Methods Based on Machine Learning for Large-Scale Classification of Crop Leaf Diseases

Open Access

Year : 2024 | Volume :02 | Issue : 01 | Page : –
By

Gajal Walia

Neha Bathla

  1. Student Department of Computer Science & Engineering, Yamuna Institute of Engineering & Technology (YIET), Gadholi, Yumnanagar Haryana India
  2. Assistant Professor Department of Computer Science & Engineering, Yamuna Institute of Engineering & Technology (YIET), Gadholi, Yamunagar Haryana India

Abstract

Worldwide productivity of crops is seriously threatened by crop leaf diseases, which can result in large crop losses and negative economic effects. Effective disease management and crop protection depend on the early and precise detection and classification of these illnesses. Machine learning approaches have gained popularity recently due to their ability to automate procedures related to illness diagnosis and classification. An overview of the several machine learning-based methods used for crop leaf disease diagnosis and classification is provided in this review paper. We go over the basic ideas and methods of the machine learning algorithms that are applied here, as well as their advantages and disadvantages. Additionally, we examine and contrast the results of several machine learning approaches published in the literature, emphasizing the critical elements affecting their efficacy. Ultimately, we pinpoint the present obstacles and forthcoming research avenues to promote progress in this domain

Keywords: Machine Learning, CNN, Transfer Learning, SVM, Random Forest, Deep learning

[This article belongs to International Journal of Computer Science Languages(ijcsl)]

How to cite this article: Gajal Walia, Neha Bathla. Methods Based on Machine Learning for Large-Scale Classification of Crop Leaf Diseases. International Journal of Computer Science Languages. 2024; 02(01):-.
How to cite this URL: Gajal Walia, Neha Bathla. Methods Based on Machine Learning for Large-Scale Classification of Crop Leaf Diseases. International Journal of Computer Science Languages. 2024; 02(01):-. Available from: https://journals.stmjournals.com/ijcsl/article=2024/view=145668

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Regular Issue Open Access Review Article
Volume 02
Issue 01
Received April 15, 2024
Accepted April 18, 2024
Published May 9, 2024
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