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UAVs in Precision Agriculture: Challenges and Future Perspectives
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UAVs in Precision Agriculture: Challenges and Future Perspectives

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Smart Agriculture".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 4369

Special Issue Editors

Dr. Yiannis Ampatzidis

E-Mail Website
Guest Editor
Southwest Florida Research and Education Center (SWFREC), University of Florida, Gainesville, FL, USA
Interests: precision agriculture; automation; robotics; UAVs; machine vision; sensing; artificial intelligence; farm machinery
Special Issues, Collections and Topics in MDPI journals
Dr. Spyros Fountas

E-Mail Website
Guest Editor
Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, 11855 Athens, Greece
Interests: precision agriculture; UAV; information systems; farm machinery
Special Issues, Collections and Topics in MDPI journals
Dr. Alireza Pourreza

E-Mail Website
Guest Editor
Department of Biological and Agricultural Engineering, University of California, Davis, One Shields Ave, Davis, CA 95616, USA
Interests: digital agriculture; radiative transfer modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Traditional sensing technologies in crop production, for pest and disease monitoring, nutrient and water management, and plant phenotyping, rely on manual scouting and sampling and are time-consuming as well as labor-intensive. Furthermore, the availability of skilled personnel for field scouting is a major issue.

Unmanned aerial vehicles (UAVs) equipped with various sensors (e.g., RGB, multispectral, hyperspectral, and Lidar) have recently become flexible and cost-effective solutions for rapid, precise, and non-destructive high-throughput phenotyping. Since UAVs collect a huge and complex amount of data (from a variety of sensors), artificial intelligence (AI), big data analytics, and cloud computing were utilized to increase data processing efficiency and provide data security as well as scalability.

Currently, the applications of UAVs in precision agriculture have achieved significant results and expanded across all areas, including soil analysis, seed sowing, water and nutrient assessment as well as management, pesticide and fertilizer spraying, the release of beneficial insects (predators) in organic farms, and crop growth assessment as well as mapping. There is no doubt that the prospects of UAVs being applied to precision agriculture are immense.

This Special Issue aims to provide a platform with which to gather recent developments in UAVs applied to agriculture and their supporting technologies. Both original research papers and review articles are welcome.

Dr. Yiannis Ampatzidis
Dr. Spyros Fountas
Dr. Alireza Pourreza
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. Sensors is an international peer-reviewed open access semimonthly 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

  • artificial intelligence
  • crop monitoring
  • nutrient management
  • phenotyping
  • remote sensing

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Published Papers (2 papers)

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Research

15 pages, 2410 KiB  
Article
Integrated UAV and Satellite Multi-Spectral for Agricultural Drought Monitoring of Winter Wheat in the Seedling Stage
by Xiaohui Yang, Feng Gao, Hongwei Yuan and Xiuqing Cao
Sensors 2024, 24(17), 5715; https://doi.org/10.3390/s24175715 - 2 Sep 2024
Cited by 3 | Viewed by 1492
Abstract
Agricultural droughts are a threat to local economies, as they disrupt crops. The monitoring of agricultural droughts is of practical significance for mitigating loss. Even though satellite data have been extensively used in agricultural studies, realizing wide-range, high-resolution, and high-precision agricultural drought monitoring [...] Read more.
Agricultural droughts are a threat to local economies, as they disrupt crops. The monitoring of agricultural droughts is of practical significance for mitigating loss. Even though satellite data have been extensively used in agricultural studies, realizing wide-range, high-resolution, and high-precision agricultural drought monitoring is still difficult. This study combined the high spatial resolution of unmanned aerial vehicle (UAV) remote sensing with the wide-range monitoring capability of Landsat-8 and employed the local average method for upscaling to match the remote sensing images of the UAVs with satellite images. Based on the measured ground data, this study employed two machine learning algorithms, namely, random forest (RF) and eXtreme Gradient Boosting (XGBoost1.5.1), to establish the inversion models for the relative soil moisture. The results showed that the XGBoost model achieved a higher accuracy for different soil depths. For a soil depth of 0–20 cm, the XGBoost model achieved the optimal result (R2 = 0.6863; root mean square error (RMSE) = 3.882%). Compared with the corresponding model for soil depth before the upscaling correction, the UAV correction can significantly improve the inversion accuracy of the relative soil moisture according to satellite remote sensing. To conclude, a map of the agricultural drought grade of winter wheat in the Huaibei Plain in China was drawn up. Full article
(This article belongs to the Special Issue UAVs in Precision Agriculture: Challenges and Future Perspectives)
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21 pages, 5565 KiB  
Article
In-Season Cotton Yield Prediction with Scale-Aware Convolutional Neural Network Models and Unmanned Aerial Vehicle RGB Imagery
by Haoyu Niu, Janvita Reddy Peddagudreddygari, Mahendra Bhandari, Juan A. Landivar, Craig W. Bednarz and Nick Duffield
Sensors 2024, 24(8), 2432; https://doi.org/10.3390/s24082432 - 10 Apr 2024
Cited by 4 | Viewed by 2243
Abstract
In the pursuit of sustainable agriculture, efficient water management remains crucial, with growers relying on advanced techniques for informed decision-making. Cotton yield prediction, a critical aspect of agricultural planning, benefits from cutting-edge technologies. However, traditional methods often struggle to capture the nuanced complexities [...] Read more.
In the pursuit of sustainable agriculture, efficient water management remains crucial, with growers relying on advanced techniques for informed decision-making. Cotton yield prediction, a critical aspect of agricultural planning, benefits from cutting-edge technologies. However, traditional methods often struggle to capture the nuanced complexities of crop health and growth. This study introduces a novel approach to cotton yield prediction, leveraging the synergy between Unmanned Aerial Vehicles (UAVs) and scale-aware convolutional neural networks (CNNs). The proposed model seeks to harness the spatiotemporal dynamics inherent in high-resolution UAV imagery to improve the accuracy of the cotton yield prediction. The CNN component adeptly extracts spatial features from UAV-derived imagery, capturing intricate details related to crop health and growth, modeling temporal dependencies, and facilitating the recognition of trends and patterns over time. Research experiments were carried out in a cotton field at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, with three replications evaluating four irrigation treatments (rainfed, full irrigation, percent deficit of full irrigation, and time delay of full irrigation) on cotton yield. The prediction revealed that the proposed CNN regression models outperformed conventional CNN models, such as AlexNet, CNN-3D, CNN-LSTM, ResNet. The proposed CNN model showed state-of-art performance at different image scales, with the R2 exceeding 0.9. At the cotton row level, the mean absolute error (MAE) and mean absolute percentage error (MAPE) were 3.08 pounds per row and 7.76%, respectively. At the cotton grid level, the MAE and MAPE were 0.05 pounds and 10%, respectively. This shows the proposed model’s adaptability to the dynamic interplay between spatial and temporal factors that affect cotton yield. The authors conclude that integrating UAV-derived imagery and CNN regression models is a potent strategy for advancing precision agriculture, providing growers with a powerful tool to optimize cultivation practices and enhance overall cotton productivity. Full article
(This article belongs to the Special Issue UAVs in Precision Agriculture: Challenges and Future Perspectives)
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