Condition Information Entropy and Rough Set Method Based on Particle Swarm Optimization Applied in the Natural Quality Evaluation of Cultivated Land

Abstract

The evaluation of the natural quality of cultivated land is crucial for preserving arable land and achieving a balance between the quantity and quality of arable land. Therefore, a timely assessment of the natural quality of cultivated land is needed to monitor its changes. However, current methods often focus on a single specified crop, neglecting the variations that occur across different specified crops. Since the indicator weight recognition method is only suitable for a single crop, this paper proposes a novel model evaluating the natural quality of cultivated land based on the method of “hidden light–temperature index and yield ratio coefficient”. In addition, the condition information entropy and rough set method based on particle swarm optimization (CIERS-PSO) were proposed to evaluate the natural quality of cultivated land in Enshi. Firstly, condition information entropy and rough set are adopted to determine the importance of the indicator automatically. Then, particle swarm optimization (PSO) is utilized to obtain the optimal weights of the first-level and second-level indicators. Finally, the proposed model and evaluation method were adopted to evaluate the natural qualities of the cultivated land. The experimental results demonstrated that the combination of the “hidden light–temperature index and yield ratio coefficient” model and the CIERS-PSO method can automatically identify the indicator weights for the evaluation of natural quality in multi-crop cultivated land. It could obtain better evaluation accuracy even if the sample size is small.