Improved Segmentation With Optimization Based Multilevel Thresholding and K-Means Clustering for Plant Disease Identification

Abstract Plant disease identification is an important application for plant protection in agriculture production. The early detection of crop disease helps to reduce the effect of disease in cultivation. The detection of disease should be done precisely. Hence the hyperspectral sensors are extensively used in plant disease detection. Artificial intelligence and machine learning-based techniques have been presented in many works for plant disease detection. Deep learning is the latest method used in image processing and pattern recognition with improved accuracy. For plant disease detection, accurate classification of disease can be obtained with the utilization of deep learning techniques. In this paper, adaptive extreme learning machine (AELT) is presented for classifying the disease. Before the classification process, the segmentation and feature extraction process is performed to improve the disease detection accuracy. Multilevel thresholding-based K-means clustering with probability-induced butterfly optimization algorithm is presented for segmentation. The entropy-based features are extracted from plant images. The features are applied to the AELT classifier. The results are evaluated with the standard dataset and compared with the state of art techniques.