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Agronomy
Special Issues
New Trends in Agricultural UAV Application
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New Trends in Agricultural UAV Application

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 10567

Special Issue Editors

Dr. Yali Zhang

E-Mail Website
Guest Editor
1. College of Engineering, South China Agricultural University, Wushan Road, Guangzhou 510642, China
2. National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China
Interests: sensing technologies; agricultural UAV application
Special Issues, Collections and Topics in MDPI journals
Dr. Ganesh Bora

E-Mail Website
Guest Editor
Office of Research and Technology Innovation, Fayetteville State University, Fayetteville, NC 28301, USA
Interests: precision agriculture; remote sensing and UAS; data science; AI and machine learning; robotics

Special Issue Information

Dear Colleagues,

UAVs have demonstrated significant advantages in agricultural scenarios such as crop information monitoring, disease and pest detection, and aerial application for pesticide, fertilizer, seed, etc. For example, UAV sprayers have proven ability in spraying and work efficiency in field crops such as rice, wheat, and corn. Still, their application in scenes such as steep mountain slopes and densely planted orchards needs further exploration and improvement. At the same time, the environmental drift caused by spraying also deserves our attention. When considering precision spraying strategies, we must include crop canopy characteristics and disease levels based on remote sensing into the scope of variable spraying decisions. When examining different agricultural production problems, we will use the UAV platform to generate new solutions, model methods, and control strategies, which are the focus of this Issue.

The theme of this Special Issue is New Trends in Agricultural UAV Applications. We encourage exploration and application research on UAVs in various fields of agriculture in different areas, including but not limited to agricultural remote sensing, pesticide spraying, and mechanical system structure innovation, covering remote sensing, plant science, agronomy, and engineering technology.

All manuscript types, such as original research papers and reviews, are welcome.

Dr. Yali Zhang
Dr. Ganesh Bora
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. Agronomy 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

  • unmanned aerial vehicles (UAVs)
  • remote sensing
  • sustainable agriculture
  • variable-rate application technology
  • artificial intelligence (AI)
  • deep learning (DL)
  • agricultural information acquisition
  • 3D reconstruction
  • plant phenotyping
  • Internet of Things (IoT)

Published Papers (6 papers)

