Few-shot cotton pest recognition and terminal realization

Xinjiang is the major cotton-producing area in China, also well known in the world for its high-quality cotton. The growth and quality of cotton are closely related to the pest attack, but it is difficult for farmers to manually recognize all the types of pests because of the similar appearance. To solve this problem, we propose a few-shot cotton pest recognition method, which only needs few raw training data, quite different from the typical deep learning methods. We use two datasets to verify the effectiveness and feasibility of the few-shot model, one is the National Bureau of Agricultural Insect Resources (NBAIR), the other is a dataset with the natural scenes. The convolutional neural network (CNN) is adopted to extract feature vectors of images. The CNN feature extractor is trained by the triplet loss to distinguish different pest species to ensure the system robustness. Furthermore, the few-shot recognition model is finally running in an embedded terminal, based on the compiled convolutional and max-pooling circuit in the FPGA and the control program in the ARM. The running speed reaches 2 frames per second and can be faster by the further parallelism in hardware. The testing accuracy for the two datasets is 95.4% and 96.2% respectively, shown the generalization ability of the proposed few-shot model. Moreover, this work can also be regarded as a positive attempt to combine the software and hardware for the landing of intelligent algorithms in the agricultural applications.