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