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AgriEngineering
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Transforming Agriculture with Artificial Intelligence: Recent Advances and Applications
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Transforming Agriculture with Artificial Intelligence: Recent Advances and Applications

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 May 2026 | Viewed by 4428

Special Issue Editors

Dr. Thuseethan Selvarajah

E-Mail Website1 Website2
Guest Editor
Faculty of Science and Technology, Charles Darwin University, Casuarina, NT 0810, Australia
Interests: machine learning; deep learning; computer vision; emotion recognition; medical imaging; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Yakub Sebastian

E-Mail Website
Guest Editor
Faculty of Science and Technology, Charles Darwin University, Casuarina, NT 0810, Australia
Interests: data science; information retrieval; text mining; network theory

Special Issue Information

Dear Colleagues,

The agricultural sector is experiencing a paradigm shift with the integration of artificial intelligence (AI). By leveraging advanced AI techniques, agriculture is moving towards precision, sustainability, and enhanced productivity. Traditional farming practices are being revolutionized through AI-driven solutions that offer real-time decision-making, resource optimization, and predictive analytics. From crop monitoring and disease detection to weather prediction and supply chain management, AI is proving to be a game-changer in addressing some of the critical challenges in agriculture.Recent advancements, such as AI-powered robotics, sensor-driven data collection, and machine learning models, are fostering smart farming systems capable of transforming productivity and efficiency. Generative AI and multimodal approaches are further enhancing capabilities in areas such as crop simulation, resource allocation, and intelligent irrigation systems. However, the adoption of AI in agriculture also presents challenges, including data scarcity, ethical concerns, and ensuring accessibility to smallholder farmers. 

This Special Issue aims to explore the transformative potential of AI in agriculture by presenting recent advancements, novel methodologies, and real-world applications. Contributions that address the challenges of implementing AI in diverse agricultural contexts are encouraged, along with studies on emerging trends and benchmarking analyses. Topics of interest for this Special Issue include, but are not limited to, the following:·       

AI-driven precision agriculture;

Machine learning for crop yield prediction and disease detection;

Robotics and automation in farming;

Generative AI applications in agriculture;

Multi-modal AI for farm management;

Edge AI for real-time agricultural analytics;

Climate-resilient agriculture using AI;

AI-based supply chain optimization and logistics;

Federated learning and privacy in agricultural data;

Ethical considerations and responsible AI in agriculture;

AI applications in soil health, water management, and pest control;

Case studies of AI-driven solutions in smallholder and large-scale farming.

Dr. Thuseethan Selvarajah
Dr. Yakub Sebastian
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. 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 1600 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

  • AgTech
  • AI
  • precision agriculture
  • generative AI
  • agricultural analytics

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Published Papers (8 papers)

