Abstract
Considering the time-consuming and tedious work of the current methods to control plant layout, which is mostly based on expert experience or field trials, we propose an algorithm to optimize and simulate a planting layout based on a virtual plant model and an optimization algorithm. A functional-structural plant model, which combines the structure and physiological function of plants, is used to construct a planting scene. The planting and row spacing are set as the genetic factors and the chromosomes of the genetic algorithm are encoded with a binary method. The photosynthetic yield of the unit planting area is denoted as the fitness value. By using this method, the intercropping of maize and soybean plants and the sole cropping of rice plants are studied. Experimental results show that the proposed method can obtain a high yield planting plan. Keywords: functional-structural plant models, genetic algorithm, spacing optimization, plant spatial distribution DOI: 10.25165/j.ijabe.20201301.4877 Citation: Ding W L, Fan C C, Xu L F, Xie T, Liu Y, Wan Z X, et al. Optimization method to obtain appropriate spacing parameters for crop cultivation. Int J Agric & Biol Eng, 2020; 13(1): 146–152.
Highlights
In China, many kinds of crops are grown
Considering the time-consuming and tedious work of the current methods to control plant layout, which is mostly based on expert experience or field trials, we propose an algorithm to optimize and simulate a planting layout based on a virtual plant model and an optimization algorithm
The planting and row spacing are set as the genetic factors and the chromosomes of the genetic algorithm are encoded with a binary method
Summary
In China, many kinds of crops are grown. Different types of crops need different environments to grow well. The determination of the layout of traditional agricultural crops is mostly based on expert experience[3,4] or field trials[2,3,4,5,6,7,8,9,10,11]. With the popularization of mechanical sowing and planting, the spacing of sowing is often uniformly determined for the same crop, and cannot reflect the characteristics of different plant types. This traditional approach has failed to meet the requirements of modern precision agriculture for the precise positioning, quantification, and timing of agronomic measures and the modern agriculture requirements for smart farming technology[12] and production processes
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