Imaging Technology for Detecting Crops and Agricultural Products—3rd Edition

Dear Colleagues,

Imaging applications for use in agriculture are rapidly improving at different scales and have the potential to be key elements of sustainable agricultural intensification systems. In particular, satellite and drone imagery provides solutions for monitoring field crops and their within-field variability regarding crop health status, weed detection, and yield monitoring. Low-altitude imagery and machine vision applications of agricultural products are having a clear impact on sorting and harvesting automation. Moreover, the current availability of multispectral and hyperspectral sensors and images, combined with several data processing and machine-learning techniques, can facilitate unprecedented ideas and applications in agriculture. Imaging applications are usually coupled with machine-learning algorithms as a means of developing classification and regression models. Deep learning is a relatively new machine-learning technique that has become increasingly important in different fields in the agri-food chain, especially with significant advancements in imaging acquisition hardware, as well as the computational power available from personal computers with high-capability GPUs and high-performance cloud-based computational servers. There is no doubt that imaging applications in agriculture will continue to lead to several promising solutions in the current digital agriculture revolution. More research efforts and application ideas are still needed to improve the quality of agricultural products and to support farmers’ decisions in light of the different field and crop conditions. This Special Issue aims to exchange knowledge, ideas, analytical techniques, applications, and experiments that use imagery solutions in the field of agricultural applications.

Dr. Ahmed Kayad
Dr. Ahmed Rady
Guest Editors

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• digital agriculture
• remote sensing
• weed detection
• drone
• RGB imaging
• thermal imagery
• object detection
• hyperspectral imaging
• machine learning
• deep learning
• Artificial intelligence
• Industry 4.0
• Agriculture 4.0
• IoT

• Imaging Technology for Detecting Crops and Agricultural Products-II in Agronomy (7 articles)