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Research

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22 pages, 12791 KiB  
Article
ViT-RoT: Vision Transformer-Based Robust Framework for Tomato Leaf Disease Recognition
by Sathiyamohan Nishankar, Velalagan Pavindran, Thurairatnam Mithuran, Sivaraj Nimishan, Selvarajah Thuseethan and Yakub Sebastian
AgriEngineering 2025, 7(6), 185; https://doi.org/10.3390/agriengineering7060185 - 10 Jun 2025
Abstract
Vision transformers (ViTs) have recently gained traction in plant disease classification due to their strong performance in visual recognition tasks. However, their application to tomato leaf disease recognition remains challenged by two factors, namely the need for models that can generalise across diverse [...] Read more.
Vision transformers (ViTs) have recently gained traction in plant disease classification due to their strong performance in visual recognition tasks. However, their application to tomato leaf disease recognition remains challenged by two factors, namely the need for models that can generalise across diverse disease conditions and the absence of a unified framework for systematic comparison. Existing ViT-based approaches often yield inconsistent results across datasets and disease types, limiting their reliability and practical deployment. To address these limitations, this study proposes the ViT-Based Robust Framework (ViT-RoT), a novel benchmarking framework designed to systematically evaluate the performance of various ViT architectures in tomato leaf disease classification. The framework facilitates the systematic classification of state-of-the-art ViT variants into high-, moderate-, and low-performing groups for tomato leaf disease recognition. A thorough empirical analysis is conducted using one publicly available benchmark dataset, assessed through standard evaluation metrics. Results demonstrate that the ConvNeXt-Small and Swin-Small models consistently achieve superior accuracy and robustness across all datasets. Beyond identifying the most effective ViT variant, the study highlights critical considerations for designing ViT-based models that are not only accurate but also efficient and adaptable to real-world agricultural applications. This study contributes a structured foundation for future research and development in vision-based plant disease diagnosis. Full article
(This article belongs to the Special Issue Transforming Agriculture with Artificial Intelligence: Recent Advances and Applications)
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13 pages, 302 KiB  
Article
Utilizing TabPFN Transformer with IoT Environmental Data for Early Prediction of Grapevine Diseases
by Nikolaos Arvanitis, Filippo Graziosi, Gina Athanasiou, Antonia Terpou, Olga Arvaniti and Theodore Zahariadis
AgriEngineering 2025, 7(6), 173; https://doi.org/10.3390/agriengineering7060173 - 3 Jun 2025
Viewed by 274
Abstract
Downy mildew and powdery mildew are among the most serious diseases that affect grapevine. They can cause severe damage, such as yield loss, and affect the size of the grapes and their ability to accumulate sugars, affecting the flavor and aroma negatively and [...] Read more.
Downy mildew and powdery mildew are among the most serious diseases that affect grapevine. They can cause severe damage, such as yield loss, and affect the size of the grapes and their ability to accumulate sugars, affecting the flavor and aroma negatively and increasing the need for fungicidal sprays to combat these diseases and the pathogens that cause them. Clearly, it is important to predict these diseases early and apply treatment promptly to prevent and mitigate the effects of these diseases on crop production. This study presents a workflow in which IoT environmental sensors and machine learning methods are leveraged to accurately predict disease onset and allow for timely fungicide applications or other disease management strategies. We collected IoT grapevine field measurements and leveraged the records of the respective time periods during which fungicide treatments were applied to grapevine, and we used them to train and evaluate different ML tabular data classifiers as early predictors for each of the two diseases. The TabPFN transformer demonstrated superior performance in disease risk assessment while enabling real-time predictions with sub-second latencies, so it can be considered as a very good choice for a real-time grapevine disease prediction system. Full article
(This article belongs to the Special Issue Transforming Agriculture with Artificial Intelligence: Recent Advances and Applications)
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12 pages, 468 KiB  
Article
Predicting Pineapple Quality from Hyperspectral Data of Plant Parts Applied to Machine Learning
by Vitória Carolina Dantas Alves, Sebastião Ferreira de Lima, Dthenifer Cordeiro Santana, Rafael Ferreira Barreto, Roger Augusto da Cunha, Ana Carina da Silva Cândido Seron, Larissa Pereira Ribeiro Teodoro, Paulo Eduardo Teodoro, Rita de Cássia Félix Alvarez, Cid Naudi Silva Campos, Carlos Antonio da Silva Junior and Fábio Luíz Checchio Mingotte
AgriEngineering 2025, 7(6), 170; https://doi.org/10.3390/agriengineering7060170 - 3 Jun 2025
Viewed by 246
Abstract
Food quality detection by machine learning (ML) is more practical and sustainable as it does not require sample preparation and reagents. However, the prediction of pineapple quality by hyperspectral data applied to ML is not known. The aim of this study was to [...] Read more.
Food quality detection by machine learning (ML) is more practical and sustainable as it does not require sample preparation and reagents. However, the prediction of pineapple quality by hyperspectral data applied to ML is not known. The aim of this study was to verify accurate ML models for predicting pineapple fruit quality and the best inputs for algorithms: Artificial Neural Networks (ANNs), M5P (model tree), REPTree decision trees, Random Forest (RF), Support Vector Machine (SMV) and Zero R. Three inputs were used for each model: leaf reflectance, peel reflectance, and fruit reflectance. The machine learning model SVM, stood out for its best results, demonstrating good generalization capacity and effectiveness in predicting these attributes, reaching accuracy values above 0.7 for Brix and ratio, using fruit reflectance. In terms of the overall efficiency of the input variables, peel and fruit were the most informative, with peel standing out for the estimation of secondary metabolism compounds, while the fruit showed excellent performance in predicting flavor-related attributes, such as acidity, °Brix and ratio, as mentioned previously, above 0.7. These results highlight the potential of using spectral data and machine learning in the non-destructive assessment of pineapple quality, enabling advances in monitoring and selecting fruits with better sensors. Full article
(This article belongs to the Special Issue Transforming Agriculture with Artificial Intelligence: Recent Advances and Applications)
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13 pages, 2316 KiB  
Article
Artificial Intelligence in the Identification of Germinated Soybean Seeds
by Hiago H. R. Zanetoni, Lucas G. Araujo, Reynaldo P. Almeida and Carlos E. A. Cabral
AgriEngineering 2025, 7(6), 169; https://doi.org/10.3390/agriengineering7060169 - 2 Jun 2025
Viewed by 263
Abstract
This study resulted from the demand for seeds with physiological qualities and studies in germination tests applied for seed improvement aimed at productive and homogeneous harvests. The objective of this study was to improve the classification of seeds in germination tests by introducing [...] Read more.
