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One of the most significant crops grown in India is the tomato. Many deep learning models have found widespread application in the precise categorization of various tomato diseases. The deep learning plant pathology models are based on the popular convolutional neural network architectures such as Inception v3, DenseNet121, and ResNet50. This paper aims to improve the prediction accuracy of these three neural networks by using them with the global color constancy approach known as color opponency space (COS), employing hue, saturation, and value. Furthermore, these three models are applied in combination approaches using ensemble learning techniques such as soft voting and weighted voting to find the best-performing combination in terms of accuracy. Inception v3 with COS, DenseNet121 without COS, and ResNet50 with COS are the recommended configurations. This combination achieves 97.74% accuracy, which is higher than any other combination of these three models. This approach demonstrates the potential of hybrid ensemble-CNN frameworks in elevating plant disease classification accuracy for real-world agricultural applications.

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Optimizing Tomato Disease Classification Using Deep Learning Ensemble Approach with Color Opponency Space

  • 会议论文

  • 发表日期：

    2026年

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  • Gurpreet Singh,
  • Sandeep Sharma

摘要

One of the most significant crops grown in India is the tomato. Many deep learning models have found widespread application in the precise categorization of various tomato diseases. The deep learning plant pathology models are based on the popular convolutional neural network architectures such as Inception v3, DenseNet121, and ResNet50. This paper aims to improve the prediction accuracy of these three neural networks by using them with the global color constancy approach known as color opponency space (COS), employing hue, saturation, and value. Furthermore, these three models are applied in combination approaches using ensemble learning techniques such as soft voting and weighted voting to find the best-performing combination in terms of accuracy. Inception v3 with COS, DenseNet121 without COS, and ResNet50 with COS are the recommended configurations. This combination achieves 97.74% accuracy, which is higher than any other combination of these three models. This approach demonstrates the potential of hybrid ensemble-CNN frameworks in elevating plant disease classification accuracy for real-world agricultural applications.