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The project addresses a critical challenge in Indian agriculture: plant disease detection. It goes beyond simply identifying diseases by proposing a system that categorizes them by severity using statistical farming data. This additional information can be crucial for farmers in determining the most appropriate course of action. The project explores Convolutional Neural Networks (CNNs) – specifically ResNet50, ResNet-152v2, and Inceptionv3 – as a deep learning approach for disease identification. By analyzing large datasets of plant images, the project aims to develop a highly effective disease detection system. This system has the potential to revolutionize agricultural practices in India by offering real-time disease detection and surpassing the capabilities of existing methods. The possibility of integrating the system with sensors further enhances its potential for comprehensive farm monitoring.

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GreenWatch: Revolutionizing Farming with Plant Disease Detection by CNN

  • 会议论文

  • 发表日期：

    2026年

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  • Aadithya Vikram Budarapu,
  • John Hyde Gaddam,
  • Ram Boyedi,
  • Thoshan Kumar Muthyala,
  • Sunanda Yadla

摘要

The project addresses a critical challenge in Indian agriculture: plant disease detection. It goes beyond simply identifying diseases by proposing a system that categorizes them by severity using statistical farming data. This additional information can be crucial for farmers in determining the most appropriate course of action. The project explores Convolutional Neural Networks (CNNs) – specifically ResNet50, ResNet-152v2, and Inceptionv3 – as a deep learning approach for disease identification. By analyzing large datasets of plant images, the project aims to develop a highly effective disease detection system. This system has the potential to revolutionize agricultural practices in India by offering real-time disease detection and surpassing the capabilities of existing methods. The possibility of integrating the system with sensors further enhances its potential for comprehensive farm monitoring.