摘要：Highlights•Deep learning models for identification of Tuta absoluta in tomato plant were developed.•A dataset of 2,145 images collected from field experiment were used to train and test the models.•Methodology used transfer learning pre-trained models for classification.•The best model achieved and accuracy of 91.9% on 66 testing images.AbstractThe agricultural sector is highly challenged by plant pests and diseases. A high–yielding crop, such as tomato with high economic returns, can greatly increase the income of smallholder farmers income when its health is maintained. This work introduces an approach to strengthen phytosanitary capacity and systems to help solve tomato plant pestTuta absolutadevastation at early tomato growth stages. We present a deep learning approach to identify tomato leaf miner pest (Tuta absoluta) invasion. The Convolutional Neural Network architectures (VGG16, VGG19, and ResNet50) were used in training classifiers on tomato image dataset captured from the field containing healthy and infested tomato leaves. We evaluated performance of each classifier by considering accuracy of classifying the tomato canopy into correct category. Experimental results show that VGG16 attained the highest accuracy of 91.9% in classifying tomato plant leaves into correct categories. Our model may be used to establish methods for early detection ofTuta absolutapest invasion at early tomato growth stages, hence assisting farmers overcome yield losses.
