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Agronomy
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Modern Control of Biotic Stress in Crops: Intelligent Detection and...
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Modern Control of Biotic Stress in Crops: Intelligent Detection and Precision Pesticide Application

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 3134

Special Issue Editors

Dr. Hongxing Peng

E-Mail Website
Guest Editor
College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Interests: machine vision; artificial intelligence; intelligent agriculture; agricultural robots
Special Issues, Collections and Topics in MDPI journals
Dr. Yuanyuan Shao

E-Mail Website
Guest Editor
College of Mechanical and Electrical Engineering, Shandong Agricultural University, Tai’an 271018, China
Interests: intelligent agriculture; agricultural product detection; hyperspectral image processing; deep learning; agricultural machinery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent detection and pesticide application technology have always been key areas of research in  crop production. With the development of technology and the need for precise agriculture, intelligent detection and pesticide application technologies have become increasingly important in solving the problems of agricultural production, such as ensuring yield and quality, reducing pesticide usage and protecting the environment. This topic has attracted widespread attention from scholars worldwide.

The aim of this Special Issue is to collect and publish cutting-edge research on intelligent detection and pesticide application technology for crops. We aim to provide a platform for scholars to share their experiences, ideas and the latest research results in this field. The scope of this Special Issue includes, but is not limited to, the following:

  • Intelligent detection technology for crop diseases, pests and weeds;
  • Pesticide application technology for crops;
  • Numerical simulation and optimization design of pesticide application;
  • Evaluation methods and standards for pesticide residue in products;
  • Intelligent agriculture;
  • Agricultural product detection;
  • Hyperspectral image processing;
  • Machine vision;
  • Artificial intelligence.

This Special Issue welcomes high-quality papers related to intelligent detection and pesticide application technology for crops. Papers should be original works not yet published elsewhere, or be review articles summarizing relevant research progress in this field.

Dr. Hongxing Peng
Dr. Yuanyuan Shao
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 detection
  • precision agriculture
  • machine vision
  • hyperspectral image processing
  • pesticide application
  • agricultural robots
  • agricultural big data
  • agricultural product quality and safety

