Research on Path Planning of Fully Automatic Planter for Small Cultivated Area Based on Improved Genetic Algorithm

Abstract

In the development of modern agriculture, automated machinery is widely used in agricultural cultivation, which is of great significance in innovating the mode of agricultural and rural business and promoting the modernisation of agriculture and rural areas. Nowadays, due to the complex terrain of small-scale farming areas, the existing large-scale agricultural machinery is restricted, and mainly, seeding relies on the workforce, resulting in the intensification of farmland abandonment in the hilly and mountainous areas in the south. Therefore, it is imperative to design an automated planter that can be applied to small-scale farming areas, and path planning is an important part of the automated planter. In this paper, an improved genetic algorithm is proposed to focus on the path planning research for small agricultural seeders. The algorithm improves the drawbacks of the traditional genetic algorithm through multi-objective optimisation, the intrinsic correlation between the number of turns and the difficulty of passage and the weight allocation, constructs a comprehensive, objective function, and realises the optimal decision of path planning. Through coding, fitness function, and other aspects of the improvement, this paper break through the limitations of the traditional genetic algorithm to a greater extent to improve the efficiency and quality of seeding for the realisation of agricultural automation to inject a strong impetus.