In agriculture, the plant disease identification can increase the production of crops. The existing data-driven deep plant disease identification methods are based on a great number of supervised data, posing vast challenges on detecting new pests of few data. And there are many trainable parameters in those deep learning-based methods, costing much computation resources. To solve those challenges, a lottery ticket-based deep sparse transfer method is proposed for the plant disease identification. Specifically, the deep lottery ticket hypothesis is introduced, in which a compressing strategy is designed to construct the deep sparse network that distills useful information in the auxiliary domains, improving the transfer efficiency. Then, a deep lottery ticket transfer algorithm is proposed to train a deep plant disease identification model that can effectively detect the new pests of few data. Finally, the proposed method is evaluated on the representative datasets, i.e., CIFAR-10 and PlantVillage, and the accuracy of detecting new pests can achieve 97.69% in plantViuage with 70%-parameter-reduction.