Deep transfer learning based disease detection and classification of tomato leaves - a comparative analysis

Munira Akter Lata, Marjia Sultana, Iffat Ara Badhan, Mastura Jahan Maria, Fariha Tasnim Nuha

Abstract


A wide variety of diseases have a significant impact on tomato plants. To avoid crop quality issues, a prompt and precise diagnosis is crucial. Classifying plant diseases is one of the numerous applications where deep transfer learning models have recently produced remarkable results. This study dealt with fine-tuning by contrasting the most advanced architectures, including Inception V3, ResNet-18, ResNet-50, VGG-16, VGG-19, GoogLeNet, and AlexNet. In the end, a comparison evaluation is conducted. Nine distinct tomato disease classes and one healthy class from PlantVillage make up the dataset used in this study. Precision, recall, F1-score, and accuracy were the basis for a multiclass statistical analysis that assessed the models. The ResNet-50 approach yielded significant results with precision: 82%, recall: 81%, F1-score: 81%, and accuracy: 85%. With this high success rate, it is reasonable to say that mobile applications or IoT-compatible gadgets implemented with the ResNet-50 model can assist farmers in identifying and safeguarding tomatoes against the aforementioned diseases.

Keywords


classification; deep transfer learning; image processing; occlusion sensitivity; tomato leaf diseases;

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DOI: http://doi.org/10.12928/telkomnika.v23i5.26887

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TELKOMNIKA Telecommunication, Computing, Electronics and Control
ISSN: 1693-6930, e-ISSN: 2302-9293
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