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

22 pages, 52487 KiB  
Article
LCFANet: A Novel Lightweight Cross-Level Feature Aggregation Network for Small Agricultural Pest Detection
by Shijian Huang, Yunong Tian, Yong Tan and Zize Liang
Agronomy 2025, 15(5), 1168; https://doi.org/10.3390/agronomy15051168 - 11 May 2025
Viewed by 238
Abstract
In agricultural pest detection, the small size of pests poses a critical hurdle to detection accuracy. To mitigate this concern, we propose a Lightweight Cross-Level Feature Aggregation Network (LCFANet), which comprises three key components: a deep feature extraction network, a deep feature fusion [...] Read more.
In agricultural pest detection, the small size of pests poses a critical hurdle to detection accuracy. To mitigate this concern, we propose a Lightweight Cross-Level Feature Aggregation Network (LCFANet), which comprises three key components: a deep feature extraction network, a deep feature fusion network, and a multi-scale object detection head. Within the feature extraction and fusion networks, we introduce the Dual Temporal Feature Aggregation C3k2 (DTFA-C3k2) module, leveraging a spatiotemporal fusion mechanism to integrate multi-receptive field features while preserving fine-grained texture and structural details across scales. This significantly improves detection performance for objects with large scale variations. Additionally, we propose the Aggregated Downsampling Convolution (ADown-Conv) module, a dual-path compression unit that enhances feature representation while efficiently reducing spatial dimensions. For feature fusion, we design a Cross-Level Hierarchical Feature Pyramid (CLHFP), which employs bidirectional integration—backward pyramid construction for deep-to-shallow fusion and forward pyramid construction for feature refinement. The detection head incorporates a Multi-Scale Adaptive Spatial Fusion (MSASF) module, adaptively fusing features at specific scales to improve accuracy for varying-sized objects. Furthermore, we introduce the MPDINIoU loss function, combining InnerIoU and MPDIoU to optimize bounding box regression. The LCFANet-n model has 2.78M parameters and a computational cost of 6.7 GFLOPs, enabling lightweight deployment. Extensive experiments on the public dataset demonstrate that the LCFANet-n model achieves a precision of 71.7%, recall of 68.5%, mAP50 of 70.4%, and mAP50-95 of 45.1%, reaching state-of-the-art (SOTA) performance in small-sized pest detection while maintaining a lightweight architecture. Full article
(This article belongs to the Special Issue Modern Control of Biotic Stress in Crops: Intelligent Detection and Precision Pesticide Application)
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36 pages, 3917 KiB  
Article
Performance Analysis of Real-Time Detection Transformer and You Only Look Once Models for Weed Detection in Maize Cultivation
by Oscar Leonardo García-Navarrete, Jesús Hernán Camacho-Tamayo, Anibal Bregon Bregon, Jorge Martín-García and Luis Manuel Navas-Gracia
Agronomy 2025, 15(4), 796; https://doi.org/10.3390/agronomy15040796 - 24 Mar 2025
Viewed by 488
Abstract
Weeds are unwanted and invasive plants characterized by their rapid growth and ability to compete with crops for essential resources such as space, water, nutrients, and sunlight. This competition has a negative impact on crop quality and productivity. To reduce the influence of [...] Read more.
Weeds are unwanted and invasive plants characterized by their rapid growth and ability to compete with crops for essential resources such as space, water, nutrients, and sunlight. This competition has a negative impact on crop quality and productivity. To reduce the influence of weeds, precision weeding is used, which uses image sensors and computational algorithms to identify plants and classify weeds using digital images. This study used images of maize (Zea mays L.) to detect four types of weeds (Lolium rigidum, Sonchus oleraceus, Solanum nigrum, and Poa annua). For this purpose, YOLO (You Only Look Once) architectures, YOLOv8s, YOLOv9s, YOLOv10s, and YOLOv11s versions, were trained and compared, along with an architecture based on RT-DETR (Real-Time Detection Transformer), version RT-DETR-1. The YOLO architectures are noted for their real-time detection efficiency, and RT-DETR-l allows evaluation of the impact of an architecture that dispenses with Non-Maximum Suppression (NMS). The YOLOv9s model had the best overall performance, achieving a mAP@0.5 of 0.834 in 60 epochs and an F1-score of 0.78, which demonstrates a optimal balance between accuracy and recall, although with less confidence in its predictions. On the other hand, the RT-DETR-l model stood out for its efficiency in convergence, reaching a competitive performance in only 58 epochs with a mAP@0.5 of 0.828 and an F1-score of 0.80. Full article
(This article belongs to the Special Issue Modern Control of Biotic Stress in Crops: Intelligent Detection and Precision Pesticide Application)
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18 pages, 5456 KiB  
Article
Smart Agricultural Pest Detection Using I-YOLOv10-SC: An Improved Object Detection Framework
by Wenxia Yuan, Lingfang Lan, Jiayi Xu, Tingting Sun, Xinghua Wang, Qiaomei Wang, Jingnan Hu and Baijuan Wang
Agronomy 2025, 15(1), 221; https://doi.org/10.3390/agronomy15010221 - 17 Jan 2025
Cited by 1 | Viewed by 1071
Abstract
Aiming at the problems of insufficient detection accuracy and high false detection rates of traditional pest detection models in the face of small targets and incomplete targets, this study proposes an improved target detection network, I-YOLOv10-SC. The network leverages Space-to-Depth Convolution to enhance [...] Read more.
Aiming at the problems of insufficient detection accuracy and high false detection rates of traditional pest detection models in the face of small targets and incomplete targets, this study proposes an improved target detection network, I-YOLOv10-SC. The network leverages Space-to-Depth Convolution to enhance its capability in detecting small insect targets. The Convolutional Block Attention Module is employed to improve feature representation and attention focus. Additionally, Shape Weights and Scale Adjustment Factors are introduced to optimize the loss function. The experimental results show that compared with the original YOLOv10, the model generated by the improved algorithm improves the accuracy by 5.88 percentage points, the recall rate by 6.67 percentage points, the balance score by 6.27 percentage points, the mAP value by 4.26 percentage points, the bounding box loss by 18.75%, the classification loss by 27.27%, and the feature point loss by 8%. The model oscillation has also been significantly improved. The enhanced I-YOLOv10-SC network effectively addresses the challenges of detecting small and incomplete insect targets in tea plantations, offering high precision and recall rates, thus providing a solid technical foundation for intelligent pest monitoring and precise prevention in smart tea gardens. Full article
(This article belongs to the Special Issue Modern Control of Biotic Stress in Crops: Intelligent Detection and Precision Pesticide Application)
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22 pages, 4876 KiB  
Article
Innovative Ghost Channel Spatial Attention Network with Adaptive Activation for Efficient Rice Disease Identification
by Yang Zhou, Yang Yang, Dongze Wang, Yuting Zhai, Haoxu Li and Yanlei Xu
Agronomy 2024, 14(12), 2869; https://doi.org/10.3390/agronomy14122869 - 1 Dec 2024
Viewed by 988
Abstract
To address the computational complexity and deployment challenges of traditional convolutional neural networks in rice disease identification, this paper proposes an efficient and lightweight model: Ghost Channel Spatial Attention ShuffleNet with Mish-ReLU Adaptive Activation Function (GCA-MiRaNet). Based on ShuffleNet V2, we effectively reduced [...] Read more.
To address the computational complexity and deployment challenges of traditional convolutional neural networks in rice disease identification, this paper proposes an efficient and lightweight model: Ghost Channel Spatial Attention ShuffleNet with Mish-ReLU Adaptive Activation Function (GCA-MiRaNet). Based on ShuffleNet V2, we effectively reduced the model’s parameter count by streamlining convolutional layers, decreasing stacking depth, and optimizing output channels. Additionally, the model incorporates the Ghost Module as a replacement for traditional 1 × 1 convolutions, further reducing computational overhead. Innovatively, we introduce a Channel Spatial Attention Mechanism (CSAM) that significantly enhances feature extraction and generalization aimed at rice disease detection. Through combining the advantages of Mish and ReLU, we designed the Mish-ReLU Adaptive Activation Function (MAAF), enhancing the model’s generalization capacity and convergence speed. Through transfer learning and ElasticNet regularization, the model’s accuracy has notably improved while effectively avoiding overfitting. Sufficient experimental results indicate that GCA-MiRaNet attains a precision of 94.76% on the rice disease dataset, with a 95.38% reduction in model parameters and a compact size of only 0.4 MB. Compared to traditional models such as ResNet50 and EfficientNet V2, GCA-MiRaNet demonstrates significant advantages in overall performance, especially on embedded devices. This model not only enables efficient and accurate real-time disease monitoring but also provides a viable solution for rice field protection drones and Internet of Things management systems, advancing the process of contemporary agricultural smart management. Full article
(This article belongs to the Special Issue Modern Control of Biotic Stress in Crops: Intelligent Detection and Precision Pesticide Application)
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