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Agriculture
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Machine Vision Solutions and AI-Driven Systems in Agriculture
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Machine Vision Solutions and AI-Driven Systems in Agriculture

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

Deadline for manuscript submissions: closed (20 July 2024) | Viewed by 9296

Special Issue Editors

Dr. Ali Alameer

E-Mail Website
Guest Editor
School of Science, Engineering and Environment, University of Salford Manchester, Salford M5 4WT, UK
Interests: precision livestock farming; machine learning; deep learning; machine/robotic vision; digital signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Taha Mansouri

E-Mail Website
Guest Editor
School of Science, Engineering and Environment, University of Salford Manchester, Salford M5 4WT, UK
Interests: machine learning; deep learning; computer vision; complex systems modelling; explainable AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, "Machine Vision Solutions and AI-Driven Systems in Agriculture", focuses on the transformative synergy between machine vision and artificial intelligence (AI) in advancing agricultural practices. This includes employing machine vision to identify animal behaviours for enhanced livestock monitoring. Additionally, it investigates the integration of AI-driven systems in the creation of, e.g., early-warning mechanisms for livestock health, thus demonstrating the potential to predict and prevent diseases or problems. Authors contributing to this Special Issue will illuminate the profound impact of these technologies on agriculture, showcasing innovative approaches that utilize machine vision and AI for improved efficiency, sustainable practices, and proactive disease management.

Dr. Ali Alameer
Dr. Taha Mansouri
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 semimonthly 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

  • precision agriculture
  • machine vision
  • machine learning
  • deep learning
  • artificial intelligence
  • livestock monitoring
  • early-warning systems

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Published Papers (4 papers)

