In-field Chilli Crop Disease Detection Using YOLOv5 Deep Learning Technique

Abstract

Crop disease detection in the actual field is difficult due to the unstructured environment. The images taken from the farms are covered mainly by green-colored plants or leaves, from which slightly varied portions of diseases are highly challenging to identify, even with the naked eye. This study contributes to detect the chilli leaf disease from images collected from actual field conditions. An RGB camera was used to collect images for the dataset from a chilli field. The research utilized one of the state-of-the-art deep learning object detection models named YOLOv5. The model is very efficient considering its speed of detection and accuracy. The model performed well despite having a small dataset with a mean average precision (mAP) of 0.461. The proposed deep learning object detection model is appreciably promising for disease detection of chilli crop.