A lightweight network for mummy berry disease recognition

Abstract

Mummy berry disease caused by Monilinia vaccinii-corymbosi (Reade) occurs during the productive season of blueberry plants. In severe cases, it will cause a huge decline in blueberry yield and cause significant economic losses. The correct identification of mummy berry disease helps growers to take timely preventive measures, which can limit the further spread of mummy berry disease and reduce the damage to blueberries. In this paper, aiming at identifying mummy berry disease in a real environment, we propose a lightweight network model that can be deployed on mobile or embedded devices without worrying about the limitation of computational cost and memory storage capacity. This model selects MobileNet V1 as the basic network. The core convolution layer adopts a multi-scale feature extraction module proposed by us, namely MSFE module, which combines the dilated convolution and the depthwise separable convolution in a parallel manner. At the end of the model, the feature filtering module FFM based on channel attention mechanism is used to improve the classification performance of the model. The parameter size of the model is only 2.61 million, which is about 19.2% lower than that of MobileNet V1. Experiments show that the model has achieved a good classification effect on the mummy berry disease data set, with a test accuracy of 99.33%, which is 3.17% higher than the MobileNet V1 test accuracy.

Keywords

• Blueberry
• Mummy berry disease
• Deep learning
• Lightweight network
• Convolution neural network
• Attention mechanism