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

Munira Akter Lata (University of Frontier Technology)
Marjia Sultana (Begum Rokeya University)
Iffat Ara Badhan (Begum Rokeya University)
Mastura Jahan Maria (University of Frontier Technology)
Fariha Tasnim Nuha (University of Frontier Technology)

TELKOMNIKA (Telecommunication Computing Electronics and Control)
Vol 23, No 5: October 2025

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.