Computer Vision and Artificial Intelligence in Precision Agriculture
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".
Deadline for manuscript submissions: 20 July 2026
Special Issue Editor
Interests: computer vision in agriculture; artificial neural networks; image-based quality assessment of crops and food products; precision agriculture decision support systems; UAV and remote sensing for crop monitoring
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The ongoing digitalization of agricultural production and the rapid advancement of modern data acquisition technologies, including multispectral imaging, computer vision systems, environmental sensing platforms, and remote sensing, have opened new avenues for AI-driven analytics. In particular, computer vision and machine learning techniques have enabled the processing and interpretation of complex visual data, forming the basis for precise monitoring of crop status, soil conditions, and environmental variability. These methods play a crucial role in early disease detection, assessment of biotic and abiotic stress, optimization of agronomic practices, and yield prediction. Recent years have witnessed significant progress in deep learning architectures, multimodal data fusion, and autonomous field systems that rely on visual perception to perform operational tasks under diverse agricultural conditions. Simultaneously, data-driven decision-making models are becoming increasingly important in precision agriculture, integrating heterogeneous data sources to enhance production efficiency while minimizing environmental impact.
This Special Issue aims to present the latest research advances in computer vision and artificial intelligence applied to precision agriculture. We invite contributions that introduce innovative algorithms and advanced data-analysis frameworks, as well as experimental and implementation-oriented studies that demonstrate the practical potential of AI-based methods in modern agriculture. Submissions addressing methodological challenges, system-level integrations, or emerging applications that support sustainable, efficient, and technologically advanced food production systems are particularly welcome.
Prof. Dr. Krzysztof Koszela
Guest Editor
Manuscript Submission Information
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Keywords
- precision agriculture
- computer vision
- deep learning
- remote sensing
- crop monitoring
- multimodal data fusion
- plant disease detection
- AI-based decision support systems
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