Detection and classification of various pest attacks and infection on plants using RBPN with GA based PSO algorithm - Kapilya Gangadharan; G. Rosline Nesa Kumari; D. Dhanasekaran; K. Malathi (Indonesian Journal of Electrical Engineering and Computer Science, 2020)
Machine learning methodologies are commonly used in the field of precession farming. It prospects greatly in the plant safety measure like disease detection and classification of pest attacks. It highly influences the crop production and management. The venture of our system is to produce healthy plantation. The proposed system involves enhanced feature fractal texture analysis, Statistical feature selection and machine learning methodology for classification. Hence more than ever there is a need for such a tool that combines image processing methodologies and the neural network concepts and that is supported by huge cloud of structured data which makes the diagnosis and classification part much easier and convenient. The proposed system recognizes and classifies the plant taxonomy and the infection based on the selected statistical features. The neural network concept followed in our proposed system is focused on artificial neural network which uses recursive back propagation neural network to speed up the training process as well as reduce multiclass problem in the network and optimize the weights on hidden layers of the Network using Genetic algorithm based particle swarm optimization technique. We have used MATLAB to implement the concept and obtained better accuracy in disease/pest detection and proved to be an efficient method.