A Comprehensive Analysis Of The Detection And Classification Of Potato And Corn Leaf Diseases Utilizing Deep Learning Methods

Due to their great nutritional content, potatoes and maize are necessary staple crops that require a strong food security system. Many diseases can negatively impact crop cultivation, thus it is critical to identify them as soon as possible to avoid output losses and agricultural devastation. Traditional methods for diagnosing plant diseases can be labor-intensive, inaccurate, and imprecise. Farms need to use computerized image processing technologies to avoid significant crop losses due to delayed identification. Farmers may more precisely and successfully manage plant diseases by using AI and machine learning techniques for image segmentation, classification, and feature extraction. As a consequence, yields will rise and losses will fall. Researchers have developed a number of techniques to forecast agricultural diseases by examining photos of damaged leaves. Even though different models and approaches are now employed to diagnose plant diseases, there is always room for improvement in light of recent discoveries and conversations. Technology has the potential to significantly boost crop output worldwide, and big data can be used to train models and techniques that more accurately detect plant diseases and deal with low-yield problems. Previous studies have shown that machine learning and deep learning are effective in this situation. In order to classify and identify potato and maize leaf diseases using deep learning techniques, a number of methodologies and approaches have been thoroughly analyzed and critically examined in this research. The study looks into the possible difficulties in using image processing to detect diseases in the leaves of potatoes and maize plants, as well as the advantages and disadvantages of various technological solutions. The findings and forecasts for the future of deep learning-based potato and maize disease detection are presented in the paper’s conclusion.