diagnosis, and yield estimation based on multisource remote sensing data and machine learning. This Special Issue welcomes regular research and review papers addressing various aspects of novel methods, approaches, or algorithms including, but not limited to, the above topics.

Dr. Zhenwang Li
Dr. Liujun Xiao
Dr. Yahui Guo
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. Agriculture 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

  • remote sensing
  • UAV
  • high-throughput phenotyping
  • artificial intelligence and machine learning
  • big data analysis
  • crop nutrition diagnosis
  • crop monitoring
  • spatial–temporal analysis
  • smart agriculture

Published Papers (4 papers)

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Research

22 pages, 9320 KiB  
Article
Monitoring Maize Canopy Chlorophyll Content throughout the Growth Stages Based on UAV MS and RGB Feature Fusion
by Wenfeng Li, Kun Pan, Wenrong Liu, Weihua Xiao, Shijian Ni, Peng Shi, Xiuyue Chen and Tong Li
Agriculture 2024, 14(8), 1265; https://doi.org/10.3390/agriculture14081265 - 1 Aug 2024
Abstract
Chlorophyll content is an important physiological indicator reflecting the growth status of crops. Traditional methods for obtaining crop chlorophyll content are time-consuming and labor-intensive. The rapid development of UAV remote sensing platforms offers new possibilities for monitoring chlorophyll content in field crops. To [...] Read more.
Chlorophyll content is an important physiological indicator reflecting the growth status of crops. Traditional methods for obtaining crop chlorophyll content are time-consuming and labor-intensive. The rapid development of UAV remote sensing platforms offers new possibilities for monitoring chlorophyll content in field crops. To improve the efficiency and accuracy of monitoring chlorophyll content in maize canopies, this study collected RGB, multispectral (MS), and SPAD data from maize canopies at the jointing, tasseling, and grouting stages, constructing a dataset with fused features. We developed maize canopy chlorophyll content monitoring models based on four machine learning algorithms: BP neural network (BP), multilayer perceptron (MLP), support vector regression (SVR), and gradient boosting decision tree (GBDT). The results showed that, compared to single-feature methods, the MS and RGB fused feature method achieved higher monitoring accuracy, with R² values ranging from 0.808 to 0.896, RMSE values between 2.699 and 3.092, and NRMSE values between 10.36% and 12.26%. The SVR model combined with MS–RGB fused feature data outperformed the BP, MLP, and GBDT models in monitoring maize canopy chlorophyll content, achieving an R² of 0.896, an RMSE of 2.746, and an NRMSE of 10.36%. In summary, this study demonstrates that by using the MS–RGB fused feature method and the SVR model, the accuracy of chlorophyll content monitoring can be effectively improved. This approach reduces the need for traditional methods of measuring chlorophyll content in maize canopies and facilitates real-time management of maize crop nutrition. Full article
(This article belongs to the Special Issue Recent Applications of Remote Sensing and Machine Learning in Smart Agriculture)
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15 pages, 6834 KiB  
Article
YOLOv8MS: Algorithm for Solving Difficulties in Multiple Object Tracking of Simulated Corn Combining Feature Fusion Network and Attention Mechanism
by Yuliang Gao, Zhen Li, Bin Li and Lifeng Zhang
Agriculture 2024, 14(6), 907; https://doi.org/10.3390/agriculture14060907 - 8 Jun 2024
Viewed by 473
Abstract
The automatic cultivation of corn has become a significant research focus, with precision equipment operation being a key aspect of smart agriculture’s advancement. This work explores the tracking process of corn, simulating the detection and approach phases while addressing three major challenges in [...] Read more.
The automatic cultivation of corn has become a significant research focus, with precision equipment operation being a key aspect of smart agriculture’s advancement. This work explores the tracking process of corn, simulating the detection and approach phases while addressing three major challenges in multiple object tracking: severe occlusion, dense object presence, and varying viewing angles. To effectively simulate these challenging conditions, a multiple object tracking dataset using simulated corn was created. To enhance accuracy and stability in corn tracking, an optimization algorithm, YOLOv8MS, is proposed based on YOLOv8. Multi-layer Fusion Diffusion Network (MFDN) is proposed for improved detection of objects of varying sizes, and the Separated and Enhancement Attention Module (SEAM) is introduced to tackle occlusion issues. Experimental results show that YOLOv8MS significantly enhances the detection accuracy, tracking accuracy and tracking stability, achieving a mean average precision (mAP) of 89.6% and a multiple object tracking accuracy (MOTA) of 92.5%, which are 1% and 6.1% improvements over the original YOLOv8, respectively. Furthermore, there was an average improvement of 4% in the identity stability indicator of tracking. This work provides essential technical support for precision agriculture in detecting and tracking corn. Full article
(This article belongs to the Special Issue Recent Applications of Remote Sensing and Machine Learning in Smart Agriculture)
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18 pages, 5052 KiB  
Article
