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Early quantification of Tuta absoluta pest's effects in tomato plants is avery important factor in controlling and preventing serious damages of thepest. The invasion of Tuta absoluta is considered a major threat to tomatoproduction causing heavy loss ranging from 80 to 100 percent when not properlymanaged. Therefore, real-time and early quantification of tomato leaf minerTuta absoluta, can play an important role in addressing the issue of pestmanagement and enhance farmers' decisions. In this study, we propose aConvolutional Neural Network (CNN) approach in determining the effects of Tutaabsoluta in tomato plants. Four CNN pre-trained architectures (VGG16, VGG19,ResNet and Inception-V3) were used in training classifiers on a datasetcontaining health and infested tomato leaves collected from real fieldexperiments. Among the pre-trained architectures, experimental results showedthat Inception-V3 yielded the best results with an average accuracy of 87.2percent in estimating the severity status of Tuta absoluta in tomato plants.The pre-trained models could also easily identify High Tuta severity statuscompared to other severity status (Low tuta and No tuta)

A Deep Learning Approach for Determining Effects of Tuta Absoluta in Tomato Plants