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Agriculture
Special Issues
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: 20 July 2024 | Viewed by 1763

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 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

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

Published Papers (1 paper)

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Research

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
Viewed by 1541
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 [email protected] 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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