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Agronomy
Special Issues
In-Field Detection and Monitoring Technology in Precision Agriculture—2nd Edition
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In-Field Detection and Monitoring Technology in Precision Agriculture—2nd Edition

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 1648

Special Issue Editors

Dr. Xi Qiao

E-Mail Website
Guest Editor
Lingnan Guangdong Laboratory of Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
Interests: image processing in agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Shuo Zhang

E-Mail Website
Guest Editor
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Interests: Intelligent agricultural equipment; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Cong Wang

E-Mail Website
Guest Editor
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Interests: intelligent sensor; agricultural Internet of Things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Precision agriculture (PA), also referred to as precision farming or smart agriculture, is a relatively recent farming management concept based on the use of information technology with the aim of obtaining higher production efficiency, sustainable profitability, and better-quality products while minimizing environmental impacts. Smarter field inspection and monitoring is one of the important technologies driving the rapid development of precision agriculture. High-throughput information data is obtained through advanced technologies, such as agricultural environment, soil and water quality parameters sensing technology, multi-scale HD/multispectral/hyperspectral images, such as UAV and satellite remote sensing, LIDAR, acoustic waves, and high-speed network technology. Then, new-generation machine learning models are used to improve the robustness of prediction algorithms to complex agricultural environments, spatial and temporal variability, etc., to provide more accurate real-time data for agricultural management.

This Special Issue intends to cover the state of the art and recent progress in different aspects related to in-field detection and monitoring technology in a wide range of agricultural fields (crops, grassland, fruit trees, water, agricultural products, etc.). All types of manuscripts (original research and reviews) providing new insights into the in-field detection and monitoring technology of agriculture are welcome. Articles may include, but are not limited to, the following topics:

  • Detection and monitoring of within-field on-farm variability;
  • Proximal and remote sensing of soils, crops, weeds, plant diseases, and insect pests;
  • Vegetation parameter sensing and management;
  • Crop models and decision support systems in PA;
  • Agricultural sensors, robotics, and engineering;
  • Plant, disease, and pest detection based on high-definition images, multispectral, hyperspectral, and sound waves;
  • Precision plant protection;
  • Water quality detection and monitoring;
  • Wireless sensor networks, Internet of Things, big data, and databases in PA.

Dr. Xi Qiao
Dr. Shuo Zhang
Dr. Cong Wang
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

  • intelligent agriculture
  • image processing
  • spectral analysis
  • quantitative inversion
  • prediction
  • early warning
  • machine learning
  • feature fusion

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Related Special Issue

Published Papers (1 paper)

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Research

19 pages, 3839 KiB  
Article
YOLO-YSTs: An Improved YOLOv10n-Based Method for Real-Time Field Pest Detection
by Yiqi Huang, Zhenhao Liu, Hehua Zhao, Chao Tang, Bo Liu, Zaiyuan Li, Fanghao Wan, Wanqiang Qian and Xi Qiao
Agronomy 2025, 15(3), 575; https://doi.org/10.3390/agronomy15030575 - 26 Feb 2025
Cited by 2 | Viewed by 1346
Abstract
The use of yellow sticky traps is a green pest control method that utilizes the pests’ attraction to the color yellow. The use of yellow sticky traps not only controls pest populations but also enables monitoring, offering a more economical and environmentally friendly [...] Read more.
The use of yellow sticky traps is a green pest control method that utilizes the pests’ attraction to the color yellow. The use of yellow sticky traps not only controls pest populations but also enables monitoring, offering a more economical and environmentally friendly alternative to pesticides. However, the small size and dense distribution of pests on yellow sticky traps lead to lower detection accuracy when using lightweight models. On the other hand, large models suffer from longer training times and deployment difficulties, posing challenges for pest detection in the field using edge computing platforms. To address these issues, this paper proposes a lightweight detection method, YOLO-YSTs, based on an improved YOLOv10n model. The method aims to balance pest detection accuracy and model size and has been validated on edge computing platforms. This model incorporates SPD-Conv convolutional modules, the iRMB inverted residual block attention mechanism, and the Inner-SIoU loss function to improve the YOLOv10n network architecture, ultimately addressing the issues of missed and false detections for small and overlapping targets while balancing model speed and accuracy. Experimental results show that the YOLO-YSTs model achieved precision, recall, mAP50, and mAP50–95 values of 83.2%, 83.2%, 86.8%, and 41.3%, respectively, on the yellow sticky trap dataset. The detection speed reached 139 FPS, with GFLOPs at only 8.8. Compared with the YOLOv10n model, the mAP50 improved by 1.7%. Compared with other mainstream object detection models, YOLO-YSTs also achieved the best overall performance. Through improvements to the YOLOv10n model, the accuracy of pest detection on yellow sticky traps was effectively enhanced, and the model demonstrated good detection performance when deployed on edge mobile platforms. In conclusion, the proposed YOLO-YSTs model offers more balanced performance in the detection of pest images on yellow sticky traps. It performs well when deployed on edge mobile platforms, making it of significant importance for field pest monitoring and integrated pest management. Full article
(This article belongs to the Special Issue In-Field Detection and Monitoring Technology in Precision Agriculture—2nd Edition)
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