  A staple food in more than a hundred nations worldwide is rice (Oryza
sativa). The cultivation of rice is vital to global economic growth. However,
the main issue facing the agricultural industry is rice leaf disease. The
quality and quantity of the crops have declined, and this is the main cause. As
farmers in any country do not have much knowledge about rice leaf disease, they
cannot diagnose rice leaf disease properly. That's why they cannot take proper
care of rice leaves. As a result, the production is decreasing. From literature
survey, it has seen that YOLOv5 exhibit the better result compare to others
deep learning method. As a result of the continual advancement of object
detection technology, YOLO family algorithms, which have extraordinarily high
precision and better speed have been used in various scene recognition tasks to
build rice leaf disease monitoring systems. We have annotate 1500 collected
data sets and propose a rice leaf disease classification and detection method
based on YOLOv5 deep learning. We then trained and evaluated the YOLOv5 model.
The simulation outcomes show improved object detection result for the augmented
YOLOv5 network proposed in this article. The required levels of recognition
precision, recall, mAP value, and F1 score are 90\%, 67\%, 76\%, and 81\%
respectively are considered as performance metrics.
