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Agriculture
Special Issues
Big Data Analytics and Machine Learning for Smart Agriculture—2nd Edition
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Big Data Analytics and Machine Learning for Smart Agriculture—2nd Edition

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

Deadline for manuscript submissions: 20 May 2024 | Viewed by 615

Special Issue Editors

Prof. Dr. Maciej Zaborowicz

E-Mail Website
Guest Editor
Department of Biosystems Engineering, Poznań University of Life Sciences, Poznan, Poland
Interests: computer image analysis; artificial neural networks; neural modeling; machine learning; deep learning; computer science in agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Jakub Frankowski

E-Mail Website
Guest Editor
Department of Bioeconomy, Institute of Natural Fibres and Medicinal Plants—National Research Institute, Wojska Polskiego 71B, 60-630 Poznań, Poland
Interests: bieconomy; waste management; agriculture; energy crops; biosystems engineering; biofuel production
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern agriculture cannot function without innovative methods, especially computer methods that collect, aggregate and analyze data to help make better decisions.

Today, decision making is based on hard data, which are collected from a large number of sensors. The way data are transmitted and stored poses many difficulties. Another important aspect is the creation of appropriate algorithms to automatically systematize information, which is then analyzed by modern IT methods such as mast learning and artificial intelligence.

The beginning of the 21st century in agriculture has witnessed widespread computerization; at this stage, that is, the 2020s, we can say that this is the era of sensors and data, which should lead the agricultural industry to intelligent management and farming.

Artificial intelligence methods have been hitting the market of machines and devices for several years now, assisting in everyday activities. As one of the oldest industries in the world, agriculture is quite conservative, but technological progress is an indispensable part of its development.

We invite papers that solve original scientific problems in the field of modern agriculture, where artificial intelligence is an effective tool in the development of the industry.

Prof. Dr. Maciej Zaborowicz
Dr. Jakub Frankowski
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

  • artificial intelligence
  • machine learning
  • data processing and analysis
  • big data
  • algorithms and data structures
  • smart agriculture
  • modern agricultural engineering

Published Papers (1 paper)

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Research

18 pages, 5741 KiB  
Article
Analysis of the Impact of Different Improvement Methods Based on YOLOV8 for Weed Detection
by Cuncai He, Fangxin Wan, Guojun Ma, Xiaobin Mou, Kaikai Zhang, Xiangfeng Wu and Xiaopeng Huang
Agriculture 2024, 14(5), 674; https://doi.org/10.3390/agriculture14050674 - 26 Apr 2024
Viewed by 354
Abstract
In response to the issues of missed detection, false positives, and low recognition rates for specific weed species during weed detection, a YOLOv8-based improved weed detection algorithm named EDS-YOLOv8 is proposed. Improvements were made in three main aspects. First, the YOLOv8 backbone network [...] Read more.
In response to the issues of missed detection, false positives, and low recognition rates for specific weed species during weed detection, a YOLOv8-based improved weed detection algorithm named EDS-YOLOv8 is proposed. Improvements were made in three main aspects. First, the YOLOv8 backbone network was enhanced with EfficientViT and RepViT architectures to improve the detection capability of dense-type weeds. Second, different attention mechanisms were added, such as SimAM and EMA, to learn 3D weights and achieve full fusion of features. BiFormer was introduced for dynamic sparse attention and resource allocation. Third, significant module improvement involved introducing dynamic snake convolution into the C2f module to further enhance detection capabilities for deformable objects, especially needle-shaped weeds. The improved model is validated on the established weed dataset. The results show that combining the original backbone network with dynamic snake convolutions yields the highest performance improvement. Precision, recall, mAP (0.5), and mAP (0.5:0.95) are improved by 5.6%, 5.8%, 6.4%, and 1%, respectively, and ablation experiments on the effects of the three improvement methods on model performance show that using EfficientViT as the backbone network while simultaneously improving the crucial module and adding the SimAM attention mechanism effectively enhances the model’s performance. Precision, recall, mAP (0.5), and mAP (0.5:0.95) are improved by 6%, 5.9%, 6.4%, and 0.7%, respectively. Full article
(This article belongs to the Special Issue Big Data Analytics and Machine Learning for Smart Agriculture—2nd Edition)
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