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.