Chicken Swarm Algorithm with Deep Learning for Plant Leaf Disease Detection and Classification

Diseases that affect plants have a major influence on crop yields around the globe. Crop yields and plant development have been severely limited in many parts of the world due to plant diseases for quite some time. The food supply has been negatively affected as a result of this. In certain cases, depending on the specifics of the issue, it may be possible to make out the plant’s leaves. Photo documentation of the plant’s leaves revealed the presence of many illnesses. It is crucial to determine the type of infestation before initiating any action to eradicate it. Among the many factors considered in this case study are the rapid spread of illnesses and the farmers’ lack of diagnostic expertise. Fortunately, by merging deep learning (DL) techniques into computer vision (CV), we might be able to simplify machine learning (ML) and so lessen this issue. The purpose of this study is to identify plant leaf diseases using a Deep Learning system in conjunction with a Chicken Swarm Algorithm. Helping farmers increase agricultural output while decreasing crop losses is one of the aims of the proposed method. Visual descriptions of images are used to achieve this. Accurately classifying the occurrence of diseases that commonly impact leaves is the goal of the proposed technique. The goal of this comprehensive experimental study was to compare the proposed method to a reference database and find out if it had any unique features. Based on the testing findings, the proposed algorithm outperformed the previous systems on multiple performance metrics.