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AgriEngineering
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The Future of Artificial Intelligence in Agriculture, 2nd Edition
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The Future of Artificial Intelligence in Agriculture, 2nd Edition

A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Computer Applications and Artificial Intelligence in Agriculture".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1841

Special Issue Editors

Prof. Dr. Sotirios K. Goudos

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Guest Editor
Department of Physics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: antenna design; microwave components design; wireless communications; evolutionary algorithms; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shaohua Wan

E-Mail Website
Guest Editor
Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518110, China
Interests: deep learning; Internet of Things; edge computing
Special Issues, Collections and Topics in MDPI journals
Dr. Achilles Boursianis

E-Mail Website
Guest Editor
ELEDIA@AUTH, Department of Physics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: wireless sensor networks; Internet of Things; broadband communications; non-ionizing radiation protection; broadband fiber-optic networks; antenna design and optimization; 5G communication networks; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The world of agriculture is undergoing a profound transformation, which many believe will be as impactful as previous agricultural revolutions. In fact, the ability to more precisely monitor crops and control irrigation, fertilization, and treatments at a much finer granularity than before is enabling the use of land previously not considered for agricultural purposes, optimizing the use of resources and maximizing the health of plants and the yield of crops. Utilizing information retrieval and processing technologies such as blockchain, IoT, machine learning, deep learning, cloud computing, and edge computing is advantageous. Applications of computer vision, machine learning, and IoT will increase the production, quality, and ultimately the profitability of farmers and related industries. Precision learning in the field of agriculture is crucial for increasing the overall harvest yield.

Artificial intelligence techniques such as deep learning as well as optimization techniques using evolutionary algorithms broaden and extend their application scope. Their application to the agriculture domain is constantly growing. Artificial intelligence is currently assisting farmers in minimizing crop losses by providing rich crop-related recommendations and insights.

This Special Issue aims to publish extended versions of papers in the area of artificial intelligence. Potential topics include but are not limited to the following:

deep learning; harvesting; machine learning; smart agriculture; evolutionary algorithms; optimization techniques; IoT; edge intelligence; computer vision

Prof. Dr. Sotirios K. Goudos
Prof. Dr. Shaohua Wan
Dr. Achilles Boursianis
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 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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AgriEngineering 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 1800 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

  • deep learning
  • harvesting
  • machine learning
  • smart agriculture
  • evolutionary algorithms
  • optimization techniques
  • IoT
  • edge intelligence
  • computer vision

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Related Special Issue

Published Papers (3 papers)

