Computer Vision and Pattern Recognition for Advanced Smart Agriculture Solutions
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Smart Agriculture".
Deadline for manuscript submissions: 30 September 2025 | Viewed by 89
Special Issue Editors
Interests: computer vision; agricultural robotics; electro-mechanical systems; controlled environment agriculture
Special Issues, Collections and Topics in MDPI journals
Interests: computer vision; agricultural robotics; electro-mechanical systems; controlled environment agriculture
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The sustainability of food production is challenged by the growing population and food demands, with an estimate of a need for up to 70% production growth by 2050, though the arable land available for agriculture is decreasing. Agriculture is evolving into smart farming through innovations in artificial intelligence (AI), big data analytics, the Internet of Things (IoT), and automation/robotics, all aimed at enhancing crop productivity and quality, leading to more cost-effective and reliable food production systems. Computer vision systems are increasingly used for smart agriculture applications such as biotic and abiotic stress detection, crop growth and yield monitoring, targeted spraying and irrigation, precision nutrient management, and robotic operations. Advanced computational and data analytics techniques, such as deep learning, foundational models, image rendering, and 3D reconstruction, have significantly enhanced the robustness, reliability, and practical applications of computer vision technologies in all aspects of production agriculture. Therefore, this Special Issue aims to promote a deeper understanding of major conceptual and technical challenges and facilitate the spread of recent breakthroughs in computer vision for smart farming.
Dr. Azlan Zahid
Dr. Yaqoob Majeed
Guest Editors
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Keywords
- computer vision
- pattern recognition
- 3D reconstruction
- deep learning
- image rendering
- classification
- precision agriculture
- object detection
- crop sensing
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