LAUSR

In the era of twenty-first century, artificial intelligence plays a vital role in the day to day life of human beings. Now days it has been used for many application such as medical, communication, object detection, object identification, object tracking. This paper is focused on the identification of diseases in bell pepper plant in large fields using deep learning approach. Bell Pepper farmers, in general do not notice if their plants are infected with bacterial spot disease. The spread of the disease usually causes a decrease in the yield. The solution is to detect if bacterial spot disease is present in the bell pepper plant at an early stage. We do some random sampling of few pictures from different parts of the farm. YOLOv5 is used for detecting the bacterial spot disease in bell pepper plant from the symptoms seen on the leaves. With YOLO v5 we are able to detect even a small spot of disease with considerable speed and accuracy. It takes the full image in a single instant and predicts bounding boxes and class probability. The input to the model is random picture from the farm by using a mobile phone. By viewing the output of the program, farmers can find out whether bacterial spot disease has in any way affected the plants in their farm. The proposed model is very useful for framers, as they can identify the plant diseases as soon as it appears and thus, do proper measures to prevent the spread of the disease. The motive of this paper is to come up with a method of detecting the bacterial spot disease in bell pepper plant from pictures taken from the farm.