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In India, potatoes are one of the key agricultural crops, and their cultivation has gained immense popularity over the past few years. However, potato crop is facing challenges due to various diseases, which are driving up costs for farmers and disrupting their livelihoods. To address this issue, we propose an automated and rapid disease detection system aimed at enhancing potato production and digitizing agricultural practices. This paper presents an ML-based automated system for identifying and classifying potato leaf diseases. Image processing emerges as an effective solution for detecting and analyzing these ailments. In this paper, we conducted a ternary classification on nearly 1500 images with three possible outcomes: early blight, late blight, and healthy leaf. The project takes an image as input and classifies it to detect the plant’s disease.

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Harnessing Machine Learning for Early Detection of Potato Leaf Diseases

  • 会议论文

  • 发表日期：

    2025年

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  • Rabins Porwal,
  • Arpit Dubey,
  • Arpita Singh,
  • Anil Kumar Yadav

摘要

In India, potatoes are one of the key agricultural crops, and their cultivation has gained immense popularity over the past few years. However, potato crop is facing challenges due to various diseases, which are driving up costs for farmers and disrupting their livelihoods. To address this issue, we propose an automated and rapid disease detection system aimed at enhancing potato production and digitizing agricultural practices. This paper presents an ML-based automated system for identifying and classifying potato leaf diseases. Image processing emerges as an effective solution for detecting and analyzing these ailments. In this paper, we conducted a ternary classification on nearly 1500 images with three possible outcomes: early blight, late blight, and healthy leaf. The project takes an image as input and classifies it to detect the plant’s disease.