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Agriculture
Special Issues
Computers and IT Solutions for Agriculture and Their Application
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Computers and IT Solutions for Agriculture and Their Application

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

Deadline for manuscript submissions: 25 September 2025 | Viewed by 586

Special Issue Editor

Dr. Dimitre Dimitrov

E-Mail Website
Guest Editor
Department of University Transfer, Faculty of Arts & Sciences, NorQuest College, Edmonton, AB T5J 1L6, Canada
Interests: mathematical-process-based; machine learning modeling; ecohydrology; biogeochemistry; ecosystem productivity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is a continuation of the Special Issues “Internet and Computers for Agriculture” and “Computational, AI and IT Solutions Helping Agriculture”. It aims to cover various contemporary digital solutions and their application in modern agriculture that can facilitate the rapid growth of the human population under global climate and environmental change. It also addresses the need for immediate actions to maintain sustainable and secure food production and water supply, while mitigating greenhouse (GHG) gas emissions, and contribute to soil and environmental health.

This Special Issue provides a stage for the growing community of digital scientists and entrepreneurs to present their innovative research in various aspects of agricultural science and practices. We welcome the submission of original scientific articles and reviews discussing the development and application of the following techniques, additionally considering their contribution to contemporary and future agriculture: artificial intelligence (AI), deep learning (DL) and machine learning (ML) methods for precision agriculture, monitoring, cultivation, harvesting, marketing, management, decision making, weather forecasting, optimization, natural language processing, computer/machine vision, smart agriculture machinery and robots, drones, real-time detection systems, sensors for field operations, diagnostics, species and disease recognition, Internet of Things (IoT) devices, web applications and mobile apps, cloud technologies, big data collections, machine learning modeling, and mathematical process-based ecosystem modeling.

Dr. Dimitre Dimitrov
Guest Editor

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

  • smart solutions for agriculture
  • artificial intelligence
  • deep learning
  • machine learning
  • big data
  • internet of things
  • modeling
  • ecohydrology
  • biogeochemistry
  • plant and ecosystem productivity

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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (1 paper)

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Research

30 pages, 5355 KiB  
Article
Instance Segmentation of Sugar Apple (Annona squamosa) in Natural Orchard Scenes Using an Improved YOLOv9-seg Model
by Guanquan Zhu, Zihang Luo, Minyi Ye, Zewen Xie, Xiaolin Luo, Hanhong Hu, Yinglin Wang, Zhenyu Ke, Jiaguo Jiang and Wenlong Wang
Agriculture 2025, 15(12), 1278; https://doi.org/10.3390/agriculture15121278 - 13 Jun 2025
Viewed by 142
Abstract
Sugar apple (Annona squamosa) is prized for its excellent taste, rich nutrition, and diverse uses, making it valuable for both fresh consumption and medicinal purposes. Predominantly found in tropical regions of the Americas and Asia, its harvesting remains labor-intensive in orchard [...] Read more.
Sugar apple (Annona squamosa) is prized for its excellent taste, rich nutrition, and diverse uses, making it valuable for both fresh consumption and medicinal purposes. Predominantly found in tropical regions of the Americas and Asia, its harvesting remains labor-intensive in orchard settings, resulting in low efficiency and high costs. This study investigates the use of computer vision for sugar apple instance segmentation and introduces an improved deep learning model, GCE-YOLOv9-seg, specifically designed for orchard conditions. The model incorporates Gamma Correction (GC) to enhance image brightness and contrast, improving target region identification and feature extraction in orchard settings. An Efficient Multiscale Attention (EMA) mechanism was added to strengthen feature representation across scales, addressing sugar apple variability and maturity differences. Additionally, a Convolutional Block Attention Module (CBAM) refined the focus on key regions and deep semantic features. The model’s performance was evaluated on a self-constructed dataset of sugar apple instance segmentation images captured under natural orchard conditions. The experimental results demonstrate that the proposed GCE-YOLOv9-seg model achieved an F1 score (F1) of 90.0%, a precision (P) of 89.6%, a recall (R) level of 93.4%, a mAP@0.5 of 73.2%, and a mAP@[0.5:0.95] of 73.2%. Compared to the original YOLOv9-seg model, the proposed GCE-YOLOv9-seg showed improvements of 1.5% in the F1 score and 3.0% in recall for object detection, while the segmentation task exhibited increases of 0.3% in mAP@0.5 and 1.0% in mAP@[0.5:0.95]. Furthermore, when compared to the latest model YOLOv12-seg, the proposed GCE-YOLOv9-seg still outperformed with an F1 score increase of 2.8%, a precision (P) improvement of 0.4%, and a substantial recall (R) boost of 5.0%. In the segmentation task, mAP@0.5 rose by 3.8%, while mAP@[0.5:0.95] demonstrated a significant enhancement of 7.9%. This method may be directly applied to sugar apple instance segmentation, providing a promising solution for automated sugar apple detection in natural orchard environments. Full article
(This article belongs to the Special Issue Computers and IT Solutions for Agriculture and Their Application)
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