Advanced Pattern Recognition and Image Processing Technology for Agricultural Engineering

Dear Colleagues,

There has been a growing interest in agricultural engineering regarding advanced pattern recognition and image processing technology in recent years. This technology finds application in various agricultural tasks such as fruit-picking robots, pest monitoring, environmental factor tracking for growth, management of agricultural planting, and enhancing seed quality breeding programs. However, the complexity of agricultural environments challenges effective pattern recognition and image processing. External factors often interfere, leading to misclassifications and errors in experimental outcomes. Moreover, despite the rapid advancement of artificial intelligence algorithms, applying pattern recognition and image processing in agriculture encounters several hurdles. These include product overlap, significant occlusion of detection targets, excessive detection of targets, and complications in image processing due to lighting and camera angles. These challenges impede the seamless integration of image-processing technology in complex agricultural settings. Nonetheless, pursuing advanced pattern recognition and image processing in agriculture remains a promising and compelling area of research. This Special Issue aims to present state-of-the-art research achievements and advances by world-class researchers contributing to the agricultural field in pattern recognition, image processing, environment perception, and sensor fusion. Review articles are also encouraged. The potential topics of this organized session include but are not limited to:

• advanced pattern recognition technology in agricultural engineering;
• image processing networks in agricultural applications;
• soil spectral data in agricultural engineering;
• multispectral image processing in agricultural engineering;
• satellite remote sensing technology in agriculture;
• neural networks applications in agriculture engineering;
• human–machine intelligent algorithm in agriculture;
• detection and location of agricultural robotics;
• near-infrared image processing in agricultural engineering;
• hyperspectral technology in crop monitoring;
• integrating perception, sensor fusion, and control in agricultural applications;
• fault detection and diagnosis in agricultural engineering.

Dr. Jiehao Li
Prof. Dr. Shan Zeng
Prof. Dr. Xiwen Luo
Prof. Dr. Chenguang Yang
Dr. Jochem Verrelst
Guest Editors

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