Crop diseases are one of the main factors threatening crop growth.In this regard,machine-learning algorithms can efficiently detect large-scale crop diseases and are beneficial for timely processing and improving crop yield and quality.In large-scale agricultural scenarios,owing to limitations in power supply and other conditions,the power-supply requirements of high-computing-power devices such as servers cannot be fulfilled.Most existing deep-network models require high computing power and cannot be deployed easily on low-power embedded devices,thus hindering the accurate identification and application of large-scale crop diseases.Hence,this paper proposes a lightweight crop-disease-recognition algorithm based on knowledge distillation.A student model based on a residual structure and the attention mechanism is designed and knowledge distillation is applied to complete transfer learning from the ConvNeXt model,thus achieving the lightweight model while maintaining high-precision recognition.The experimental results show that the accuracy of image classification for 39 types of crop diseases is 98.72%under a model size of 2.28 MB,which satisfies the requirement for deployment in embedded devices and indicates a practical and efficient solution for crop-disease recognition.
万方数据
维普