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Research

18 pages, 7349 KiB  
Article
YOLOv8-GABNet: An Enhanced Lightweight Network for the High-Precision Recognition of Citrus Diseases and Nutrient Deficiencies
by Qiufang Dai, Yungao Xiao, Shilei Lv, Shuran Song, Xiuyun Xue, Shiyao Liang, Ying Huang and Zhen Li
Agriculture 2024, 14(11), 1964; https://doi.org/10.3390/agriculture14111964 - 1 Nov 2024
Cited by 2 | Viewed by 1197
Abstract
Existing deep learning models for detecting citrus diseases and nutritional deficiencies grapple with issues related to recognition accuracy, complex backgrounds, occlusions, and the need for lightweight architecture. In response, we developed an improved YOLOv8-GABNet model designed specifically for citrus disease and nutritional deficiency [...] Read more.
Existing deep learning models for detecting citrus diseases and nutritional deficiencies grapple with issues related to recognition accuracy, complex backgrounds, occlusions, and the need for lightweight architecture. In response, we developed an improved YOLOv8-GABNet model designed specifically for citrus disease and nutritional deficiency detection, which effectively addresses these challenges. This model incorporates several key enhancements: A lightweight ADown subsampled convolutional block is utilized to reduce both the model’s parameter count and its computational demands, replacing the traditional convolutional module. Additionally, a weighted Bidirectional Feature Pyramid Network (BiFPN) supersedes the original feature fusion network, enhancing the model’s ability to manage complex backgrounds and achieve multiscale feature extraction and integration. Furthermore, we introduced important features through the Global to Local Spatial Aggregation module (GLSA), focusing on crucial image details to enhance both the accuracy and robustness of the model. This study processed the collected images, resulting in a dataset of 1102 images. Using LabelImg, bounding boxes were applied to annotate leaves affected by diseases. The dataset was constructed to include three types of citrus diseases—anthracnose, canker, and yellow vein disease—as well as two types of nutritional deficiencies, namely magnesium deficiency and manganese deficiency. This dataset was expanded to 9918 images through data augmentation and was used for experimental validation. The results show that, compared to the original YOLOv8, our YOLOv8-GABNet model reduces the parameter count by 43.6% and increases the mean Average Precision (mAP50) by 4.3%. Moreover, the model size was reduced from 50.1 MB to 30.2 MB, facilitating deployment on mobile devices. When compared with mainstream models like YOLOv5s, Faster R-CNN, SSD, YOLOv9t, and YOLOv10n, the YOLOv8-GABNet model demonstrates superior performance in terms of size and accuracy, offering an optimal balance between performance, size, and speed. This study confirms that the model effectively identifies the common diseases and nutritional deficiencies of citrus from Conghua’s “Citrus Planet”. Future deployment to mobile devices will provide farmers with instant and precise support. Full article
(This article belongs to the Special Issue Machine Vision Solutions and AI-Driven Systems in Agriculture)
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18 pages, 3834 KiB  
Article
Improved Tomato Leaf Disease Recognition Based on the YOLOv5m with Various Soft Attention Module Combinations
by Yong-Suk Lee, Maheshkumar Prakash Patil, Jeong Gyu Kim, Seong Seok Choi, Yong Bae Seo and Gun-Do Kim
Agriculture 2024, 14(9), 1472; https://doi.org/10.3390/agriculture14091472 - 29 Aug 2024
Cited by 5 | Viewed by 1598
Abstract
To reduce production costs, environmental effects, and crop losses, tomato leaf disease recognition must be accurate and fast. Early diagnosis and treatment are necessary to cure and control illnesses and ensure tomato output and quality. The YOLOv5m was improved by using C3NN modules [...] Read more.
To reduce production costs, environmental effects, and crop losses, tomato leaf disease recognition must be accurate and fast. Early diagnosis and treatment are necessary to cure and control illnesses and ensure tomato output and quality. The YOLOv5m was improved by using C3NN modules and Bidirectional Feature Pyramid Network (BiFPN) architecture. The C3NN modules were designed by integrating several soft attention modules into the C3 module: the Convolutional Block Attention Module (CBAM), Squeeze and Excitation Network (SE), Efficient Channel Attention (ECA), and Coordinate Attention (CA). The C3 modules in the Backbone and Head of YOLOv5 model were replaced with the C3NN to improve feature representation and object detection accuracy. The BiFPN architecture was implemented in the Neck of the YOLOv5 model to effectively merge multi-scale features and improve the accuracy of object detection. Among the various combinations for the improved YOLOv5m model, the C3ECA-BiFPN-C3ECA-YOLOv5m achieved a precision (P) of 87.764%, a recall (R) of 87.201%, an F1 of 87.482, an mAP.5 of 90.401%, and an mAP.5:.95 of 68.803%. In comparison with the YOLOv5m and Faster-RCNN models, the improved models showed improvement in P by 1.36% and 7.80%, R by 4.99% and 5.51%, F1 by 3.18% and 6.86%, mAP.5 by 1.74% and 2.90%, and mAP.5:.95 by 3.26% and 4.84%, respectively. These results demonstrate that the improved models have effective tomato leaf disease recognition capabilities and are expected to contribute significantly to the development of plant disease detection technology. Full article
