StrawberryNet: Fast and Precise Recognition of Strawberry Disease Based on Channel and Spatial Information Reconstruction

Abstract

Timely and effective identification and diagnosis of strawberry diseases play essential roles in the prevention of strawberry diseases. Nevertheless, various types of strawberry diseases with high similarity pose a great challenge to the accuracy of strawberry diseases, and the recent module with high parameter counts is not suitable for real-time identification and monitoring. Therefore, in this paper, we propose a lightweight strawberry disease identification method, termed StrawberryNet, to achieve accurate and real-time identification of strawberry diseases. First, to decrease the number of parameters, instead of standard convolution, a partial convolution is selected to construct the backbone for extracting the features of strawberry disease, which can significantly improve efficiency. And then, a discriminative feature extractor, including channel information reconstruction network (CIR-Net) and spatial information reconstruction network (SIR-Net) modules, is designed for abstracting the identifiable features of different types of strawberry disease. A large number of experimental results were conducted on the constructed strawberry disease dataset, containing 2903 images and 10 common strawberry diseases and normal leaves and fruits. Extensive experiments show that the recognition accuracy of the proposed method can reach 99.01% with only 3.6 M parameters, which have good balance between the identification precision and speed compared to other excellent modules.

Keywords

• strawberry disease
• image recognition
• deep learning
• partial convolution
• lightweight network