Early betel plant disease detection using convolutional neural network for IOT applications.

The development of image processing technology will detect the disease of the betel leaves in the plant. The realizing the image analysis and classification technology. Specifically, there are several innovations in image segmentation and recognition systems for plant disease detection. In this way, Convolutional Neural Network (CNN) has proposed detecting the features, for preprocessing and advanced processing correction task is usually performed by the Image Signal Processor (ISP). An automated disease detection system is based on the development of changes in the disease status of the betel plant's leaves using the Internet of Things (IoT). For Convolutional Neural Network (CNN), it uses a complex feed-forward neural network, and a CNN has high accuracy in image classification and recognition. Simultaneously, image processing is combined to improve the system's accuracy and intelligence, and the recognition system will use to create multiple images in feature extraction. After evaluating the results of different image training library systems, practical image recognition function has been demonstrated to have high precision and strong reliability. [ABSTRACT FROM AUTHOR]