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Agriculture
Special Issues
Advanced Image Processing in Agricultural Applications
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Advanced Image Processing in Agricultural Applications

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: 25 June 2024 | Viewed by 2058

Special Issue Editors

Dr. Jiehao Li

E-Mail Website
Guest Editor
Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, College of Engineering, South China Agricultural University, Guangzhou 510642, China
Interests: agricultural robotics; image processing; motion control; neural networks; artificial intelligence
Prof. Dr. Jun Li

E-Mail Website
Guest Editor
Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, College of Engineering, South China Agricultural University, Guangzhou 510642, China
Interests: intelligent agricultural equipment; agricultural robot; intelligent control technology; vehicle design; image processing
Prof. Dr. Weibin Wu

E-Mail Website
Guest Editor
Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, College of Engineering, South China Agricultural University, Guangzhou 510642, China
Interests: intelligent agricultural equipment; agricultural robot; vehicle systems; precision agriculture; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Application of image processing technology in agricultural production scenarios, such as fruit-picking robots, pest monitoring, growth environment factor monitoring, agricultural planting management, crop baking and drying, and seed quality breeding, has recently garnered increasing research interest. However, in a complex agricultural environment, image processing is rather difficult, as it can often lead to misclassification due to the interference of external factors, thereby resulting in erroneous experimental results. In addition, with the rapid development of image processing algorithms, the application of this technology in agriculture faces further challenges, including overlapping of agricultural products, serious occlusion of detection targets, excessive detection of targets, and difficulties in image processing due to light and camera angle. Therefore, advanced image processing technology in agriculture is an inspiring and promising area of research.

This Special Issue aims to present state-of-the-art research achievements that contribute to a better understanding of the agricultural field in terms of image processing, environment perception, and sensor fusion. We also encourage submissions of review articles.

The potential topics for this Special Issue include, but are not limited to, the following:

  • Deep learning algorithm in agricultural applications;
  • Image processing technology in pest monitoring;
  • Soil spectral data in agricultural engineering;
  • Multispectral image processing in agricultural engineering;
  • Satellite remote sensing technology in agriculture;
  • Detection and location of agricultural robotics;
  • Near-infrared image processing in agricultural engineering;
  • Hyperspectral technology in crop monitoring;
  • Machine learning technology in crop baking and drying;
  • Statistical analysis technology in crop quality assessment.

Dr. Jiehao Li
Prof. Dr. Jun Li
Prof. Dr. Weibin Wu
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. Agriculture 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

  • agricultural robotics
  • crop processing
  • computer vision
  • deep learning
  • hyperspectral imagery
  • RGB image
  • image processing
  • feature extract
  • artificial intelligence

Published Papers (2 papers)

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Research

13 pages, 2974 KiB  
Article
High-Precision Detection for Sandalwood Trees via Improved YOLOv5s and StyleGAN
by Yu Zhang, Jiajun Niu, Zezhong Huang, Chunlei Pan, Yueju Xue and Fengxiao Tan
Agriculture 2024, 14(3), 452; https://doi.org/10.3390/agriculture14030452 - 11 Mar 2024
Viewed by 828
Abstract
An algorithm model based on computer vision is one of the critical technologies that are imperative for agriculture and forestry planting. In this paper, a vision algorithm model based on StyleGAN and improved YOLOv5s is proposed to detect sandalwood trees from unmanned aerial [...] Read more.
An algorithm model based on computer vision is one of the critical technologies that are imperative for agriculture and forestry planting. In this paper, a vision algorithm model based on StyleGAN and improved YOLOv5s is proposed to detect sandalwood trees from unmanned aerial vehicle remote sensing data, and this model has excellent adaptability to complex environments. To enhance feature expression ability, a CA (coordinate attention) module with dimensional information is introduced, which can both capture target channel information and keep correlation information between long-range pixels. To improve the training speed and test accuracy, SIOU (structural similarity intersection over union) is proposed to replace the traditional loss function, whose direction matching degree between the prediction box and the real box is fully considered. To achieve the generalization ability of the model, StyleGAN is introduced to augment the remote sensing data of sandalwood trees and to improve the sample balance of different flight heights. The experimental results show that the average accuracy of sandalwood tree detection increased from 93% to 95.2% through YOLOv5s model improvement; then, on that basis, the accuracy increased by another 0.4% via data generation from the StyleGAN algorithm model, finally reaching 95.6%. Compared with the mainstream lightweight models YOLOv5-mobilenet, YOLOv5-ghost, YOLOXs, and YOLOv4-tiny, the accuracy of this method is 2.3%, 2.9%, 3.6%, and 6.6% higher, respectively. The size of the training sandalwood tree model is 14.5 Mb, and the detection time is 17.6 ms. Thus, the algorithm demonstrates the advantages of having high detection accuracy, a compact model size, and a rapid processing speed, making it suitable for integration into edge computing devices for on-site real-time monitoring. Full article
(This article belongs to the Special Issue Advanced Image Processing in Agricultural Applications)
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21 pages, 7447 KiB  
Article
DiffuCNN: Tobacco Disease Identification and Grading Model in Low-Resolution Complex Agricultural Scenes
by Huizhong Xiong, Xiaotong Gao, Ningyi Zhang, Haoxiong He, Weidong Tang, Yingqiu Yang, Yuqian Chen, Yang Jiao, Yihong Song and Shuo Yan
Agriculture 2024, 14(2), 318; https://doi.org/10.3390/agriculture14020318 - 17 Feb 2024
Viewed by 679
Abstract
A novel deep learning model, DiffuCNN, is introduced in this paper, specifically designed for counting tobacco lesions in complex agricultural settings. By integrating advanced image processing techniques with deep learning methodologies, the model significantly enhances the accuracy of detecting tobacco lesions under low-resolution [...] Read more.
A novel deep learning model, DiffuCNN, is introduced in this paper, specifically designed for counting tobacco lesions in complex agricultural settings. By integrating advanced image processing techniques with deep learning methodologies, the model significantly enhances the accuracy of detecting tobacco lesions under low-resolution conditions. After detecting lesions, the grading of the disease severity is achieved through counting. The key features of DiffuCNN include a resolution enhancement module based on diffusion, an object detection network optimized through filter pruning, and the employment of the CentralSGD optimization algorithm. Experimental results demonstrate that DiffuCNN surpasses other models in precision, with respective values of 0.98 on precision, 0.96 on recall, 0.97 on accuracy, and 62 FPS. Particularly in counting tobacco lesions, DiffuCNN exhibits an exceptional performance, attributable to its efficient network architecture and advanced image processing techniques. The resolution enhancement module based on diffusion amplifies minute details and features in images, enabling the model to more effectively recognize and count tobacco lesions. Concurrently, filter pruning technology reduces the model’s parameter count and computational burden, enhancing the processing speed while retaining the capability to recognize key features. The application of the CentralSGD optimization algorithm further improves the model’s training efficiency and final performance. Moreover, an ablation study meticulously analyzes the contribution of each component within DiffuCNN. The results reveal that each component plays a crucial role in enhancing the model performance. The inclusion of the diffusion module significantly boosts the model’s precision and recall, highlighting the importance of optimizing at the model’s input end. The use of filter pruning and the CentralSGD optimization algorithm effectively elevates the model’s computational efficiency and detection accuracy. Full article
(This article belongs to the Special Issue Advanced Image Processing in Agricultural Applications)
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