Paddy leaf diseases recognition and classification using PCA and BFO-DNN algorithm by image processing

Abstract

Currently, agriculture has become the most significant approach than it has been used before a few years back, when the plants are used for feeding the human and flora and fauna. Presently, the plants have been used to create the electricity and other type of the power to enhance the living situations of the social beings. Due to this, it is required for the suitable care of the plants to acquire the maximum advantage The main region that required main consideration is the cutting plant leaves diseases. A number of the diseases affect the leaves that may cause maximum destruction to different economic and social aspect. It may also cause high environmental losses. In the proposed system, study the different leaf infections using detection and classification technique in image processing. Initially, various paddy leaves acquire using digital pictures. After that, RGB model converted into the HSV model for resizing the picture using k mean clustering with image segmentation. Then, the specific features are extracted using the PCA algorithm. Moreover, the feature extraction and BFO-DNN method implemented for classification of the paddy leaf diseases. This classification method is used to improve the detection rate and reduce the entropy loss. It is highly efficient and accurate to detect or recognize the disease image with different number of categories (Bacteria light, sheath rot, Brown spot and Normal etc.). Experimental analysis is done to calculate the performance metric like as accuracy, TPR, TNR,FDR, Cross Entropy and FPR. Then, the comparative analysis of the existing parameters compared to the current parameters. The proposed system performance value of accuracy is 98% with hybrid BFOA-DNN, accuracy, value is 97 and DNN 93.50 percentage . The research system performance value of Cross Entropy Loss is 0.0011 with hybrid BFOA-DNN, Entropy Loss value is 0.0100 and DNN 0.01700 per cent.