Real Time Emblica Fruit Disease Monitoring System

Abstract

Abstract: Phyllanthus Emblica (Amla) fruit is vulnerable to fungal diseases mainly soft-rot and rust causing 25-30% loss in production of fruit in India, the largest producer of Phyllanthus Emblica. So, effective automated detection of Phyllanthus Emblica fruit disease at early stage will not only monitor health status but also help farmers to correctly identify and treat Phyllanthus Emblica disease. This project proposes novel technique of real time detection and categorization of soft-rot (Phomopsis Phyllanthi) and rust (Ravenelia Emblicae) using CNN and YOLO-V2 and providing eco-friendly treatment of detected disease using Apple Cider Vinegar (ACV) and bio-fertilizer as fungal controller. For early detection of disease, input image is taken directly from the field using ESP32 camera and YOLO-V2 detects whether input contains Phyllanthus Emblica fruit or not. The output image is trained using CNN to classify fruit disease. CNN and YOLO-V2 is used together for the increased accuracy and better IOU as it is faster algorithm than its counterparts, running at 45 FPS in detection of Phyllanthus Emblica fruit disease over SVM methods and result showed 99.99% accuracy in early detection of soft-rot and rust. Ravenelia Emblicae and Phomopsis Phyllanthi were isolated and identified using PDA and assessed for fungitoxity of homemade ACV and Bio-fertilizer using microbes culturing method. ACV effectively inhibited fungi on 5th day .Thus disease can be controlled after 3-5 attempts of ACV. This technique is easily accessible to farmers and finds a great future for timely detection, prevention and control using eco-friendly treatment of fruit disease which is crucial to promote healthy growth and maintain nutritive value of Phyllanthus Emblica fruit.