A Systematic Analysis of Various Techniques for Mango Leaf Disease Detection

Abstract

Monitoring plant illnesses was just by vision, is insufficient for recognizing plant diseases. The leaf changes color, revealing blotches such as yellow dots, black spots, or chocolate brown patches, as a result of the symptoms. Diseases like Anthracnose, Powdery Mildew, and Sooty Mold can be found on some leaves. To diagnose the disease, manual observation and pathogen detection are used, which takes longer and costs more money and gives less precision results. Therefore, a superior option to fast and precise identification through image processing techniques can be used, which can be more dependable than some other old traditional ways. Fruit, leaves, stems, and lesions are examples of plant components that may exhibit symptoms. The goal is to accurately find and diagnose the disease based on the leaf photos. Image preprocessing, segmentation, feature extraction, and classification are all necessary phases in the process. This paper will go through how to recognize mango leaf disease. Leaf characteristics such as their axis, including main and minor axes, are acquired, and diagnosed using various classification methods for illness diagnosis.