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Research

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22 pages, 15144 KiB  
Article
Multiscale Inversion of Leaf Area Index in Citrus Tree by Merging UAV LiDAR with Multispectral Remote Sensing Data
by Weicheng Xu, Feifan Yang, Guangchao Ma, Jinhao Wu, Jiapei Wu and Yubin Lan
Agronomy 2023, 13(11), 2747; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy13112747 - 31 Oct 2023
Cited by 2 | Viewed by 1556
Abstract
The LAI (leaf area index) is an important parameter describing the canopy structure of citrus trees and characterizing plant photosynthesis, as well as providing an important basis for selecting parameters for orchard plant protection operations. By fusing LiDAR data with multispectral data, it [...] Read more.
The LAI (leaf area index) is an important parameter describing the canopy structure of citrus trees and characterizing plant photosynthesis, as well as providing an important basis for selecting parameters for orchard plant protection operations. By fusing LiDAR data with multispectral data, it can make up for the lack of rich spatial features of multispectral data, thus obtaining higher LAI inversion accuracy. This study proposed a multiscale LAI inversion method for citrus orchard based on the fusion of point cloud data and multispectral data. By comparing various machine learning algorithms, the mapping relationship between the characteristic parameters in multispectral data and point cloud data and citrus LAI was established, and we established the inversion model based on this, by removing redundant features through redundancy analysis. The experiment results showed that the BP neural network performs the best at both the community scale and the individual scale. After removing redundant features, the R2, RMSE, and MAE of the BP neural network at the community scale and individual scale were 0.896, 0.112, 0.086, and 0.794, 0.408, 0.328, respectively. By adding the three-dimensional gap fraction feature to the two-dimensional vegetation index features, the R2 at community scale and individual scale increased by 4.43% and 7.29%, respectively. The conclusion of this study suggests that the fusion of point cloud and multispectral data exhibits superior accuracy in multiscale citrus LAI inversion compared to relying solely on a single data source. This study proposes a fast and efficient multiscale LAI inversion method for citrus, which provides a new idea for the orchard precise management and the precision of plant protection operation. Full article
(This article belongs to the Special Issue New Trends in Agricultural UAV Application)
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17 pages, 5615 KiB  
Article
Evaluation of Coffee Plants Transplanted to an Area with Surface and Deep Liming Based on Multispectral Indices Acquired Using Unmanned Aerial Vehicles
by Rafael Alexandre Pena Barata, Gabriel Araújo e Silva Ferraz, Nicole Lopes Bento, Daniel Veiga Soares, Lucas Santos Santana, Diego Bedin Marin, Drucylla Guerra Mattos, Felipe Schwerz, Giuseppe Rossi, Leonardo Conti and Gianluca Bambi
Agronomy 2023, 13(10), 2623; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy13102623 - 17 Oct 2023
Cited by 1 | Viewed by 1103
Abstract
The use of new technologies to monitor and evaluate the management of coffee crops allowed for a significant increase in productivity. Precision coffee farming has leveraged the development of this commodity by using remote sensing and Unmanned Aerial Vehicles (UAVs). However, the success [...] Read more.
The use of new technologies to monitor and evaluate the management of coffee crops allowed for a significant increase in productivity. Precision coffee farming has leveraged the development of this commodity by using remote sensing and Unmanned Aerial Vehicles (UAVs). However, the success of coffee farming in the country also resulted from management practices, including liming management in the soils. This study aimed to evaluate the response of coffee seedlings transplanted to areas subjected to deep liming in comparison to conventional (surface) liming, using vegetation indices (VIs) generated by multispectral images acquired using UAVs. The study area was overflown bimonthly by UAVs to measure the plant height, crown diameter, and chlorophyll content in the field. The VIs were generated and compared with the data measured in the field using linear time graphs and a correlation analysis. Linear regression was performed to predict the biophysical parameters as a function of the VIs. A significant difference was found only in the chlorophyll content. Most indices were correlated with the biophysical parameters, particularly the green chlorophyll index (GCI) and the canopy area calculated via vectorization. Therefore, UAVs proved to be effective coffee monitoring tools and can be recommended for coffee producers. Full article
(This article belongs to the Special Issue New Trends in Agricultural UAV Application)
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18 pages, 11646 KiB  
Article
Canopy Laser Interception Compensation Mechanism—UAV LiDAR Precise Monitoring Method for Cotton Height
by Weicheng Xu, Weiguang Yang, Jinhao Wu, Pengchao Chen, Yubin Lan and Lei Zhang
Agronomy 2023, 13(10), 2584; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy13102584 - 09 Oct 2023
Cited by 2 | Viewed by 1238
Abstract
Plant height is a crucial phenotypic trait that plays a vital role in predicting cotton growth and yield, as well as in estimating biomass in cotton plants. The accurate estimation of canopy height from single-flight LiDAR data remains a formidable challenge in current [...] Read more.
Plant height is a crucial phenotypic trait that plays a vital role in predicting cotton growth and yield, as well as in estimating biomass in cotton plants. The accurate estimation of canopy height from single-flight LiDAR data remains a formidable challenge in current high-density cotton cultivation patterns, where dense foliage obstructs the collection of bare soil terrain, particularly after flowering. The existing LiDAR-based methods for cotton height estimation suffer from significant errors. In this study, a new method is proposed to compensate for the canopy height estimation by using the canopy laser interception rate. The ground points are extracted by the ground filtering algorithm, and the interception rate of the laser per unit volume of the canopy is calculated to represent the canopy density and compensate for the cotton height estimation. The appropriate segmented height compensation function is determined by grouping and step-by-step analysis of the canopy laser interception rate. Verified by 440 groups of height data measured manually in the field, the results show that the canopy laser interception compensation mechanism is of great help in improving the estimation accuracy of LiDAR. R2 and RMSE reach 0.90 and 6.18 cm, respectively. Compared with the estimation method before compensation, R2 is increased by 13.92%, and RMSE is reduced by 49.31%. And when the canopy interception rate is greater than 99%, the compensation effect is more obvious, and the RMSE is reduced by 62.49%. This research result can significantly improve the height estimation accuracy of UAV-borne for high planting density cotton areas, which is helpful to improve the efficiency of cotton quality breeding and match genomics data. Full article
(This article belongs to the Special Issue New Trends in Agricultural UAV Application)
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14 pages, 5347 KiB  
Article
Detection of Power Poles in Orchards Based on Improved Yolov5s Model
by Yali Zhang, Xiaoyang Lu, Wanjian Li, Kangting Yan, Zhenjie Mo, Yubin Lan and Linlin Wang
Agronomy 2023, 13(7), 1705; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy13071705 - 26 Jun 2023
Cited by 6 | Viewed by 1055
Abstract
During the operation of agricultural unmanned aerial vehicles (UAVs) in orchards, the presence of power poles and wires pose a serious threat to flight safety, and can even lead to crashes. Due to the difficulty of directly detecting wires, this research aimed to [...] Read more.
During the operation of agricultural unmanned aerial vehicles (UAVs) in orchards, the presence of power poles and wires pose a serious threat to flight safety, and can even lead to crashes. Due to the difficulty of directly detecting wires, this research aimed to quickly and accurately detect wire poles, and proposed an improved Yolov5s deep learning object detection algorithm named Yolov5s-Pole. The algorithm enhances the model’s generalization ability and robustness by applying Mixup data augmentation technique, replaces the C3 module with the GhostBottleneck module to reduce the model’s parameters and computational complexity, and incorporates the Shuffle Attention (SA) module to improve its focus on small targets. The results show that when the improved Yolov5s-Pole model was used for detecting poles in orchards, its accuracy, recall, and mAP@50 were 0.803, 0.831, and 0.838 respectively, which increased by 0.5%, 10%, and 9.2% compared to the original Yolov5s model. Additionally, the weights, parameters, and GFLOPs of the Yolov5s-Pole model were 7.86 MB, 3,974,310, and 9, respectively. Compared to the original Yolov5s model, these represent compression rates of 42.2%, 43.4%, and 43.3%, respectively. The detection time for a single image using this model was 4.2 ms, and good robustness under different lighting conditions (dark, normal, and bright) was demonstrated. The model is suitable for deployment on agricultural UAVs’ onboard equipment, and is of great practical significance for ensuring the efficiency and flight safety of agricultural UAVs. Full article
(This article belongs to the Special Issue New Trends in Agricultural UAV Application)
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14 pages, 2245 KiB  
Article
Citrus Identification and Counting Algorithm Based on Improved YOLOv5s and DeepSort
by Yuhan Lin, Wenxin Hu, Zhenhui Zheng and Juntao Xiong
Agronomy 2023, 13(7), 1674; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy13071674 - 21 Jun 2023
Cited by 3 | Viewed by 1158
Abstract
A method for counting the number of citrus fruits based on the improved YOLOv5s algorithm combined with the DeepSort tracking algorithm is proposed to address the problem of the low accuracy of counting citrus fruits due to shading and lighting factors in videos [...] Read more.
A method for counting the number of citrus fruits based on the improved YOLOv5s algorithm combined with the DeepSort tracking algorithm is proposed to address the problem of the low accuracy of counting citrus fruits due to shading and lighting factors in videos taken in orchards. In order to improve the recognition of citrus fruits, the attention module CBAM is fused with the backbone part of the YOLOv5s network, and the Contextual Transformer self-attention module is incorporated into the backbone network; meanwhile, SIoU is used as the new loss function instead of GIoU to further improve the accuracy of detection and to better keep the model in real time. Then, it is combined with the DeepSort algorithm to realize the counting of citrus fruits. The experimental results demonstrated that the average recognition accuracy of the improved YOLOv5s algorithm for citrus fruits improved by 3.51% compared with the original algorithm, and the average multi-target tracking accuracy for citrus fruits combined with the DeepSort algorithm was 90.83%, indicating that the improved algorithm has a higher recognition accuracy and counting precision in a complex environment, and has a better real-time performance, which can effectively achieve the real-time detection and tracking counting of citrus fruits. However, the improved algorithm has a reduced real-time performance and has difficulty in distinguishing whether or not the fruit is ripe. Full article
(This article belongs to the Special Issue New Trends in Agricultural UAV Application)
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Review

