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AgriEngineering
Special Issues
Machine Vision Applications in Crop Harvesting and Quality Control
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Machine Vision Applications in Crop Harvesting and Quality Control

A special issue of AgriEngineering (ISSN 2624-7402).

Deadline for manuscript submissions: closed (15 March 2026) | Viewed by 1463

Editors

Dr. Ke Zhang

E-Mail Website
Guest Editor
College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China
Interests: machine learning; image recognition; precision agriculture; remote sensing technology and methods; crop growth monitoring; nutrition diagnosis; crop nutrient management
Dr. Xiaodong Bai

E-Mail Website
Guest Editor
School of Computer Science and Technology, Hainan University, Haikou 570228, China
Interests: machine vision; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Jiayi Zhang

E-Mail
Guest Editor
Jiangsu Academy of Agricultural Sciences Wuxi Branch, Wuxi 214174, China
Interests: precision agriculture; remote sensing; crop monitoring; machine learning; crop model; unmanned aerial vehicle; satellite; image processing
Dr. Jibo Yue

E-Mail Website
Guest Editor
College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Interests: remote sensing; precision agriculture; deep learning; crop models; crop mapping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue "Machine Vision Applications in Crop Harvesting and Quality Control" explores cutting-edge advancements in automated agricultural technologies, focusing on the integration of machine vision systems to enhance efficiency and precision in crop harvesting and post-harvest quality assessment. Contributions highlight innovative approaches such as deep learning, hyperspectral imaging, and robotic automation for tasks including fruit/vegetable detection, yield estimation, defect identification, and grading. These technologies address critical challenges in modern agriculture, such as labor shortages, resource optimization, and sustainability, by enabling real-time, non-destructive monitoring and decision-making. This Special Issue also emphasizes the development of scalable solutions tailored to diverse crops and environments, from field-based robotic harvesters to AI-driven quality control systems in processing facilities. By bridging the gap between theoretical research and practical implementation, this collection aims to accelerate the adoption of smart farming practices, ultimately improving productivity, reducing waste, and ensuring food security in a rapidly evolving agricultural landscape.

We look forward to receiving your contributions.

Dr. Ke Zhang
Dr. Xiaodong Bai
Dr. Jiayi Zhang
Dr. Jibo Yue
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AgriEngineering 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 1800 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

  • AI
  • image interpretation
  • precision agriculture
  • remote sensing
  • crop yield and quality
  • machine vision
  • crop harvesting
  • quality control
  • image processing

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Published Papers (1 paper)

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Research

16 pages, 3668 KB  
Article
Research on Rice Pest Detection and Classification Based on YOLOv5 and Transformer Combination
by Qiaonan Yang, Yayong Chen, Qing Hai, Sehar Razzaq, Yiming Cui, Xingwang Wang and Beibei Zhou
AgriEngineering 2026, 8(4), 138; https://doi.org/10.3390/agriengineering8040138 - 3 Apr 2026
Viewed by 680
Abstract
The significant differences in insects trapped by pest detection lamps lead to low classification accuracy of existing models for rice pests. To address this issue, this paper proposes a small pest target detection and classification model (ViT-YOLOv5p) by integrating the YOLO backbone and [...] Read more.
The significant differences in insects trapped by pest detection lamps lead to low classification accuracy of existing models for rice pests. To address this issue, this paper proposes a small pest target detection and classification model (ViT-YOLOv5p) by integrating the YOLO backbone and Transformer module. First, the number of training samples is expanded through data augmentation during model training. Furthermore, appropriate noise data are introduced to enhance the robustness and generalization ability of the model. Before detection and classification, image cutting and stitching strategies are adopted to improve the detection accuracy of small objects. The bounding box of the pest is determined by the YOLO backbone, and the corresponding region is fed into the Transformer model to obtain the classification result. Finally, YOLOv5, Faster R-CNN, YOLOv4, and the proposed ViT-YOLOv5p are trained on the same dataset, with average detection time (ADT) and classification accuracy employed as evaluative metrics. The results show that ViT-YOLOv5p achieves the highest classification accuracy of 91.89% with an ADT of 50.41 ms. Compared with the commonly used Faster R-CNN, YOLOv5, and YOLOv4 models, the accuracy is improved by 1.50%, 8.71%, and 9.74%, respectively. This study provides a reference for agricultural pest detection, automatic insect classification systems, and deep learning-based detection of small agricultural targets. Full article
(This article belongs to the Special Issue Machine Vision Applications in Crop Harvesting and Quality Control)
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