Optimization allocation strategy of agricultural production resources based on SSA-BP algorithm.

Inefficient agricultural resource allocation results in resource inefficiencies and reduced yields. Especially in the context of climate change and reduced arable land, how to balance maximizing yield, and improving ecological benefits is an urgent challenge for modern agriculture. The research proposes a hybrid optimization model based on Sparrow Search Algorithm (SSA) and Back-propagation Neural Network (BP). To solve the problems that traditional methods are prone to fall into local optimum, have slow convergence speed and insufficient prediction stability, and improve crop yield and optimize allocation, SSA explored the constrained resource allocation problem globally by simulating the foraging and alert behavior of sparrow populations, while BP fitted the nonlinear relationship between resources and yield through error back-propagation mechanism. The two work together to optimize the initial weights of the neural network and introduce differential evolution strategy as part of SSA to enhance robustness. The experiments showed that when the number of iterations of the research method reached 8 times, the average fitness dropped to 3. In the accuracy analysis of the calculation results, when the number of nodes in the hidden layer of the research method was 2, the accuracy remained above 98.5%. The resource cost-output ratio of the research method remained above 1.15, indicating cost-effectiveness. This study provides real-time decision-making tools for intelligent agricultural management platforms, supporting cross regional resource scheduling and extreme climate adaptation optimization. It can enhance resource utilization by adjusting the water and fertilizer ratio in real time, contributing to the dual optimization of agricultural economic and ecological benefits.