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Agriculture
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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 "Artificial Intelligence and Digital Agriculture".

Deadline for manuscript submissions: closed (25 September 2025) | Viewed by 11915

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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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Related Special Issue

Published Papers (6 papers)

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Research

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36 pages, 1588 KB  
Article
AGRICLIMA: Towards a Federated Platform for Spatiotemporal Risk Analysis in Agriculture
by Miguel Pincheira, Fabio Antonelli and Massimo Vecchio
Agriculture 2025, 15(23), 2450; https://doi.org/10.3390/agriculture15232450 - 26 Nov 2025
Viewed by 1010
Abstract
Climate change intensifies agricultural risks, requiring an integrated analysis of climatic, hydrological, and crop data to support resilient farming. Despite advances in remote sensing, in-field sensors, and artificial intelligence, fragmented data silos hinder spatiotemporal risk assessments by requiring labor-intensive data handling. We present [...] Read more.
Climate change intensifies agricultural risks, requiring an integrated analysis of climatic, hydrological, and crop data to support resilient farming. Despite advances in remote sensing, in-field sensors, and artificial intelligence, fragmented data silos hinder spatiotemporal risk assessments by requiring labor-intensive data handling. We present agriclima, a federated, cloud-native, FAIR-by-design platform that unifies heterogeneous agricultural and environmental datasets under consistent identity, policy, and metadata governance. Its scalable open-source architecture, compliance with INSPIRE and RNDT standards, and privacy-preserving access enable researchers and decision-makers to perform comprehensive analyses with minimal coding, accelerating data-driven agricultural risk management. Developed and tested in a research project by a consortium of stakeholders in agricultural risk management, the platform was evaluated via: (1) FAIR assessment of 26 datasets using F-UJI, (2) system performance monitoring on Kubernetes, and (3) a demonstrative spatiotemporal aggregation use case. It achieved 80% average FAIR compliance, with perfect accessibility (7.00/7.00), while findability and reusability remain key areas for improvement. Performance showed stable operation (CPU 17.24%, memory 49.89%) with capacity headroom. The demonstrative use case validated that researchers can conduct spatiotemporal analyses with minimal coding effort through the abstracted data access components. Beyond technical evaluation, we share lessons learned to guide future platform development and metadata standardization, highlighting the platform’s effectiveness as a foundation for data-driven agricultural decision-making. Full article
(This article belongs to the Special Issue Computers and IT Solutions for Agriculture and Their Application)
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27 pages, 6536 KB  
Article
Development of a Tractor Hydrostatic Transmission Efficiency Prediction Model Using Novel Hybrid Deep Kernel Learning and Residual Radial Basis Function Interpolator Model
by Jin Kam Park, Oleksandr Yuhai, Jin Woong Lee, Yubin Cho and Joung Hwan Mun
Agriculture 2025, 15(22), 2325; https://doi.org/10.3390/agriculture15222325 - 8 Nov 2025
Viewed by 1228
Abstract
This study proposes a data-efficient surrogate modeling approach for predicting hydrostatic transmission (HST) system efficiency in tractors using minimal data. Only 27 samples were selected from a dataset of 5092 measurements based on the minimum, mean, and maximum values of the input variables [...] Read more.
This study proposes a data-efficient surrogate modeling approach for predicting hydrostatic transmission (HST) system efficiency in tractors using minimal data. Only 27 samples were selected from a dataset of 5092 measurements based on the minimum, mean, and maximum values of the input variables (input shaft speed, HST ratio, and load), which were used as the training data. A hybrid prediction model combining deep kernel learning and a residual radial basis function surrogate was developed with hyperparameters optimized via Bayesian optimization. For performance verification, the proposed model was compared with Neural Network (NN), Random Forest, XGBoost, Gaussian Process (GP), and Support Vector Regressor (SVR) models trained using 27 samples. As a result, the proposed model achieved the highest prediction accuracy (R2 = 0.93, MAPE = 5.94%, RMSE = 4.05). Process, SVM (Support Vector MA). These findings indicate that the proposed approach can be effectively used to predict the overall HST efficiency using minimal data, particularly in situations where experimental data collection is limited. Full article
(This article belongs to the Special Issue Computers and IT Solutions for Agriculture and Their Application)
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32 pages, 3417 KB  
Article
The Innovation Lifecycle of AI-Driven Agriculture: Causal Dynamics in University–Industry–Research Collaboration
by Giani Gradinaru, Madalina Zurini, Bogdan Florin Matei and Ioana-Diana Petre
Agriculture 2025, 15(21), 2298; https://doi.org/10.3390/agriculture15212298 - 4 Nov 2025
Viewed by 1978
Abstract
Considering the current prediction from the Food and Agriculture Organization, food production needs an increase of over 70 percent by 2050, and the agriculture sector requires a boost that is also obtained by integrating novelty technologies and methods of the artificial intelligence (AI) [...] Read more.
Considering the current prediction from the Food and Agriculture Organization, food production needs an increase of over 70 percent by 2050, and the agriculture sector requires a boost that is also obtained by integrating novelty technologies and methods of the artificial intelligence (AI) field. This study investigates the innovation lifecycle of AI-driven agriculture through a causal inference analysis within the university–industry–research (UIR) collaboration framework. Using time series data from 1985 to 2023, collected from Web of Science (academic articles), CORDIS (research projects), WIPO (patents), and Crunchbase (start-ups), the study explores the causal dynamics among four key innovation pillars. Results from Granger causality and impulse-response analyses reveal a sequential innovation pathway in which start-up activity precedes research projects, followed by academic publications and patent filings. These findings highlight the interdependence of innovation stages and the evolving role of UIR collaboration in driving agricultural transformation. The study provides quantitative insights for policymakers, investors, and researchers to strategically foster innovation ecosystems in AI-driven agriculture. Full article
(This article belongs to the Special Issue Computers and IT Solutions for Agriculture and Their Application)
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30 pages, 10140 KB  
Article
High-Accuracy Cotton Field Mapping and Spatiotemporal Evolution Analysis of Continuous Cropping Using Multi-Source Remote Sensing Feature Fusion and Advanced Deep Learning
by Xiao Zhang, Zenglu Liu, Xuan Li, Hao Bao, Nannan Zhang and Tiecheng Bai
Agriculture 2025, 15(17), 1814; https://doi.org/10.3390/agriculture15171814 - 25 Aug 2025
Viewed by 1535
Abstract
Cotton is a globally strategic crop that plays a crucial role in sustaining national economies and livelihoods. To address the challenges of accurate cotton field extraction in the complex planting environments of Xinjiang’s Alaer reclamation area, a cotton field identification model was developed [...] Read more.
Cotton is a globally strategic crop that plays a crucial role in sustaining national economies and livelihoods. To address the challenges of accurate cotton field extraction in the complex planting environments of Xinjiang’s Alaer reclamation area, a cotton field identification model was developed that integrates multi-source satellite remote sensing data with machine learning methods. Using imagery from Sentinel-2, GF-1, and Landsat 8, we performed feature fusion using principal component, Gram–Schmidt (GS), and neural network techniques. Analyses of spectral, vegetation, and texture features revealed that the GS-fused blue bands of Sentinel-2 and Landsat 8 exhibited optimal performance, with a mean value of 16,725, a standard deviation of 2290, and an information entropy of 8.55. These metrics improved by 10,529, 168, and 0.28, respectively, compared with the original Landsat 8 data. In comparative classification experiments, the endmember-based random forest classifier (RFC) achieved the best traditional classification performance, with a kappa value of 0.963 and an overall accuracy (OA) of 97.22% based on 250 samples, resulting in a cotton-field extraction error of 38.58 km2. By enhancing the deep learning model, we proposed a U-Net architecture that incorporated a Convolutional Block Attention Module and Atrous Spatial Pyramid Pooling. Using the GS-fused blue band data, the model achieved significantly improved accuracy, with a kappa coefficient of 0.988 and an OA of 98.56%. This advancement reduced the area estimation error to 25.42 km2, representing a 34.1% decrease compared with that of the RFC. Based on the optimal model, we constructed a digital map of continuous cotton cropping from 2021 to 2023, which revealed a consistent decline in cotton acreage within the reclaimed areas. This finding underscores the effectiveness of crop rotation policies in mitigating the adverse effects of large-scale monoculture practices. This study confirms that the synergistic integration of multi-source satellite feature fusion and deep learning significantly improves crop identification accuracy, providing reliable technical support for agricultural policy formulation and sustainable farmland management. Full article
(This article belongs to the Special Issue Computers and IT Solutions for Agriculture and Their Application)
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30 pages, 5355 KB  
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
Cited by 6 | Viewed by 1939
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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Other

