Tomato disease leaf recognition based on the YOLOv8 multi-classify model
A multi-classification model using the deep learning technology YOLOv8 was developed to identify seven common tomato diseases,focusing on its application in agriculture.Two pre-trained models,YOLOv8n-cls and YOLOv8x-cls,were trained on a dataset of tomato disease leaf images.The results show high prediction accuracy for late blight,yellowing leaf curl disease,mosaic virus,and healthy leaves,with low false detection rates.However,spot blight,early blight,and leaf mold had lower recognition accuracy due to similar symptoms,leading to confusion.Future work will increase the number and diversity of datasets to optimize model parameters,aiming to improve recall and accuracy for all disease categories and enhance the practical application of YOLO technology in agriculture.
tomato diseasesYOLOv8 multi classification modelprediction accuracyrecall
万方数据
维普
