Quality Attributes Prediction of Flame Seedless Grape Clusters Based on Nutritional Status Employing Multiple Linear Regression Technique

Flame Seedless grape is considered one of the most popular and favorite grapes for consumers, since it ripens early, has good cluster quality. seedless marketing value depends upon its desirable appearance, berry, size, shape. Therefore, imperative that yield quality are enhanced to ensure profitability. In this study, prediction physical characteristics clusters berries’ color attributes grown under different culture practices, in particular fertilization treatments, was carried out using nutritional status concentration (leaf mineral elements, total chlorophyll content, carotenoids content) multiple linear regression (MLR). The method based on development two indices: first called index 1 (%) formulated by combing elements N, P, K, Ca, Mg concentrations; second 2 (ppm) Fe, Cu, Mn, Zn, B concentrations leaf petioles. results indicated established MLR models can obtain variation accuracy, values coefficients determination (R2) test set. R2 were range 0.9286 0.9972 weight, length, shoulder (L*, a*, b*, chroma, hue, red (CIRG)). This study highlighted during a season, coupled with model be used successfully evaluate grape. easy, fast reliable as retains appearance fruits adjusting leaves. Moreover, had greatest weight all predicted attributes.