Development of model for early identification of tomato plant damages caused by TUTA ABSOLUTA

Abstract

Plant pests and diseases challenge the agricultural sector. A high-yielding crop, such as tomato which has the potential to increase income of smallholder farmers, its production is threatened by invasive pest called Tuta absoluta. Despite many efforts made by farmers in its management, has continued to be a great constraint, hence calling for scholars to devise approaches of identifying and combating it before causing great losses to farmers. This study introduces, a deep learning based approach for the identification of the pest at early stages of tomato growth through classification of tomato leaf images. In this study, the Convolutional Neural Network architectures (VGG16, VGG19 and ResNet50) were trained on tomato images dataset captured from the field containing healthy and infested tomato leaves. Evaluation of performance for each classifier was done by considering accuracy of classifying the tomato leaf into correct category. Experimental results showed that VGG16 attained the highest accuracy of 91.9% in classifying tomato plant leaves into correct categories. This model can be deployed and used to establish tool for early detection of Tuta absoluta pest