Recognition of Disease in Leaves Using Genetic Algorithm and Neural Network Based Feature Selection

Abstract

Objectives : To suggest a suitable image recognition approach for the early recognition of leaf diseases using hybrid features with genetic algorithm and neural network feature selection technique to maximize the accuracy. Methods: Various image processing techniques are utilized to recognize disease in the leaf. In the pre-processing phase, CNN based de-noising is utilized to remove noise from the image. Next, disease part of the leaf is segmented by Pixel wise Classification approach with an optimization technique. Then, the colour and the segmented area of an image is used to remove out texture features. With GLCM and SIFT technique. Then the appropriate features are selected by Genetic Algorithm and Neural Network based feature selection. Extracted features are classified using an Ensemble classifier for recognizing disease in the leaf. For this experiment images are taken from the plant village data set. In this work, the disease caused by Alternaria solani and Pytopthara infestans pathogens is considered for recognizing the disease in the leaf. 1500 leaves are used. From each image, 1654 features are extracted. There is a 70% training and 30% test data split. Classification accuracy, precision and recall measures are considered for evaluating the proposed work's performance. Findings: The proposed work gives 97.7% classification accuracy, 97.3% precision and 97.5% recall measures with various visual feature descriptors and GANN feature selection. Novelty: The in-depth investigation compares the proposed descriptor GANN_SVM and GANN ES detection and classification technique to local and global SVMs. The suggested descriptor outperforms current approaches for diagnosing Alternaria solani and Pytopthara infestans leaves and the method can be applied to all plants infected leaves.