Analysis of Growth Characteristics of Kimchi Cabbage Using Drone-Based Cabbage Surface Model Image

Abstract

Cultivation soil is the basis for cabbage growth, and it is important to assess not only to provide information on how it affects the growth of vegetable crops but also for cultivation management. Until now, field cabbage surveys have measured size and growth variations in the field, and this method requires a lot of time and effort. Drones and sensors provide opportunities to accurately capture and utilize cabbage growth and variation data. This study aims to determine the growth stages based on drone remote estimation of the cabbage height and evaluate the impact of the soil texture on cabbage height. Time series variation according to the growth of Kimchi cabbage exhibits an S-shaped sigmoid curve. The logistic model of the growth curve indicates the height and growth variation of Kimchi cabbage, and the growth rate and growth acceleration formula of Kimchi cabbage can thus be derived. The curvature of the growth parameter can be used to identify variations in Kimchi cabbage height and its stages of growth. The main research results are as follows. (1) According to the growth curve, Kimchi cabbage growth can be divided into four stages: initial slow growth stage (seedling), growth acceleration stage (transplant and cupping), heading through slow growth, and final maturity. The three boundary points of the Kimchi cabbage growth curve are 0.2113 Gmax, 0.5 Gmax, and 0.7887 Gmax, where Gmax is the maximum height of Kimchi cabbage. The growth rate of cabbage reaches its peak at 0.5 Gmax. The growth acceleration of cabbage forms inflection points at 0.2113 Gmax and 0.7887 Gmax, and shows a variation characteristic. (2) The produced logistic growth model expresses the variation in the cabbage surface model value for each date of cabbage observation under each soil texture condition, with a high degree of accuracy. The accuracy evaluation showed that R2 was at least 0.89, and the normalized root-mean-square error (nRMSE) was 0.09 for clay loam, 0.06 for loam, and 0.07 for sandy loam, indicating a very strong regression relationship. It can be concluded that the logistic model is an important model for the phase division of cabbage growth and height variation based on cabbage growth parameters. The results obtained in this study provide a new method for understanding the characteristics and mechanisms of the growth phase transition of cabbage, and this study will be useful in the future to extract various types of information using drones and sensors from field vegetable crops.