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Agronomy
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Revolutionizing Crop Management: Integrating UAV Technology for Precision Agriculture
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Revolutionizing Crop Management: Integrating UAV Technology for Precision Agriculture

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 3200

Special Issue Editors

Dr. Biquan Zhao

E-Mail Website
Guest Editor
Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
Interests: remote sensing; crop and range management; agricultural information engineering
Dr. Cengiz Koparan

E-Mail Website
Guest Editor
Department of Agricultural Education, Communications and Technology, Biological and Agricultural Engineering, University of Arkansas, Fayetteville, AR 72701, USA
Interests: sensors and controls; precision agriculture technology; unmanned vehicles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Precision agriculture is a modern farming strategy that places emphasis on site-specific crop management to respond to spatial and temporal field variability in crop growth, soils, and environmental conditions. It is made possible through integrating UAV technology for real-time or near-real-time observations and assessments of crop conditions throughout the growing season. UAVs equipped with various sensors allow imaging crops at a canopy level, enabling a fast and convenient field-level quantification and estimation of crop height, stresses, pest occurrence, and crop yield, which enhances the efficiency of decision making and operations compared to conventionally manned field surveys. Based on the above, we initiated a Special Issue in Agronomy on “Revolutionizing Crop Management: Integrating UAV Technology for Precision Agriculture”, which will focus on, among other things, the following:

  • The spatial and temporal analysis and zoning of field-level variability with UAVs;
  • Combining UAV technology with other methods in precision agriculture;
  • Crop sensing with UAV multispectral, hyperspectral, and other sensors;
  • Applications of crop management enabled by UAV technology;
  • Spray and seeding applications with UAVs.

Dr. Biquan Zhao
Dr. Cengiz Koparan
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

  • UAV remote sensing
  • precision farming
  • site-specific management
  • UAV image processing
  • robotic and sensor technology
  • crop modeling
  • drone applications

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Published Papers (4 papers)