This study resulted from the demand for seeds with physiological qualities and studies in germination tests applied for seed improvement aimed at productive and homogeneous harvests. The objective of this study was to improve the classification of seeds in germination tests by introducing YOLO as a classification tool for germinated or nongerminated seeds to specify the results and optimize the analysis period. Germination tests were performed for Glycine max (soybean) seeds, and the capture of images from the tests and conventional categorization was performed by uncorrelated individuals, for the processing of these images and application to YOLO. Subsequently, graphical analyses of the YOLO results and comparison metrics with conventional categorization were performed to determine the accuracy of YOLO as a seed categorization tool. The results derived from the analysis of the graphs and comparisons to the conventional methodology of seed classification showed the effectiveness of YOLO for classifying seeds as germinated or nongerminated, reaching 95% accuracy in seed classification, beyond the range of 0–0.110 of the prediction errors, determined by the application of the methodology of mean square error, highlighting the efficiency of YOLO. Full article
(This article belongs to the Special Issue Transforming Agriculture with Artificial Intelligence: Recent Advances and Applications)
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20 pages, 6149 KiB  
Article
Computer Vision and Transfer Learning for Grading of Egyptian Cotton Fibres
by Ahmed Rady, Oliver Fisher, Aly A. A. El-Banna, Haitham H. Emasih and Nicholas J. Watson
AgriEngineering 2025, 7(5), 127; https://doi.org/10.3390/agriengineering7050127 - 22 Apr 2025
Viewed by 522
Abstract
Egyptian cotton fibres have worldwide recognition due to their distinct quality and luxurious textile products known by the “Egyptian Cotton“ label. However, cotton fibre trading in Egypt still depends on human grading of cotton quality, which is resource-intensive and faces challenges in terms [...] Read more.
Egyptian cotton fibres have worldwide recognition due to their distinct quality and luxurious textile products known by the “Egyptian Cotton“ label. However, cotton fibre trading in Egypt still depends on human grading of cotton quality, which is resource-intensive and faces challenges in terms of subjectivity and expertise requirements. This study investigates colour vision and transfer learning to classify the grade of five long (Giza 86, Giza 90, and Giza 94) and extra-long (Giza 87 and Giza 96) staple cotton cultivars. Five Convolutional Neural networks (CNNs)—AlexNet, GoogleNet, SqueezeNet, VGG16, and VGG19—were fine-tuned, optimised, and tested on independent datasets. The highest classifications were 75.7%, 85.0%, 80.0%, 77.1%, and 90.0% for Giza 86, Giza 87, Giza 90, Giza 94, and Giza 96, respectively, with F1-Scores ranging from 51.9–100%, 66.7–100%, 42.9–100%, 40.0–100%, and 80.0–100%. Among the CNNs, AlexNet, GoogleNet, and VGG19 outperformed the others. Fused CNN models further improved classification accuracy by up to 7.2% for all cultivars except Giza 87. These results demonstrate the feasibility of developing a fast, low-cost, and low-skilled vision system that overcomes the inconsistencies and limitations of manual grading in the early stages of cotton fibre trading in Egypt. Full article
(This article belongs to the Special Issue Transforming Agriculture with Artificial Intelligence: Recent Advances and Applications)
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20 pages, 2268 KiB  
Article
Benchmarking Large Language Models in Evaluating Workforce Risk of Robotization: Insights from Agriculture
by Lefteris Benos, Vasso Marinoudi, Patrizia Busato, Dimitrios Kateris, Simon Pearson and Dionysis Bochtis
AgriEngineering 2025, 7(4), 102; https://doi.org/10.3390/agriengineering7040102 - 3 Apr 2025
Viewed by 547
Abstract
Understanding the impact of robotization on the workforce dynamics has become increasingly urgent. While expert assessments provide valuable insights, they are often time-consuming and resource-intensive. Large language models (LLMs) offer a scalable alternative; however, their accuracy and reliability in evaluating workforce robotization potential [...] Read more.
Understanding the impact of robotization on the workforce dynamics has become increasingly urgent. While expert assessments provide valuable insights, they are often time-consuming and resource-intensive. Large language models (LLMs) offer a scalable alternative; however, their accuracy and reliability in evaluating workforce robotization potential remain uncertain. This study systematically compares general-purpose LLM-generated assessments with expert evaluations to assess their effectiveness in the agricultural sector by considering human judgments as the ground truth. Using ChatGPT, Copilot, and Gemini, the LLMs followed a three-step evaluation process focusing on (a) task importance, (b) potential for task robotization, and (c) task attribute indexing of 15 agricultural occupations, mirroring the methodology used by human assessors. The findings indicate a significant tendency for LLMs to overestimate robotization potential, with most of the errors falling within the range of 0.229 ± 0.174. This can be attributed primarily to LLM reliance on grey literature and idealized technological scenarios, as well as their limited capacity, to account for the complexities of agricultural work. Future research should focus on integrating expert knowledge into LLM training and improving bias detection and mitigation in agricultural datasets, as well as expanding the range of LLMs studied to enhance assessment reliability. Full article
(This article belongs to the Special Issue Transforming Agriculture with Artificial Intelligence: Recent Advances and Applications)
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20 pages, 4839 KiB  
Article
Prediction of Foliar Nutrient Contents and Differentiation of Scion/Rootstock Combinations in Citrus via X-Ray Fluorescence Spectrometry
by Maíra Ferreira de Melo Rossi, Eduane José de Pádua, Renata Andrade Reis, Pedro Henrique Reis Vilela, Marco Aurélio Carbone Carneiro, Nilton Curi, Sérgio Henrique Godinho Silva and Ana Claudia Costa Baratti
AgriEngineering 2025, 7(3), 79; https://doi.org/10.3390/agriengineering7030079 - 14 Mar 2025
Viewed by 520
Abstract
Citriculture has worldwide importance, and monitoring the nutritional status of plants through leaf analysis is essential. Recently, proximal sensing has supported this process, although there is a lack of studies conducted specifically for citrus. The objective of this study was to evaluate the [...] Read more.
Citriculture has worldwide importance, and monitoring the nutritional status of plants through leaf analysis is essential. Recently, proximal sensing has supported this process, although there is a lack of studies conducted specifically for citrus. The objective of this study was to evaluate the application of portable X-ray fluorescence spectrometry (pXRF) combined with machine learning algorithms to predict the nutrient content (B, Ca, Cu, Fe, K, Mg, Mn, P, S, and Zn) of citrus leaves, using inductively coupled plasma optical emission spectrometry (ICP-OES) results as a reference. Additionally, the study aimed to differentiate 15 citrus scion/rootstock combinations via pXRF results and investigate the effect of the sample condition (fresh or dried leaves) on the accuracy of pXRF predictions. The samples were analyzed with pXRF both fresh and after drying and grinding. Subsequently, the samples underwent acid digestion and analysis via ICP-OES. Predictions using dried leaves yielded better results (R2 from 0.71 to 0.96) than those using fresh leaves (R2 from 0.35 to 0.87) for all analyzed elements. Predictions of scion/rootstock combinations were also more accurate with dry leaves (Overall accuracy = 0.64, kappa index = 0.62). The pXRF accurately predicted nutrient contents in citrus leaves and differentiated leaves from 15 scion/rootstock combinations. This can significantly reduce costs and time in the nutritional assessment of citrus crops. Full article
(This article belongs to the Special Issue Transforming Agriculture with Artificial Intelligence: Recent Advances and Applications)
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Review

