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Leaf disease detection is an important aspect of modern agriculture, aiding timely identification and management of plant health. It has been observed that fungal disease covers major part of total diseases. This study proposes a novel and comprehensive approach called “Foliage Guardian” for detecting leaf diseases using Convolutional Neural Networks (CNN). The proposed system employs deep learning to examine leaf images and exactly diagnosis diverse fungal diseases. The main idea is to have a form of cure that can detect many different kinds of the condition providing a multi-purpose tool to the farmers for their plant health management. This technique involves training CNN’s on datasets with various leaf diseases. The model is designed to learn complex patterns and traits associated with each disease, thus allowing it to perform well on new data examples. Post-training phase methods are used to further improve model robustness.

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Revolutionizing Plant Health: An Efficient CNN-Based Approach to Comprehensive Leaf Disease Diagnosis

  • 会议论文

  • 发表日期：

    2025年

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  • Kiran Dhangar,
  • Parneeta Dhaliwal

摘要

Leaf disease detection is an important aspect of modern agriculture, aiding timely identification and management of plant health. It has been observed that fungal disease covers major part of total diseases. This study proposes a novel and comprehensive approach called “Foliage Guardian” for detecting leaf diseases using Convolutional Neural Networks (CNN). The proposed system employs deep learning to examine leaf images and exactly diagnosis diverse fungal diseases. The main idea is to have a form of cure that can detect many different kinds of the condition providing a multi-purpose tool to the farmers for their plant health management. This technique involves training CNN’s on datasets with various leaf diseases. The model is designed to learn complex patterns and traits associated with each disease, thus allowing it to perform well on new data examples. Post-training phase methods are used to further improve model robustness.