Comparative Analysis of Feature Importance Algorithms for Grassland Aboveground Biomass and Nutrient Prediction Using Hyperspectral Data
by Yue Zhao, Dawei Xu, Shuzhen Li, Kai Tang, Hongliang Yu, Ruirui Yan, Zhenwang Li, Xu Wang and Xiaoping Xin
Agriculture 2024, 14(3), 389; https://doi.org/10.3390/agriculture14030389 - 28 Feb 2024
Cited by 2 | Viewed by 1098
Abstract
Estimating forage yield and nutrient composition using hyperspectral remote sensing is a major challenge. However, there is still a lack of comprehensive research on the optimal wavelength for the analysis of various nutrients in pasture. In this research, conducted in Hailar District, Hulunber [...] Read more.
Estimating forage yield and nutrient composition using hyperspectral remote sensing is a major challenge. However, there is still a lack of comprehensive research on the optimal wavelength for the analysis of various nutrients in pasture. In this research, conducted in Hailar District, Hulunber City, Inner Mongolia Autonomous Region, China, 126 sets of hyperspectral data were collected, covering a spectral range of 350 to 1800 nanometers. The primary objective was to identify key spectral bands for estimating forage dry matter yield (DMY), nitrogen content (NC), neutral detergent fiber (NDF), and acid detergent fiber (ADF) using principal component analysis (PCA), random forests (RF), and SHapley Additive exPlanations (SHAP) analysis methods, and then the RF and Extra-Trees algorithm (ERT) model was used to predict aboveground biomass (AGB) and nutrient parameters using the optimized spectral bands and vegetation indices. Our approach effectively minimizes redundancy in hyperspectral data by selectively employing crucial spectral bands, thus improving the accuracy of forage nutrient estimation. PCA identified the most variable bands at 400 nm, 520–550 nm, 670–720 nm, and 930–950 nm, reflecting their general spectral significance rather than a link to specific forage nutrients. Further analysis using RF feature importance pinpointed influential bands, predominantly within 930–940 nm and 700–730 nm. SHAP analysis confirmed critical bands for DMY (965 nm, 712 nm, and 1652 nm), NC (1390 nm and 713 nm), ADF (1390 nm and 715–725 nm), and NDF (400 nm, 983 nm, 1350 nm, and 1800 nm). The fitting accuracy for ADF estimated using RF was lower (R2 = 0.58), while the fitting accuracy for other indicators was higher (R2 ≥ 0.59). The performance and prediction accuracy of ERT (R2 = 0.63) were noticeably superior to those of RF. In conclusion, our method effectively identifies influential bands, optimizing forage yield and quality estimation. Full article
(This article belongs to the Special Issue Recent Applications of Remote Sensing and Machine Learning in Smart Agriculture)
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22 pages, 11664 KiB  
Article
Unmanned Aerial Vehicle-Scale Weed Segmentation Method Based on Image Analysis Technology for Enhanced Accuracy of Maize Seedling Counting
by Tianle Yang, Shaolong Zhu, Weijun Zhang, Yuanyuan Zhao, Xiaoxin Song, Guanshuo Yang, Zhaosheng Yao, Wei Wu, Tao Liu, Chengming Sun and Zujian Zhang
Agriculture 2024, 14(2), 175; https://doi.org/10.3390/agriculture14020175 - 24 Jan 2024
Cited by 1 | Viewed by 1106
Abstract
The number of maize seedlings is a key determinant of maize yield. Thus, timely, accurate estimation of seedlings helps optimize and adjust field management measures. Differentiating “multiple seedlings in a single hole” of maize accurately using deep learning and object detection methods presents [...] Read more.
The number of maize seedlings is a key determinant of maize yield. Thus, timely, accurate estimation of seedlings helps optimize and adjust field management measures. Differentiating “multiple seedlings in a single hole” of maize accurately using deep learning and object detection methods presents challenges that hinder effectiveness. Multivariate regression techniques prove more suitable in such cases, yet the presence of weeds considerably affects regression estimation accuracy. Therefore, this paper proposes a maize and weed identification method that combines shape features with threshold skeleton clustering to mitigate the impact of weeds on maize counting. The threshold skeleton method (TS) ensured that the accuracy and precision values of eliminating weeds exceeded 97% and that the missed inspection rate and misunderstanding rate did not exceed 6%, which is a significant improvement compared with traditional methods. Multi-image characteristics of the maize coverage, maize seedling edge pixel percentage, maize skeleton characteristic pixel percentage, and connecting domain features gradually returned to maize seedlings. After applying the TS method to remove weeds, the estimated R2 is 0.83, RMSE is 1.43, MAE is 1.05, and the overall counting accuracy is 99.2%. The weed segmentation method proposed in this paper can adapt to various seedling conditions. Under different emergence conditions, the estimated R2 of seedling count reaches a maximum of 0.88, with an RMSE below 1.29. The proposed approach in this study shows improved weed recognition accuracy on drone images compared to conventional image processing methods. It exhibits strong adaptability and stability, enhancing maize counting accuracy even in the presence of weeds. Full article
(This article belongs to the Special Issue Recent Applications of Remote Sensing and Machine Learning in Smart Agriculture)
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