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The deployment and performance of deep learning algorithms are improving the advancement in agricultural sector. Plant diseases significantly impact agricultural productivity, necessitating rapid and accurate detection systems. This research presents a deep learning-based Convolutional Neural Network (CNN) for classifying plant diseases using leaf images. The model extracts hierarchical features through multiple convolutional layers and performs classification using fully connected layers. Performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrices. The proposed approach achieves high accuracy, demonstrating its effectiveness in early plant disease detection, which can aid farmers in implementing timely interventions. By this CNN approach we can conduct the early detection with the accurate remedy for the diseased plants.

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Deep Learning-Based Plant Disease Detection: A CNN Approach for Accurate Diagnosis

  • 会议论文

  • 发表日期：

    2026年

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  • Shubhankar Karajkhede,
  • Divya Tambe,
  • Swaraj Zende,
  • Sanjay T. Gandhe

摘要

The deployment and performance of deep learning algorithms are improving the advancement in agricultural sector. Plant diseases significantly impact agricultural productivity, necessitating rapid and accurate detection systems. This research presents a deep learning-based Convolutional Neural Network (CNN) for classifying plant diseases using leaf images. The model extracts hierarchical features through multiple convolutional layers and performs classification using fully connected layers. Performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrices. The proposed approach achieves high accuracy, demonstrating its effectiveness in early plant disease detection, which can aid farmers in implementing timely interventions. By this CNN approach we can conduct the early detection with the accurate remedy for the diseased plants.