Crop disease monitoring and recognizing system by soft computing and image processing models

摘要: In order to make the crop disease intelligent diagnosis system more cheap, convenient and efficient for common farmers, an effective general monitoring of diseases is constructed by Internet Things (IoT). system, images are collected IoT passed Web server wireless network. First, a image dataset constructed. Second, K-mean clustering algorithm utilized segment leaf images, sum difference histogram (SADH) feature vector extracted from each segmented defect based on intensity values neighboring pixels. Finally, reasoning process built decision tree system. The results validates that proposed benefit controlling in practice.