The substitution of chemical pesticides by biopesticides is crucial to ensure the quality of agricultural products and to foster environmental sustainability. This study takes the willingness and the behaviors of rice farmers on the application of biopesticides as the research object. The survey questionnaire was designed based on the theory of rational small-scale farmers from three aspects: “individual and family characteristics of farmers”, “cognition of farmers” and “external factors”. The survey was then conducted on 163 rice farmers in seven prefecture-level cities in Jilin Province of China. The logistic model was used to analyze the influencing factors resulting in the deviation of the behaviors of the rice farmers from their initial willingness on the application of biopesticides. The explanatory structure model (ISM) was used to analyze the logical hierarchical relationship among various influencing factors. The results show that: (1) For 45% of the farmers surveyed, there’s a deviation between their willingness and behaviors regarding the application of biopesticides; (2) Among the significant factors leading to the deviation between farmers’ willingness and behaviors concerning the application of biopesticides, the surface-level direct factor is biopesticide awareness. The mid-level indirect factors are agricultural product quality and safety awareness and the deep-level root cause is farmers’ education level. (3) The primary reason for the deviation of the farmers’ behaviors from their willingness is their lack of knowledge about biopesticides and the biopesticides’ incomplete market structure. Based on the comprehensive analysis, it is recommended to improve the professionalization of the farmers, to strengthen the publicity of green production and to accelerate the formulation of the biopesticides market to further promote the usage of biopesticides.

The introduction of logistics theory and logistics technology has made the government and enterprises gradually realize that the development of logistics has an important strategic role, which can effectively solve the changing needs of users, optimize resource allocation, improve the investment environment, and enhance the overall strength and overall competitiveness of the regional economy. This paper carries out matrix-vector multiplication operations and weight update operations, designs a perceptron neural network model, and realizes a simulation platform based on MLP neural network. Moreover, on the basis of the standard MLP neural network, this paper proposes to use the deep learning training mechanism to improve the MLP neural network, which provides effective technical support for the improvement of the prediction model. In addition, through the fusion of deep learning and MLP neural network, an MLP neural network with three hidden layers is determined. Finally, this paper builds a model based on the MLP neural network algorithm, selects the RBF kernel function as the kernel function of the model by referring to the relevant literature, and uses PSO to optimize the combination of parameters. It can be seen from the result of the evaluation index that each evaluation index is relatively small. The result shows that the prediction is accurate, and the empirical result shows the feasibility of the model to predict the demand for industrial logistics in Shanxi Province.

The development of smart farming comes with a lot of data problems. Studies have shown this is due to insufficient cognition of the structural relationship between data and events. Shili Theory is an attractive concept. To embed intelligent agricultural technology in events and the natural environment, especially to unify and standardize agricultural production data, firstly, this paper has defined the concept of Shili Theory which researches the natural regularity of the event by Shili Mirrored Structure. Secondly, this paper has proposed a Shili Mirrored Structure based on the technology development path (from the human brain memory mechanism to the information storage mechanism to intelligent technology). Finally, the structure has been applied to develop an intelligent system of agricultural production data management. In rice production of Jilin Province, it forms the event chain of the whole plant 5T (seed, seeding, paddy shoot, grain, product period operation) and grain period 5T (harvesting, field stacking, drying, warehousing, storing). The system application shows that this management structure can reduce data flow, improve data utilization, and enhance the correlation between data and events. It can realize the quality improvement of the agricultural production process, especially revealing the 8.83% significant latent loss in rice harvest.