Advances in Real-Time Computer Vision and Artificial Intelligence for Crop Management

Dear Colleagues,

As the global population grows and climate change increasingly impacts arable land, maximizing crop yield and resource efficiency has become a critical global challenge. Traditional crop management relies heavily on manual scouting, a labor-intensive, time-consuming process that is prone to human error. However, the rapid evolution of real-time computer vision and artificial intelligence (AI) is revolutionizing precision agriculture. By enabling rapid, non-destructive, and accurate monitoring of crop phenotyping and field conditions, these technologies provide unprecedented opportunities to optimize planting densities, monitor growth stages, and manage complex agricultural environments dynamically.

The urgency of this research lies in the demand for intelligent agricultural machinery and edge-computing devices that can process field data instantaneously. The integration of advanced deep learning algorithms and multi-source sensor fusion empowers autonomous systems to make real-time, adaptive decisions directly in the field. This Special Issue of AgriEngineering is vital for bridging the gap between theoretical AI models and practical, deployable agricultural engineering solutions, driving the next generation of smart farming.

Despite these significant advancements, the field continues to face several core technical challenges. Deploying AI models in real-world agricultural settings requires overcoming issues related to varying illumination, dynamic weather conditions, and background clutter, which often degrade algorithm robustness. Additionally, balancing high computational demands with the limited processing power and energy constraints of agricultural edge devices remains a persistent hurdle. Furthermore, seamlessly fusing multi-modal data from heterogeneous sensors in real time presents complex calibration and synchronization difficulties that must be addressed to ensure reliable autonomous operation.

This Special Issue aims to collate and disseminate the latest research findings related to real-time computer vision and artificial intelligence in crop management. We welcome the submission of papers covering innovative algorithms, embedded systems, and vision-guided machinery designed to enhance crop monitoring and agricultural automation.

Suitable topics include, but are not limited to, the following:

• Lightweight deep learning architectures (e.g., YOLO variants, multi-modal fusion networks) for real-time crop detection and phenotyping;
• Multi-source sensor integration and fusion for 3D environment perception and plant population mapping;
• Vision-guided autonomous agricultural machinery and adaptive posture control systems;
• Edge computing and embedded hardware implementations for real-time agricultural AI;
• AI-driven early detection of plant diseases, pests, and physiological stress.

This Special Issue brings together researchers, practitioners, and industry experts to explore how these technologies are shaping the future of sustainable agriculture. The articles will not only deepen our understanding of these technologies but also provide practical recommendations for farmers, policymakers, and stakeholders dedicated to implementing sustainable agricultural practices.

We encourage the submission of manuscripts that demonstrate interdisciplinary approaches, practical applications, and the potential scalability of the proposed technologies. Submissions from fields such as agricultural sciences, engineering, data science, and environmental studies are particularly welcome.

We look forward to receiving your contributions.

Dr. Yangfan Luo
Dr. Yuanhong Li
Dr. Weicheng Xu
Dr. Tianci Chen
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-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.