DEEP LEARNING BASED MODEL FOR RICE PLANT DISEASE CLASSIFICATION USING YOLOV8
Volume:1
Issue: 1
Page: 1-19
Received:03-Jul-2023
Accepted:07-Jul-2023
ABSTRACT:Proper plant diseases classification is important as it helps farmers in providing appropriate pesticide to the affected area. Rice farmers are facing a lot of challenges in classifying diseases in their rice farm, improper classification can lead to in appropriate pesticides and as such the damages can increase. In Nigeria rice is among the most stable food that is consumed in almost all house hold, as it’s normally used in wedding events, ceremonies and during festival celebration. Rice farmers used necked eye observation to classify the sign or symptoms of the disease which is time consuming and sometimes expert need to be taken to the field for proper classification. In some cases samples need to be taken to laboratory which another expensive and time consuming. Therefore, the need for thorough research to investigate the classification of diseases in rice plant is of much significant in order to improve quantity and quality during cultivation which as a result will enhance food security of the nation. This research present a deep learning based model for rice plant diseases classification using YOLOv8 as pretrained model it also used data that are directly captured from our environment using android phones. 500 images were captured under white background and are used during the training of the model. 100% accuracy was obtained from both training and validation on 50 epochs. The model will performed only binary classification and it was trained in goggle colab.
