Region-Farm Crop Planning Through Double Deep Q-Learning Toward Sustainable Agriculture

Abstract

Global food market faces escalating risks and uncertainties, bringing great challenges in balancing a country's food supply and demand. Therefore, it is of great significance to carry out crop planning and reasonably divide the planting area of each crop to ensure the national food security. However, the existing planting planning methods have the problems of inaccurate crop price prediction and poor flexibility, and challenges remain on how to motivate farmers. With the rapid development of science and technology, agricultural crop planning techniques have made great progress. This study focuses on agricultural planting planning, exploring both planting area planning based on predicted crop prices and a crop allocation model within a multifarmer context. The regional planting goals are decomposed into specific allocations for individual farmers and plots, addressing objectives including maximizing farmer profits and expanding soybean cultivation for national self-sufficiency. The work employs the long short-term memory (LSTM) model to predict the prices of soybean, wheat, and maize. First, linear programming model is applied to plan planting areas of crops, incorporating constraints to encourage sustainable agricultural practices. Second, a multifarmer crop allocation model, utilizing the double deep Q network (DDQN) algorithm, is developed to enhance the fairness among farmers and assure rotational benefits. Experimental validation confirms the effectiveness of the proposed algorithms, providing valuable decision support for agricultural planning with economic and ecological sustainability.