Image Transform and Hybrid Optimization Based Feature Reduction for Leaf Diseases Classification

Abstract

Abstract: In recent years, there has been amazing progress in technology innovation across many sectors, including medical care, homeland security, the arts, and even agriculture. The ability to run one's business well is highly regarded in every sector. Because to recent advancements in technology, such as smart phones and the Internet of Things, this is now a distinct possibility. In this research, we offer a smart management system for horticulture that makes use of machine learning methods. This system is based on the smart management scheme that was previously described. As a component of horticulture management, many academics have advocated for the use of machine learning to analyse plant growth based on the features of the leaf. We used the grayscale image, which employed numerous features for classification while simultaneously normalising the pixel values. As a consequence of this, the processing of damaged leaves in their early stages takes more time and results in less accurate classification. As a result, for the purpose of this work, we do a colour space conversion in order to investigate the image in terms of hue, saturation, and value. Following that, it extracted features from the Value channel by making advantage of the histogram's attributes. After that, the best characteristics were chosen by a process that included both particle swarm and cuckoo search optimisation. Following this, a regression neural network was trained to classify data based on the features that had been selected in the previous step. In order to evaluate how effective the proposed strategy is, classification performance criteria such as accuracy, sensitivity, and specificity were utilised. The suggested approach was tested in a simulated Windows 10 environment using a real-time dataset and the MATLAB software. It was compared to other methods that are already in use. While evaluating performance, comparisons to the current state of the art are conducted with regard to accuracy, sensitivity, and specificity