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.
Highlights
Disease problems are responsible for the decline of vegetable quality, which leads to farmers’ economic losses, and are closely related to daily economic activities
In reference [32], the author proposed an improved residual network (ResNet) model for the image classification of multiple diseases in medical X-ray images, the global average pooling is replaced by an adaptive dropout of medical image classification, and the multi-label classification is converted into N two meta-classification, and the last multiple experimental evaluations show that compared with the traditional architecture and VGG16, the accuracy of the proposed model achieved 87.71% and 81.8% on different datasets, respectively. e author in the literature [33] describes a large number of algorithms and applications related to data analysis, pattern recognition, machine learning, deep learning, and the Internet of things, especially in the field of health care; for example, various advanced neural network algorithms are used to identify and classify disease images
The strawberry disease image dataset is trained on five network models: G-ResNet50, ResNet50, VGG16, InceptionV3, and MobileNetV2, and each network is trained for 35 epochs
Summary
Disease problems are responsible for the decline of vegetable quality, which leads to farmers’ economic losses, and are closely related to daily economic activities. As one of the main crops cultivated in greenhouse, strawberry has many disease problems. How to quickly and accurately discover and identify strawberry diseases and take corresponding control measures is an important means to ensure strawberry growth, cure the disease, and increase farmers’ income. Common strawberry diseases include powdery mildew, strawberry anthracnose, and leaf spot, effectively solving the problem of yield reduction caused by strawberry diseases, which mainly depend on accurately and quickly identifying strawberry diseases. Traditional manual identification of strawberry diseases is low in efficiency, poor in real time, has low accuracy, and is timeliness. Erefore, efficient and accurate identification of strawberry diseases can effectively reduce diseases and increase yield Traditional manual identification of strawberry diseases is low in efficiency, poor in real time, has low accuracy, and is timeliness. erefore, efficient and accurate identification of strawberry diseases can effectively reduce diseases and increase yield
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