Study on the Identification of Mildew Disease of Cuttings at the Base of Mulberry Cuttings by Aeroponics Rapid Propagation Based on a BP Neural Network

Accurate detection of cutting diseases in the process aeroponic rapid propagation is very important for improving rooting rate and survival cuttings. This paper proposes to use image processing, with a dataset growth mulberry cuttings backward (BP) neural network, identify mildew on roots branches propagation, before extracting texture color features. An intelligent control aeroponics system was designed ambient temperature humidity entire incubator according rate, thereby time aeroponics, as well rates. In order distinguish extracted features, they were classified identified using constructed BP network model. The results indicated that performance neutral showed lowest mean square error validation set after three rounds training; therefore, model third round chosen best Furthermore, training effect revealed had good stability could accurately root zone After MATLAB training, regression correlation coefficients R 0.98 fitting curve dataset, test set, 0.99 indicating prediction aligned actual results. It can be concluded research method described this excellent identifying health status during process, it able quickly affected by disease an accuracy 80%. provides technical reference factories nurseries.