Agricultural Imagery and Machine Vision

Dear Colleagues,

In the present era, global agriculture stands at a pivotal juncture of accelerated transition from conventional methodologies to intelligent, precision-driven systems. Agricultural imagery and machine vision technologies have emerged as indispensable linchpins, powerfully steering the course of smart agriculture's development. From the real-time monitoring of crop growth trends in the fields, through the early and precise warning systems for pests and diseases and the autonomous operation of agricultural robots, to the intelligent management of agricultural resources, the application of these technologies is reshaping the production models and decision-making systems of traditional agriculture. However, as research continues to deepen and application scenarios expand, this field is encountering a variety of issues, such as fragmented research outcomes, insufficient interdisciplinary communication, and a pressing need for breakthroughs in key technologies. In practical applications, issues such as the insufficient accuracy of image recognition in complex field environments, the need to expand the depth and breadth of multi-source data fusion, and the excessively high cost of implementing the technology on the ground severely limit the large-scale promotion and application of these technologies.

Within this Special Issue, research areas encompass crop phenotyping analysis using imagery and machine vision; pest, disease, and weed identification through image recognition and machine learning; yield prediction with machine learning models; robotics and automation systems for agricultural tasks; multi-source data fusion to improve decision-making; and novel imaging technologies and sensors, including drone imaging, satellite remote sensing, and LiDAR for agricultural data acquisition.

Dr. Ximing Li
Prof. Dr. Xuejun Yue
Guest Editors

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