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46 pages, 1545 KB  
Systematic Review
Overview of Artificial Intelligence Applications in Roselle (Hibiscus sabdariffa) from Cultivation to Post-Harvest: Challenges and Opportunities
by Alfonso Ramírez-Pedraza, Juan Terven, José-Joel González-Barbosa, Juan-Bautista Hurtado-Ramos, Diana-Margarita Córdova-Esparza, Francisco-Javier Ornelas-Rodríguez, Raymundo Ramirez-Pedraza, Julio-Alejandro Romero-González and Sebastián Salazar-Colores
Agriculture 2025, 15(16), 1758; https://doi.org/10.3390/agriculture15161758 - 16 Aug 2025
Viewed by 2608
Abstract
Hibiscus sabdariffa (H. sabdariffa) is a high-value economic and functional crop, limited by agroclimatic conditions and low technological adoption. This systematic review examines the current state of artificial intelligence applications in agricultural management, analyzing 2111 records, selecting 82, and synthesizing 22 studies that [...] Read more.
Hibiscus sabdariffa (H. sabdariffa) is a high-value economic and functional crop, limited by agroclimatic conditions and low technological adoption. This systematic review examines the current state of artificial intelligence applications in agricultural management, analyzing 2111 records, selecting 82, and synthesizing 22 studies that meet the inclusion criteria. This review adopts a holistic framework aligned with three priority areas in agriculture—resource and climate management, crop productivity and quality, and sustainability—to explore how AI addresses key challenges in the cultivation and post-harvest processing of Hibiscus sabdariffa. The results show a predominance of classical machine learning techniques, with limited implementation of deep learning models. The most common applications include image classification, yield prediction, and analysis of bioactive compounds. However, limitations remain in the availability of open data, reproducible code, and standardized metrics. The narrative synthesis identified clear opportunities to integrate emerging technologies, such as deep neural networks and the Internet of Things (IoT), particularly in water management and stress monitoring. The review concludes that strengthening interdisciplinary research and promoting data openness is key to achieving a more resilient, sustainable, and technologically advanced crop. Full article
(This article belongs to the Special Issue Computers and IT Solutions for Agriculture and Their Application)
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