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Research

24 pages, 10540 KiB  
Article
Field Rice Growth Monitoring and Fertilization Management Based on UAV Spectral and Deep Image Feature Fusion
by Bingnan Chen, Qihe Su, Yansong Li, Rui Chen, Wanneng Yang and Chenglong Huang
Agronomy 2025, 15(4), 886; https://doi.org/10.3390/agronomy15040886 - 1 Apr 2025
Viewed by 237
Abstract
Rice, as a globally vital staple crop, requires efficient field monitoring to ensure optimal growth conditions. This study proposed a novel framework for classifying nutrient deficiencies and formulating fertilization strategies in field-grown rice by fusing UAV-derived vegetation indices (VIs) with deep image features [...] Read more.
Rice, as a globally vital staple crop, requires efficient field monitoring to ensure optimal growth conditions. This study proposed a novel framework for classifying nutrient deficiencies and formulating fertilization strategies in field-grown rice by fusing UAV-derived vegetation indices (VIs) with deep image features extracted via deep neural networks. The framework integrated visible light VIs, spectral VIs, and image features to provide a comprehensive reflection of crop nutritional conditions, aligning closely with practical production needs. The deep image features achieved nutrition classification accuracies of 88.78% and 84.56% for rice spikelet protection fertilizer application stage (S1) and bud-promoting fertilizer application stage (S2), while the fusion of VIs and deep image features significantly enhanced the accuracy of nutrient classification, with the RF model achieving the highest accuracy (97.50% in S1 and 96.56% in S2). The proposed fertilization strategy effectively improved rice growth traits, demonstrating the potential of UAV-based remote sensing for precision agriculture, which would provide a scalable solution for optimizing rice cultivation and ensuring food security. Full article
(This article belongs to the Special Issue Revolutionizing Crop Management: Integrating UAV Technology for Precision Agriculture)
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30 pages, 6363 KiB  
Article
Using High-Resolution Multispectral Data to Evaluate In-Season Cotton Growth Parameters and End-of-the-Season Cotton Fiber Yield and Quality
by Lorena N. Lacerda, Matheus Ardigueri, Thiago O. C. Barboza, John Snider, Devendra P. Chalise, Stefano Gobbo and George Vellidis
Agronomy 2025, 15(3), 692; https://doi.org/10.3390/agronomy15030692 - 13 Mar 2025
Viewed by 647
Abstract
Estimating cotton fiber quality early in the season, or its field variability, is impractical due to limitations in current methods, and it has not been widely explored. Similarly, few studies have tried estimating the parameters contributing to in-season cotton yield using UAV-based sensors. [...] Read more.
Estimating cotton fiber quality early in the season, or its field variability, is impractical due to limitations in current methods, and it has not been widely explored. Similarly, few studies have tried estimating the parameters contributing to in-season cotton yield using UAV-based sensors. Thus, this study aims to explore the potential of using UAV-based multispectral images to estimate important in-season parameters, such as intercepted photosynthetically active radiation (IPAR), cotton height, the number of mainstem nodes, leaf area index (LAI), and end-of-the-season yield and cotton fiber quality parameters. Research trials were carried out in 2018 and 2020 in two experimental fields. In both years, a randomized complete block design was used with three cotton cultivars (2018), three plant growth regulators (2020), and three different irrigation levels to promote variability (both years). Cotton growth parameters were collected throughout the season on the same dates as UAV flights. Yield and fiber quality data were collected during harvest. The VI-based models used in this study were mostly sensitive to differences in cotton growth and final yield but less sensitive in detecting variation in cotton fiber quality indicators, such as length, strength, and micronaire, early in the season. The best performing regression model among the three fiber quality indicators was achieved in 2020, using a combination of four VIs, which explained 68% of the micronaire variability at 71 DAP. Results from this study also showed that multispectral-based VIs can be applied as early as the squaring stage at around 44 DAP to estimate most cotton growth indicators and final lint yield. Multiple linear regression validation models for height using NDVI, GNDVI, and RDVI obtained an R2 of 0.62, and for LAI using MSR and NDVI an R2 of 0.60. For lint yield, the best regression model combined four VIs and explained 66% of the yield variability. The ability to capture the variability in important growth and yield parameters early in the season can provide useful insights on potential crop performance and aid in in-season decisions. Full article
(This article belongs to the Special Issue Revolutionizing Crop Management: Integrating UAV Technology for Precision Agriculture)
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15 pages, 9987 KiB  
Article
Characterizing Optimum N Rate in Waterlogged Maize (Zea mays L.) with Unmanned Aerial Vehicle (UAV) Remote Sensing
by Bhawana Acharya, Syam Dodla, Brenda Tubana, Thanos Gentimis, Fagner Rontani, Rejina Adhikari, Dulis Duron, Giulia Bortolon and Tri Setiyono
Agronomy 2025, 15(2), 434; https://doi.org/10.3390/agronomy15020434 - 10 Feb 2025
Viewed by 643
Abstract
High soil moisture due to frequent excessive precipitation can lead to reductions in maize grain yields and increased nitrogen (N) loss. The traditional methods of computing N status in crops are destructive and time-consuming, especially in waterlogged fields. Therefore, in this study, we [...] Read more.
High soil moisture due to frequent excessive precipitation can lead to reductions in maize grain yields and increased nitrogen (N) loss. The traditional methods of computing N status in crops are destructive and time-consuming, especially in waterlogged fields. Therefore, in this study, we used unmanned aerial vehicle (UAV) remote sensing to evaluate the status of maize under different N rates and excessive soil moisture conditions. The experiment was performed using a split plot design with four replications, with soil moisture conditions as main plots and different N rates as sub-plots. The artificial intelligence SciPy (version 1.5.2) optimization algorithm and spherical function were used to estimate the economically optimum N rate under the different treatments. The computed EONR for CRS 2022 was 157 kg N ha−1 for both treatments, with the maximum net return to N of USD 1203 ha−1. In 2023, the analysis suggested a lower maximum attainable yield in excessive water conditions, with EONR pushed up to 197 kg N ha−1 as compared to 185 kg N ha−1 in the control treatment, resulting in a lower maximum net return to N of USD 884 ha−1 as compared to USD 1019 ha−1 in the control treatment. This study reveals a slight reduction of the fraction of NDRE at EONR to maximum NDRE under excessive water conditions, highlighting the need for addressing such abiotic stress circumstances when arriving at an N rate recommendation based on an N-rich strip concept. This study confirms the importance of sensing technology for N monitoring in maize, particularly in supporting decision making in nutrient management under adverse weather conditions. Full article
(This article belongs to the Special Issue Revolutionizing Crop Management: Integrating UAV Technology for Precision Agriculture)
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13 pages, 4060 KiB  
Article
Monitoring of Broccoli Flower Head Development in Fields Using Drone Imagery and Deep Learning Methods
by Chenzi Zhang, Xiaoxue Sun, Shuxin Xuan, Jun Zhang, Dongfang Zhang, Xiangyang Yuan, Xiaofei Fan and Xuesong Suo
Agronomy 2024, 14(11), 2496; https://doi.org/10.3390/agronomy14112496 - 25 Oct 2024
Viewed by 942
Abstract
For different broccoli materials, it used to be necessary to manually plant in a large area for the investigation of flower ball information, and this method is susceptible to subjective influence, which is not only time-consuming and laborious but may also cause some [...] Read more.
For different broccoli materials, it used to be necessary to manually plant in a large area for the investigation of flower ball information, and this method is susceptible to subjective influence, which is not only time-consuming and laborious but may also cause some damage to the broccoli in the process of investigation. Therefore, the rapid and nondestructive monitoring of flower heads is key to acquiring high-throughput phenotypic information on broccoli crops. In this study, we used an unmanned aerial vehicle (UAV) to acquire hundreds of images of field-grown broccoli to evaluate their flower head development rate and sizes during growth. First, YOLOv5 and YOLOv8 were used to complete the position detection and counting statistics at the seedling and heading stages. Then, UNet, PSPNet, DeepLabv3+, and SC-DeepLabv3+ were used to segment the flower heads in the images. The improved SC-DeepLabv3+ model excelled in segmenting flower heads, showing Precision, reconciled mean F1-score, mean intersection over union, and mean pixel accuracy values of 93.66%, 95.24%, 91.47%, and 97.24%, respectively, which were 0.57, 1.12, 1.16, and 1.70 percentage points higher than the respective values achieved with the DeepLabv3+ model. Flower head sizes were predicted on the basis of the pixel value of individual flower heads and ground sampling distance, yielding predictions with an R2 value of 0.67 and root-mean-squared error of 1.81 cm. Therefore, the development rate and sizes of broccoli flower heads during growth were successively estimated and calculated. Compared with the existing technology, it greatly improves work efficiency and can help to obtain timely information on crop growth in the field. Our methodology provides a convenient, fast, and reliable way for investigating field traits in broccoli breeding. Full article
(This article belongs to the Special Issue Revolutionizing Crop Management: Integrating UAV Technology for Precision Agriculture)
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