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Precision Tomato Disease Detection with a Hybrid Deep Learning Model Based on VGG-16 and ResNet-50

  • Unique Paper ID: 168771
  • Volume: 11
  • Issue: 5
  • PageNo: 2284-2291
  • Abstract:
  • One of the most significant crops in the world, tomato plants are widely cultivated in different regions of India. Tomato crops, however, are extremely vulnerable to a number of illnesses that can result in a significant loss of output. In this work, we suggest a deep ensemble learning method for tomato plant disease detection that makes use of VGG-16 and ResNet-50. In order to automatically extract and learn discriminative features from tomato leaf images and categorize them into healthy or diseased groups, the suggested method makes use of the advantages of both CNN architectures. We used a publicly accessible dataset of tomato leaf photos with five distinct disease classifications to assess the efficacy of the method. To increase its size and diversity, the dataset underwent pre-processing and augmentation. The ensemble model underwent testing and training on dataset to achieving an overall accuracy of 95.78% on the test set. Furthermore, we compared the performance of the proposed deep ensemble model with other state-of-the-art classification techniques, demonstrating that the ensemble model outperformed them. This deep ensemble-based tomato plant disease detection system can serve as a valuable tool for farmers and researchers, enabling the timely identification of diseased crops and helping to mitigate crop yield loss by preventing disease spread.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 5
  • PageNo: 2284-2291

Precision Tomato Disease Detection with a Hybrid Deep Learning Model Based on VGG-16 and ResNet-50

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