Leaf Disease Detection in Crops based on Single-Hidden Layer Feed-Forward Neural Network and Hierarchal Temporary Memory

Abstract

Insects, mites, and fungi are common causes in plant disease, which can significantly reduce yields if not addressed promptly. Farmers are losing money as a result of crop illnesses. As the average under cultivation increases, it becomes more of a burden for farmers to keep an eye on everything. In this study, the median filter is used as a preprocessing step to transform the input image into a grayscale representation which used YCbCr color space. Otsu's segmentation is used to divide photographs that contain bright items on a dark background. Feature extraction using Grey Level Co-occurrence Matrix (GLCM). The proposed technique, known as ELM-HTM combines a simple yet adaptable extreme learning machine (ELM) with a Hierarchical Temporal Memory (HTM). This approach outperforms the ELM and HTM model with an accuracy of about 98.8%.