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: 31 March 2025 | Viewed by 1033

Special Issue Editors


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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

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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
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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

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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 (1 paper)

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Research

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 648
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
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