Abstract: India, predominantly reliant on agriculture, suffers an estimated 18% loss in global crop yield annually due to pest attacks, amounting to approximately Rs. 90,000 million. Overuse of pesticides poses numerous hazards including soil degradation, acute toxicity to humans and animals, shifts in pest populations, high control costs, and environmental residue issues. Whiteflies are particularly problematic pests, infesting plant leaves, excreting sticky honeydew, causing leaf discoloration or death, and reducing crop yield. Traditionally, farmers have relied on visual assessments to gauge whitefly infestations, but this method is often imprecise due to varying identification skills and the time-consuming nature of laboratory inspections. Given the economic importance of crops and the severe impact of pest damage, early detection of whiteflies has become imperative. To address this, we propose an Android application that calculates the affected area of plants and determines disease severity. The application also provides treatment recommendations in Hindi for identified diseases. Detection of plant diseases is a critical research area, offering benefits in monitoring vast agricultural fields. Automated disease detection through image processing offers a more accurate and efficient alternative to manual visual identification, which is prone to errors and time constraints. This approach enhances accuracy and facilitates timely intervention and disease management, ultimately improving crop productivity and sustainability.
Keywords: : Image Processing, Plant Disease, HSV(Hue Saturation Value), Machine Learning. Android Application
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DOI:
10.17148/IJARCCE.2024.13409