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20 pages, 1137 KiB  
Review
Drone-Related Agrotechnologies for Precise Plant Protection in Western Balkans: Applications, Possibilities, and Legal Framework Limitations
by Aleksandar Ivezić, Branislav Trudić, Zoran Stamenković, Boris Kuzmanović, Sanja Perić, Bojana Ivošević, Maša Buđen and Kristina Petrović
Agronomy 2023, 13(10), 2615; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy13102615 - 14 Oct 2023
Cited by 2 | Viewed by 3748
Abstract
Modern agriculture necessitates the use of techniques and tools that pollute the environment less and improve the safety of food and feed production. In the field of plant protection, drones are attracting increasing attention due to their versatility and applicability in a variety [...] Read more.
Modern agriculture necessitates the use of techniques and tools that pollute the environment less and improve the safety of food and feed production. In the field of plant protection, drones are attracting increasing attention due to their versatility and applicability in a variety of environmental and working conditions. Drone crop spraying techniques offer several advantages, including increased safety and cost effectiveness through autonomous and programmed operations based on specific schedules and routes. One of the main advantages of using drones for plant protection is their ability to monitor large areas of crops in a short amount of time. In addition to crop protection management, using drones for augmentative biocontrol facilitates the distribution of beneficial organisms to the exact locations where they are required, which can increase the effectiveness of biocontrol agents while reducing distribution costs. In this context, given the very limited commercial use of drones in the Western Balkans’ agri-food sector, the use of drones in the agri-food industry is a topic that needs to be elaborated on and highly promoted. Additionally, the specific legal regulations in Serbia that currently limit the use of drones in agriculture must be outlined. Conventional crop production is still significantly more prevalent in Serbia, but given the region’s continuous technological progress, there is no doubt that farmers’ education and future investments in precision agriculture will most likely increase the use of state-of-the-art technologies and drones in agriculture. Full article
(This article belongs to the Special Issue New Trends in Agricultural UAV Application)
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