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Research

20 pages, 1809 KB  
Article
Comparative Evaluation of Deep Learning Architectures for Non-Destructive Estimation of Carotenoid Content from Visible–Near-Infrared (400–850 nm) Spectral Reflectance Data
by Yuta Tsuchiya, Yuhei Hirono and Rei Sonobe
AgriEngineering 2026, 8(1), 36; https://doi.org/10.3390/agriengineering8010036 - 19 Jan 2026
Viewed by 236
Abstract
This study compared three deep learning architectures—one-dimensional convolutional neural network (1D-CNN), self-supervised learning (SSL), and Vision Transformer (ViT)—to evaluate their ability to predict carotenoid content from visible–near-infrared (VIS–NIR) spectral reflectance data (400–850 nm) acquired non-destructively from tea leaves. Model performance was evaluated using [...] Read more.
This study compared three deep learning architectures—one-dimensional convolutional neural network (1D-CNN), self-supervised learning (SSL), and Vision Transformer (ViT)—to evaluate their ability to predict carotenoid content from visible–near-infrared (VIS–NIR) spectral reflectance data (400–850 nm) acquired non-destructively from tea leaves. Model performance was evaluated using 10-fold cross-validation and analyzed through the mean SHapley Additive exPlanations values to identify key spectral features. The ViT model achieved the highest predictive accuracy (coefficient of determination [R2] = 0.81, root mean square error [RMSE] = 1.04, ratio of performance to deviation [RPD] = 2.32), followed by 1D-CNN (R2 = 0.75, RMSE = 1.21, RPD = 1.99), whereas SSL showed substantially lower predictive performance (R2 = 0.30, RMSE = 2.01, RPD = 1.20). Feature importance analysis revealed that ViT focused strongly on the red-edge region around 720 nm, which corresponds to spectral features associated with carotenoids and chlorophyll. The 1D-CNN relied mainly on blue (450–480 nm) and red (670–700 nm) regions, while SSL exhibited a broadly distributed importance pattern across wavelengths. These results indicate that ViT’s self-attention mechanism captures long-range spectral dependencies more effectively than conventional convolutional or self-supervised models. Overall, the study demonstrates that transformer-based architectures provide a powerful and interpretable framework for non-destructive estimation of carotenoid content from VIS–NIR reflectance spectroscopy. Full article
(This article belongs to the Special Issue The Future of Artificial Intelligence in Agriculture, 2nd Edition)
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22 pages, 4384 KB  
Article
Development and Validation of an Image Dataset for Automatic Recognition of the Olive Fruit Fly (Bactrocera oleae) Using Machine Learning
by Flora Moreno-Alcaide, Meelad Yousef-Yousef, Juan Manuel Díaz-Cabrera, Luis Miguel Cámara-Díaz, Enrique Quesada-Moraga and José Cristóbal Ramírez-Faz
AgriEngineering 2025, 7(12), 422; https://doi.org/10.3390/agriengineering7120422 - 8 Dec 2025
Cited by 1 | Viewed by 525
Abstract
The olive fruit fly Bactrocera oleae (Rossi) (Diptera: Tephritidae) is the primary pest of olive crop globally, causing serious economic losses each year. Early and accurate detection of this pest is essential for implementing integrated management strategies and minimizing the use of chemical [...] Read more.
The olive fruit fly Bactrocera oleae (Rossi) (Diptera: Tephritidae) is the primary pest of olive crop globally, causing serious economic losses each year. Early and accurate detection of this pest is essential for implementing integrated management strategies and minimizing the use of chemical inputs. In this context, the application of advanced technologies such as computer vision and machine learning through modelling emerges as a promising solution for monitoring and managing this pest. However, the absence of a robust and efficient dataset has hindered the development of reliable models for its recognition. This study details the creation procedure of a dataset comprising 2440 images collected from field and laboratory environments, along with data augmentation and training of three different models using machine learning algorithms. The models were implemented with YOLOv5 and optimized with different versions (s, m, and epoch). All three models achieved accuracy exceeding 90%. The optimisation process, which combined different YOLOv5 versions (s and m) and epochs (300 and 150), determined that the model trained with the s version and 300 epoch provided the best trade-off between accuracy, robustness, and computational efficiency. This makes it the most suitable option for implementation on low-cost, resource-limited platforms such as the Raspberry Pi. This study represents a step toward the integration of artificial intelligence into olive cultivation, bringing significant benefits to both producers and the environment. This study differs from previous YOLOv5-based pest detection research by providing a heterogeneous dataset that combines field and laboratory conditions, and by validating its deployment on a low-cost embedded platform (Raspberry Pi), thus enabling practical automation in Integrated Pest Management (IPM) systems. Full article
(This article belongs to the Special Issue The Future of Artificial Intelligence in Agriculture, 2nd Edition)
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23 pages, 1747 KB  
Article
Machine Learning-Based Prediction of Soybean Plant Height from Agronomic Traits Across Sequential Harvests
by Bruno Rodrigues de Oliveira, Renato Lustosa Sobrinho, Fernando Rodrigues Trindade Ferreira, Fernando Ferrari Putti, Matteo Bodini, Camila Martins Saporetti and Leonardo Goliatt
AgriEngineering 2025, 7(12), 408; https://doi.org/10.3390/agriengineering7120408 - 2 Dec 2025
Viewed by 674
Abstract
The accurate prediction of plant height is crucial for optimizing soybean cultivar selection and improving yield estimations. In this study, we investigate the potential of machine learning (ML) algorithms to predict soybean plant height (PH) based on a diverse set of agronomic parameters [...] Read more.
The accurate prediction of plant height is crucial for optimizing soybean cultivar selection and improving yield estimations. In this study, we investigate the potential of machine learning (ML) algorithms to predict soybean plant height (PH) based on a diverse set of agronomic parameters analyzed from forty soybean cultivars evaluated across sequential harvests. Using a comprehensive dataset, the models Elastic Net (EN), Extra Trees (ET), Gaussian Process Regressor (GPR), K-Nearest Neighbors, and XGBoost (XGB) were compared in terms of predictive accuracy, uncertainty, and robustness. Our results demonstrate that ET outperformed other models with an average correlation coefficient of 0.674, R2 of 0.426 and the lowest RMSE of 6.859 cm and MAE of 5.361 cm, while also showing the lowest uncertainty (5.07%). The proposed ML framework includes an extensive model evaluation pipeline that incorporates the Performance Index (PI), ANOVA, and feature importance analysis, providing a multidimensional perspective on model behavior. The most influential features for PH prediction were the number of stems (NS) and insertion of the first pod (IFP). This research highlights the viability of integrating explainable ML techniques into agricultural decision support systems, enabling data-driven strategies for cultivar evaluation and phenotypic trait forecasting. Full article
(This article belongs to the Special Issue The Future of Artificial Intelligence in Agriculture, 2nd Edition)
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