In the digital era, an increasing number of people prefer shopping on e-commerce platforms. With the development of agricultural product e-commerce platforms, consumers find it challenging to discover suitable products among numerous choices. To enhance user satisfaction and purchase intent, agricultural product e-commerce platforms need to recommend appropriate products based on user preferences. Considering various agricultural features such as season, region, user interests, and product attributes, feature interactions can better capture user demands. This study introduces a new model, fine-grained feature interaction selection networks (FgFisNet). The model effectively learns feature interactions using both the inner product and Hadamard product by introducing fine-grained interaction layers and feature interaction selection layers. During the training process, it automatically identifies important feature interactions, eliminates redundant ones, and feeds the significant feature interactions and first-order features into a deep neural network to obtain the final click through rate (CTR) prediction. Extensive experiments on a real dataset from agricultural e-commerce demonstrate significant economic benefits achieved by the proposed FgFisNet method.