Deep Learning in Agriculture: Enhancing Informatics for Precision Farming

Dear Colleagues,

Recent advances in deep learning and data-driven modeling have opened new opportunities for transforming modern agriculture into a highly efficient, autonomous and adaptive system. This Special Issue aims to explore emerging artificial-intelligence techniques that enhance precision farming, resource optimization and agricultural task automation. While the existing literature has demonstrated the value of deep learning across specific tasks, there remains a need for broader experimental studies that evaluate a wide range of AI models—including lightweight CNNs, transformer-based vision models, large language models (LLMs) and multimodal architectures—within realistic agricultural environments. 

The focus of this Special Issue is the practical and experimental applications of deep learning for agricultural informatics. We welcome studies that explore a wide range of AI-driven approaches for advancing plant and crop informatics. We are also interested in research on data-driven management and intelligent control strategies for protected or open-field agriculture. Submissions that evaluate the feasibility, limitations and potential of emerging models including generative AI and large-scale pretrained models are highly encouraged. 

The purpose of this Special Issue is to provide a platform for researchers to test, adapt and refine diverse AI methodologies for agriculture, even at a preliminary or experimental stage. Unlike traditional engineering approaches that prioritize highly optimized systems, this issue welcomes exploratory work that examines alternative architectures, prototype implementations, cross-domain model transfers and hybrid strategies combining AI with conventional agronomic knowledge. By doing so, the Special Issue will supplement the existing literature with new insights into model behavior, data requirements, generalizability and applicability across different agricultural contexts. 

We hope this Special Issue will stimulate interdisciplinary collaboration among researchers in precision agriculture, computer vision, robotics, plant science and AI engineering, ultimately contributing to sustainable and intelligent farming systems for the future.

Dr. Min-Seok Gang
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. 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.