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AI InsightThis presents a* significant contribution toward the development of digital quality control systems in agricultural production.
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Özet
In this study, the relationship between color parameters and nutrient composition was examined using silage trial data conducted on 10 different plant species or mixtures by the Filik Research Lab. The dataset, comprising a total of 6,254 observations, was analyzed using big data analytics and statistical modeling techniques. Significant associations were identified between color parameters (L*, a*, b*, C*, h°, ΔE, saturation, and hue) and the physical, chemical, and sensory characteristics of the silage (crude protein, NDF, ADF, TDN, NFC). Initially, exploratory data analysis (EDA) and correlation matrices were employed to observe preliminary relationships between color and nutritional components. These were followed by classical and machine learning methods, such as multiple linear regression and Random Forest, to assess the strength and direction of these associations. The analysis revealed a* negative correlation (r = -0.45) between L* (lightness) and crude protein (CP, %), while a strong positive relationship (r = 0.52) was found between b* (yellowness) and NDF. Additionally, a meaningful correlation (r = 0.41) was observed between color saturation and TDN. Random Forest analysis confirmed these findings and highlighted L*, b*, and saturation as the most influential color parameters. These results suggest that color parameters are not only indicators of visual quality but also provide indirect insights into the chemical and nutritional content of silage. This study, conducted within the scope of big data analytics, demonstrates that color data can serve as a* rapid predictive tool, either as an alternative or complement to traditional chemical analyses. This presents a* significant contribution toward the development of digital quality control systems in agricultural production.
Abstract
In this study, the relationship between color parameters and nutrient composition was examined using silage trial data conducted on 10 different plant species or mixtures by the Filik Research Lab. The dataset, comprising a total of 6,254 observations, was analyzed using big data analytics and statistical modeling techniques. Significant associations were identified between color parameters (L*, a*, b*, C*, h°, ΔE, saturation, and hue) and the physical, chemical, and sensory characteristics of the silage (crude protein, NDF, ADF, TDN, NFC). Initially, exploratory data analysis (EDA) and correlation matrices were employed to observe preliminary relationships between color and nutritional components. These were followed by classical and machine learning methods, such as multiple linear regression and Random Forest, to assess the strength and direction of these associations. The analysis revealed a* negative correlation (r = -0.45) between L* (lightness) and crude protein (CP, %), while a strong positive relationship (r = 0.52) was found between b* (yellowness) and NDF. Additionally, a meaningful correlation (r = 0.41) was observed between color saturation and TDN. Random Forest analysis confirmed these findings and highlighted L*, b*, and saturation as the most influential color parameters. These results suggest that color parameters are not only indicators of visual quality but also provide indirect insights into the chemical and nutritional content of silage. This study, conducted within the scope of big data analytics, demonstrates that color data can serve as a* rapid predictive tool, either as an alternative or complement to traditional chemical analyses. This presents a* significant contribution toward the development of digital quality control systems in agricultural production.