Analysis of the relationship between leaf color spectrum and soil plant analysis development. Issue 1 (November 2021) - Zainuddin, Bunyamin; Aqil, Muhammad
Abstract: Assessment nutrient content of maize leaf is particularly important in achieving higher grain yield. Characterization of leaf chlorophyll involves routine Soil Plant Analyzer Development (SPAD) reading particularly at critical stage of growth development. The objective of the study was to assess the color spectrum of maize leaf in relation to the chlorophyll content by using Random-forest modeling. genotypes of corn plants based on the characters of the ear and kernel using a logistic regression model. The research was conducted at IP2TP Bajeng in 2021 by planting maize varieties at various fertilizer level. RGB data of maize leaf was recorded by using Hamamatsu sensor (Hamamatsu, Japan), and converted to HIS, HSV and LAB color spectrum. The results indicated that Random-forest model with 20-fold validation indicated the highest accuracy as compared to the other fold-range. Among the tested model, integration of Random-forest model to LAB (Light, red/green coordinate, and the yellow/blue coordinate) color spectrum provided the best model performances with RMSE (4.77), MSE (22.76), MAE (3.80) and R 2 (0.853). This value indicates that the use of Hamamatsu color sensor and converted into LAB color spectrum provided the best SPAD (Soil Plant Analyzer Development) reading with high accuracy and consistency of results. Thus, digital based model can be integrated with manual selection for fast and precise nutrient monitoring.