USE OF DIGITAL IMAGES TO CLASSIFY LEAF PHOSPHORUS CONTENT IN GRAPE TOMATOES

Abstract

Leaf chemical analysis is one of the ways to assess plant development. However, this type of assessment is expensive and time-consuming. The variation of nutrient content in the leaves modifies the proportion of light reflected and absorbed by plants at different wavelengths. Being able to relate the color reflected by the leaves with their phosphorus (P) content and using this data as input into an artificial neural network (ANN) can be an alternative for its determination. For this, it is necessary to establish which colors are most correlated with the different nutrients. Therefore, the phosphorus content in tomato leaves was evaluated in this study, according to four treatments (0.25, 50, 75, and 100% of the P doses). Different vegetation indices were also evaluated using images of mini-tomato leaves through a principal component analysis to determine which ones would be suitable to serve as an input to an ANN (multilayer perceptron). DGCI (Dark Green Color Index) and Bn (Normalized Blue) were the indices most related to P content. The neural network obtained 90% accuracy in the classification after training using both sides of the leaves.