Eggplant Disease Detection Using Yolo Algorithm Telegram Notified

Abstract

Eggplant (Solanum Melongena L) is a type of seasonal vegetable. Eggplants vary in shape and color from white to green to purple. Facts prove that eggplant plants face several threats to their survival and growth, including disease disturbances such as leaf beetles, fruit rot, mealybugs, etc. One way to treat and monitor eggplant plants is to install a camera to capture accurate and fast image data. The image captured by the camera will be digitally processed, which can be used by deep learning methods on object recognition and evaluation system in eggplant plants. Digital image processing can present the color accuracy of leaves, and fruit stems in eggplant plants for solutions and innovations. Digital image processing is the study of image processing techniques. Processed images are still images or images captured with a model camera. In this study, the images taken were of colored leaves attacked by eggplant rot and brown stem rot. The color model used in this study to transform images to color uses the red-green-blue color function. This study uses the YOLOv4 algorithm implemented on a mobile computing device such as Raspberry Pi 4 to select images of eggplant plants captured by a camera. The image data that has been processed will produce results in plant disease diagnoses based on the image captured by the camera. The results will be announced immediately or announced on the Telegram application. We can then detect diseases in eggplant plants and provide immediate preventative measures for diseased plants to maximize yield.