Multiple disease detection method for greenhouse-cultivated strawberry based on multiscale feature fusion Faster R_CNN

Disease has a significant impact on strawberry quality and yield, and deep learning has become an important approach for the detection of crop disease. To address the problems of complex backgrounds and small disease spots in strawberry disease images from natural environments, we propose a new Faster R_CNN architecture. The multiscale feature fusion network is composed of ResNet, FPN, and CBAM blocks, and it can effectively extract rich strawberry disease features. We built a dataset for strawberry leaves, flowers and fruits, and the experi-mental results showed that the model was able to effectively detect healthy strawberries and seven strawberry diseases under natural conditions, with an mAP of 92.18% and an average detection time of only 229 ms. The model is compared with Mask R_CNN and YOLO-v3, and we find that our model can guarantee high accuracy and fast detection operational requirements. Our method provides an effective solution for crop disease detection and can improve farmers' management of the strawberry growing process.

Automated summary

Diseases have a significant impact on strawberry quality and yield, and deep learning has become an important approach for disease detection. A new architecture is proposed to accurately extract disease features from natural environments, and experimental results show its effectiveness in strawberry cultivation.