Data-Fusion Based on Transfer Learning for Plant Disease Recognition

Abstract

In this paper, the research focused on wild and introduced cultivated flowers with multiple diseases such as Stephanitis, Sooty Mould, Xanthosis, and Leaf Blight, utilizing transfer learning and data fusion technology to construct a plant disease detection model employing Faster R-CNN. The self-built data set collected during the flower growth cycle was trained and identified. To solve the problem of disease category imbalance in the actual collected data samples, the data of small category samples is enhanced from the perspective of category balance and label balance, and FocalLoss is used to improve the original classification loss function. Based on this self-built data set, the constructed IFRCNN disease detection model was compared with the SSD (Single Shot mul-tibox Detector), ResNet18 and Yolov3 models. The results showed that for several common plant diseases in the dataset, the mAP of IFRCNN disease detection model was significantly higher than that of the other three models. It can effectively locate plant leaf disease areas, realize the detection of multiple diseases, and provide reference for accurate disease prevention and control.