Image Processing Techniques for Leaf Disease Detection based on ELM-SSA Approach

Leaf diseases have emerged as a significant issue in agriculture due to their ability to significantly lower both the quality and quantity of harvests. If pests are not detected on crops and leaves in a timely manner, and an effective treatment is not administered, food insecurity, poverty, and mortality rates will rise. This demonstrates why automated leaf disease recognition is so crucial in the agricultural sector. Preprocessing, segmentation, feature extraction, and model training are all parts of the proposed technique. Preprocessing an image involves adjusting its data in order to remove unwanted distortions and boost other characteristics of the image that will be used later in analysis. The process of dividing a digital image into smaller images is called image segmentation. The proposed approach employed GLCM for the feature extraction process. Following the collection of features, ELM-SSA is used to train the models. The proposed method is superior to the ELM and SSA methods, which are both very popular.