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

Journal of Artificial Intelligence Research & Advances

Machine Learning Based Approaches for the Detection and Classification of Crop Leaf Diseases

  1. Gajal Walia
  2. Neha Bathla

Abstract

Crop leaf diseases pose a significant threat to agricultural productivity worldwide, leading to substantial crop losses and economic consequences. Timely and precise identification and categorization of these diseases are vital for effective disease management and safeguarding crops. Over the past years, machine learning methods have gained prominence due to their potential to automate disease diagnosis and classification procedures. This review paper presents an overview of the various machine learning based techniques employed for the diagnosis and classification of crop leaf diseases. We discuss the fundamental concepts and methodologies of machine learning algorithms used in this context, along with their strengths and limitations. Furthermore, we analyze and compare the performance of different machine learning techniques reported in the literature and highlight the key factors influencing their effectiveness. Lastly, we pinpoint the present challenges and future research avenues to drive progress in this domain.

Keywords

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