CLASSIFICATION OF THE NUTRITIONAL CONDITION OF BEAN PLANTS (Phaseolus Vulgaris) USING CONVOLUTIONAL NEURAL NETWORKS AND IMAGE ANALYSIS

Abstract

Agriculture plays an essential role in Brazil, especially in the production of beans (Phaseolus vulgaris), an important source of plant protein. In this study, a convolutional neural network (CNN) model was developed to classify the nutritional status of the bean plant focusing on nitrogen (N) content, using RGB images. The experiment was conducted at USP, in Pirassununga, with five nitrogen fertilization treatments and 30 bean plant pots. Weekly images of the leaves were captured starting from 30 days after emergence (DAE). The images were processed and used to train and test different CNN configurations. The results indicated that larger sets of images and smaller blocks (10x10 pixels) increased accuracy, especially at 37 DAE. It is concluded that the proposed model is effective for nutritional monitoring, providing an efficient alternative to traditional leaf analysis.