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

Go, Seung-Hwan; Lee, Dong-Ho; Na, Sang-Il; Park, Jong-Hwa

doi:10.3390/agriculture12020216

Cited by 9 publications

(1 citation statement)

References 43 publications

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“…However, there is limited data on the phenology (cultivation capacity, ear-earning time, harvesting time) and final yield of crops other than rice. Although the growth characteristics of crops vary slightly depending on the crop, they often show a sigmoid growth curve [3]. If a system is established to observe basic meteorological factors and crop growing conditions in real time, farmers and food producers will be able to precisely manage crops through model construction.…”

Section: Introductionmentioning

confidence: 99%

Development of drone acquisition imagery and AI-based field crop status and growth prediction model

Go,

Park

2023

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV

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Remote sensing drone utilization technology is rapidly developing and is being used in a variety of ways, from seed sowing to disease management and maintenance. Crop growth environment analysis and crop prediction vary depending on the climate, soil environment, topography, and applied technology of the target area. Crop growth is a complex trait determined by various factors such as genotype, growing environment, and interactions. To accurately predict growth conditions and growth, it is necessary to fundamentally understand the functional relationship between these interaction factors through data analysis. Interpretation of growth-related relationships requires both a comprehensive dataset and powerful algorithms in the model. This study aimed to build a model using drone imaging and AI technology to develop a model for the cultivation status and growth prediction of various crops grown in the field. The development model included the entire process of drone image acquisition, image processing, AI algorithm application, farmland information, crop status, and growth information production. This paper presents the overall configuration for the construction of the growth prediction model and the results of the AI-based cultivation area extraction model conducted in the first stage. Classifying cultivated crops by field is important for identifying the cultivated area and predicting yield. The development of drone remote sensing (RS) and AI technology has made it possible to precisely analyze the characteristics of field crops with images. The purpose of this study was to create and evaluate an AI-based cultivated crop classification model using the reflectance and texture characteristics of drone RGB images. The major crops cultivated during the crop classification survey period were kimchi cabbage, soybean, and rice. The texture applied in this development model is the texture characteristic of Haralick using GLCM (Gray Level Co-occurrence Matrix). A total of 8 factors were used to create the model: mean, variance, contrast, homogeneity, correlation, ASM (Angular Second Moment), homogeneity, and dissimilarity. Two AI models, SVC and RFC, were built in this study. For the SVC-based classification model, the hyperparameters C and gamma were set to 1.5 and 0.01, respectively, and a radial basis function (RBF) kernel was used. The cross-validation accuracy was 0.88 and the test set accuracy was 0.91. The maximum depth of the RFC-based classification model was set to 8 and the number of trees was set to 500. The cross-validation accuracy of the RFC-based model was 0.95, and the test set accuracy was 0.89. The learning time of the two models was 90 seconds for the SVC model and 7,200 seconds for the RFC model. The SVC-based classification model was evaluated as advantageous when considering classification accuracy and learning time. The findings of this study are expected to improve the precision of crop cultivation area identification using AI technology and be used as a useful tool for agricultural production manag...

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Section: Introductionmentioning

confidence: 99%

Development of drone acquisition imagery and AI-based field crop status and growth prediction model

Go,

Park

2023

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV

Self Cite

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Residue dissipation dynamics and dietary risk assessment of emamectin benzoate, chlorantraniliprole, chlorfenapyr, and lufenuron in cabbage

Wang,

Hu,

Meng

et al. 2023

Environ Sci Pollut Res

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Seasonal variation of metabolites in Kimchi cabbage: utilizing metabolomics based machine learning for cultivation season and taste discrimination

Ju,

Park,

Lee

et al. 2024

Hortic. Environ. Biotechnol.

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Kimchi cabbage, a staple in South Korean cuisine, exhibits taste variations depending on the season of cultivation, with significant implications for kimchi production quality. In this study, we conducted comprehensive metabolomic analyses of kimchi cabbage grown in diverse environments throughout the year. We identified 15 primary metabolites, 10 glucosinolates, and 12 hydrolysates, providing valuable insights into the metabolic composition of kimchi cabbage. Using this data, we developed predictive models for taste and quality differentiation in kimchi cabbage based on the season of cultivation. Three regression models, including Orthogonal Partial Least Squares regression (OPLS), Partial Least Squares (PLS) regression, and Random Forest regression, were employed to predict seasonal variation. The models exhibited high accuracy, with R2 values ranging from 0.77 to 0.95, indicating their potential for distinguishing seasonal differences. Notably, hydroxyglucobrassicin, 5-oxoproline, and inositol consistently emerged as significant metabolites across all models. Additionally, we developed regression models for predicting sweetness and bitterness in kimchi cabbage. Metabolites such as malic acid, fructose, and glucose were positively correlated with sweetness, while neoglucobrassicin and glucobrassicin were negatively correlated. Conversely, metabolites like glucoerucin and glucobrassicin were positively correlated with bitterness, while malic acid and sucrose were negatively correlated. These findings provide a valuable foundation for understanding the metabolic basis of taste variation in kimchi cabbage and offer practical applications for improving kimchi production quality. By incorporating more varieties and multi-year data, future research aims to develop even more accurate predictive models for kimchi cabbage taste and quality, ultimately contributing to the consistency of kimchi production. Graphical Abstract

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

Cited by 9 publications

References 43 publications

Development of drone acquisition imagery and AI-based field crop status and growth prediction model

Development of drone acquisition imagery and AI-based field crop status and growth prediction model

Residue dissipation dynamics and dietary risk assessment of emamectin benzoate, chlorantraniliprole, chlorfenapyr, and lufenuron in cabbage

Seasonal variation of metabolites in Kimchi cabbage: utilizing metabolomics based machine learning for cultivation season and taste discrimination

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