Research on Strawberry Disease Diagnosis Based on Improved Residual Network Recognition Model

Abstract

Considering the problems of high cost, inefficiency, and time consumption of manual diagnosis of strawberry diseases, G-ResNet50 is proposed based on transfer learning and deep residual network for strawberry disease identification and classification. The G-ResNet50 is based on the ResNet50, and the focal loss function is introduced in G-ResNet50 to make the model devote itself to disease images that are difficult to classify. During the training process of the G-ResNet50 model, its convolutional layer and pooling layer inherit the pre-trained weight parameters from the ResNet50 model on the PlantVillage dataset, while adding dropout regularization and batch regularization methods to optimize the network model. The strawberry disease dataset includes four sample images of healthy plants, powdery mildew, strawberry anthracnose, and leaf spot disease. The dataset is enhanced and expanded by operations including angle rotation, adjusting contrast and brightness, and adding Gaussian noise. Compared with existing models such as VGG16, ResNet50, InceptionV3, and MobileNetV2, the results of model training and testing on 7,525 four-category leaf datasets show that the G-ResNet50 model has faster convergence speed and better classification effect, and its average recognition accuracy rate reached 98.67%, which is significantly higher than other models. Through the three evaluation indicators of precision rate, recall rate, and confusion matrix, it is concluded that the G-ResNet50 has good robustness, high stability, and high recognition accuracy and can provide a feasible solution for strawberry disease detection in practical applications.

A Crop Leaf Disease Image Recognition Method Based on Bilinear Residual Networks

“…e value at the diagonal line shows correctly predicted tags. e darker the diagonal line indicates the better the model's effect [33].…”

Cotton disease identification method based on pruning

“…However, SCDD is too small, if training the network directly on it may lead to the problems such as low recognition accuracy or overfitting. Transfer learning can solve these problems very well ( Ghazi et al., 2017 ; Chen et al., 2020 ; Wenchao and Zhi, 2022 ). The key to transfer learning is to find out the similarities between the source domain and the target domain ( Gao and Mosalam, 2018 ).…”

Thermographic Fault Diagnosis of Shaft of BLDC Motor

“…Neural networks are used for fault diagnosis, the verification of technical equipment, image recognition, and industrial fault diagnosis systems [ 15 , 16 , 17 , 18 ]. There are several types of Convolutional Neural Networks (CNNs): GoogLeNet [ 19 , 20 , 21 ], ResNet50 [ 22 , 23 , 24 ], and EfficientNet-b0 [ 25 , 26 , 27 ]. CNNs can assign importance to objects in the thermal image.…”

“…EfficientNet-b0, available in Matlab software, consists of 290 layers. More information on GoogLeNet, ResNet50, and EfficientNet-b0 is described in the literature [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ].…”