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AgriEngineering
Special Issues
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 1058

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 (3 papers)

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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 (registering DOI) - 22 Apr 2025
Viewed by 167
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 276
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 362
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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