STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Artificial Intelligence Research & Advances Review Article

Transformative Breakthroughs: Revolutionizing Potato Disease Detection Through Machine Learning

  1. S. Shenbaha Department of Computer Science with Data Analytics, Dr. N. G. P. Arts and Science College, Coimbatore
  2. V. Sreepriya Department of Computer Science with Data Analytics, Dr. N. G. P. Arts and Science College, Coimbatore

Abstract

Advancements in agricultural technology and the integration of artificial intelligence for diagnosing plant and leaf diseases are crucial for sustainable agricultural development. Conditions like early blight and late blight exert a notable influence on both the quality and quantity of potato harvests. Identifying these leaf diseases manually demands significant labor and a considerable level of expertise. Therefore, efficient, and automated methods for disease detection are essential to improve potato production. This study introduces a model that utilizes pre-trained models like VGG16 for fine-tuning via transfer learning to extract relevant features from the dataset. The dataset is sourced from a publicly available plant database. Several trained models are employed to detect and categorize both diseased and healthy leaves. The system achieves an impressive testing accuracy of 99.23%, with 25% test data and 75% train data split. The results demonstrate that machine learning surpasses existing methods in potato disease detection. This research underscores the significance of leveraging advanced technologies in agriculture to address crucial challenges and improve productivity.

Keywords

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