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26 pages, 2217 KiB  
Review
Advancing Precision Agriculture Through Digital Twins and Smart Farming Technologies: A Review
by Muhammad Awais, Xiuquan Wang, Sajjad Hussain, Farhan Aziz and Muhammad Qasim Mahmood
AgriEngineering 2025, 7(5), 137; https://doi.org/10.3390/agriengineering7050137 - 6 May 2025
Viewed by 1336
Abstract
The agricultural sector is evolving with the adoption of smart farming technologies, where Digital Twins (DTs) offer new possibilities for real-time monitoring, simulation, and decision-making. While previous research has explored the Internet of Things (IoT), UAVs, machine learning (ML), and remote sensing (RS) [...] Read more.
The agricultural sector is evolving with the adoption of smart farming technologies, where Digital Twins (DTs) offer new possibilities for real-time monitoring, simulation, and decision-making. While previous research has explored the Internet of Things (IoT), UAVs, machine learning (ML), and remote sensing (RS) in enhancing agricultural efficiency, a systematic approach to integrating these technologies within a DTs ecosystem remains underdeveloped. This paper presents a systematic review of 167 studies published between 2018 and 2025. The objective of this study is to examine recent advancements in DTs-enabled precision agriculture and propose a comprehensive framework for designing, integrating, and optimizing DTs in smart farming. The study systematically examines the current state of DT adoption, identifies key barriers, and computational efficiency challenges, and provides a step-by-step methodology for DT implementation. The review sheds light on potential future research direction and implications for policy, with the aim to speed up the adoption of DTs-based farm management systems in their operational success and commercial viability through analysis of practical applications and future perspectives. This study presents an innovative strategy for integrating digital and physical systems into agriculture and is an important contribution to existing literature. Full article
(This article belongs to the Special Issue Transforming Agriculture with Artificial Intelligence: Recent Advances and Applications)
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