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This paper presents a the development and optimization of a Convolutional Neural Network (CNN) model, designed for Tomato Plant disease and pest Classification (TPC_Net). Using images from the PlantVillage and TomatoVillage datasets, the model was trained on a balanced subset created through augmentation techniques to enhance accuracy and generalizability. We compared TPC_Net with established models adapted for tomato disease classification, demonstrating its superior accuracy, precision, and recall in identifying 11 distinct classes of diseases and pests. The model’s streamlined architecture facilitates deployment in mobile applications, promising significant advancements in agricultural technology for effective disease management.

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TPC_Net: An Efficient CNN Architecture for Tomato Plant Disease and Pest Classification

  • 会议论文

  • 发表日期：

    2025年

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  • Ovidiu Cosma,
  • Laura Cosma

摘要

This paper presents a the development and optimization of a Convolutional Neural Network (CNN) model, designed for Tomato Plant disease and pest Classification (TPC_Net). Using images from the PlantVillage and TomatoVillage datasets, the model was trained on a balanced subset created through augmentation techniques to enhance accuracy and generalizability. We compared TPC_Net with established models adapted for tomato disease classification, demonstrating its superior accuracy, precision, and recall in identifying 11 distinct classes of diseases and pests. The model’s streamlined architecture facilitates deployment in mobile applications, promising significant advancements in agricultural technology for effective disease management.