Browsing by Author "Mkonyi, Lilian"

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    Development of model for early identification of tomato plant damages caused by TUTA ABSOLUTA
    (NM-AIST, 2021-10) Mkonyi, Lilian
    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
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    Early identification of Tuta absoluta in tomato plants using deep learning
    (Elsevier B.V., 2020-11) Mkonyi, Lilian; Rubanga, Denis; Richard, Mgaya; Zekeya, Never; Sawahiko, Shimada; Maiseli, Baraka; Machuve, Dina
    The 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 small- holder farmers income when its health is maintained. This work introduces an approach to strengthen phytosanitary capacity and systems to help solve tomato plant pest Tuta ab- soluta devastation 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 high- est accuracy of 91.9% in classifying tomato plant leaves into correct categories. Our model may be used to establish methods for early detection of Tuta absoluta pest invasion at early tomato growth stages, hence assisting farmers overcome yield losses.