(This article belongs to the Special Issue Machine Vision Solutions and AI-Driven Systems in Agriculture)
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16 pages, 12863 KiB  
Article
Research on Multi-Step Fruit Color Prediction Model of Tomato in Solar Greenhouse Based on Time Series Data
by Shufeng Liu, Hongrui Yuan, Yanping Zhao, Tianhua Li, Linlu Zu and Siyuan Chang
Agriculture 2024, 14(8), 1211; https://doi.org/10.3390/agriculture14081211 - 24 Jul 2024
Cited by 1 | Viewed by 1228
Abstract
Color change is the most obvious characteristic of the tomato ripening stage and an important indicator of the tomato ripening condition, which directly affects the commodity value of tomato. To visualize the color change of tomato fruit during the mature stage, this paper [...] Read more.
Color change is the most obvious characteristic of the tomato ripening stage and an important indicator of the tomato ripening condition, which directly affects the commodity value of tomato. To visualize the color change of tomato fruit during the mature stage, this paper proposes a gated recurrent unit network with an encoder–decoder structure. This structure dynamically simulates the growth and development of tomatoes using time-dependent lines, incorporating real-time information such as tomato color and shape. Firstly, the .json file was converted into a mask.png file, the tomato mask was extracted, and the tomato was separated from the complex background environment, thus successfully constructing the tomato growth and development dataset. The experimental results showed that for the gated recurrent unit network with the encoder–decoder structure proposed, when the hidden layer number was 1 and hidden layer number was 512, a high consistency and similarity between the model predicted image sequence and the actual growth and development image sequence was realized, and the structural similarity index measure was 0.746. It was proved that when the average temperature was 24.93 °C, the average soil temperature was 24.06 °C, and the average light intensity was 11.26 Klux, the environment was the most suitable for tomato growth. The environmental data-driven tomato growth model was constructed to explore the growth status of tomato under different environmental conditions, and thus, to understand the growth status of tomato in time. This study provides a theoretical foundation for determining the optimal greenhouse environmental conditions to achieve tomato maturity and it offers recommendations for investigating the growth cycle of tomatoes, as well as technical assistance for standardized cultivation in solar greenhouses. Full article
(This article belongs to the Special Issue Machine Vision Solutions and AI-Driven Systems in Agriculture)
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17 pages, 4504 KiB  
Article
Lightweight Network for Corn Leaf Disease Identification Based on Improved YOLO v8s
by Rujia Li, Yadong Li, Weibo Qin, Arzlan Abbas, Shuang Li, Rongbiao Ji, Yehui Wu, Yiting He and Jianping Yang
Agriculture 2024, 14(2), 220; https://doi.org/10.3390/agriculture14020220 - 29 Jan 2024
Cited by 23 | Viewed by 4297
Abstract
This research tackles the intricate challenges of detecting densely distributed maize leaf diseases and the constraints inherent in YOLO-based detection algorithms. It introduces the GhostNet_Triplet_YOLOv8s algorithm, enhancing YOLO v8s by integrating the lightweight GhostNet (Ghost Convolutional Neural Network) structure, which replaces the YOLO [...] Read more.
This research tackles the intricate challenges of detecting densely distributed maize leaf diseases and the constraints inherent in YOLO-based detection algorithms. It introduces the GhostNet_Triplet_YOLOv8s algorithm, enhancing YOLO v8s by integrating the lightweight GhostNet (Ghost Convolutional Neural Network) structure, which replaces the YOLO v8s backbone. This adaptation involves swapping the head’s C2f (Coarse-to-Fine) and Conv (Convolutional) modules with C3 Ghost and GhostNet, simplifying the model architecture while significantly amplifying detection speed. Additionally, a lightweight attention mechanism, Triplet Attention, is incorporated to refine the accuracy in identifying the post-neck layer output and to precisely define features within disease-affected areas. By introducing the ECIoU_Loss (EfficiCLoss Loss) function, replacing the original CIoU_Loss, the algorithm effectively mitigates issues associated with aspect ratio penalties, resulting in marked improvements in recognition and convergence rates. The experimental outcomes display promising metrics with a precision rate of 87.50%, a recall rate of 87.70%, and an mAP@0.5 of 91.40% all within a compact model size of 11.20 MB. In comparison to YOLO v8s, this approach achieves a 0.3% increase in mean average precision (mAP), reduces the model size by 50.2%, and significantly decreases FLOPs by 43.1%, ensuring swift and accurate maize disease identification while optimizing memory usage. Furthermore, the practical deployment of the trained model on a WeChat developer mini-program underscores its practical utility, enabling real-time disease detection in maize fields to aid in timely agricultural decision-making and disease prevention strategies. Full article
(This article belongs to the Special Issue Machine Vision Solutions and AI-Driven Systems in Agriculture)
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