Crop Disease Prediction by Machine Learning

Year : 2024 | Volume : 11 | Issue : 02 | Page : 21 25
    By

    Sanika Deshmukh,

  • Yash Katkar,

  • Atharva Ubale,

  • Mangesh Kondhalkar,

  • Santosh S Shinde,

  1. Student, Department of Computer Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India
  2. Student, Department of Computer Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India
  3. Student, Department of Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India
  4. Student, Department of Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India
  5. Student, Department of Engineering, Savitribai Phule Pune University, Maharashtra, India

Abstract

The classification of Crop can be classified into several methods. The data set of crop leaf illnesses, notably Bacterial Leaf Blight disease (BLB), a crop leaf disease with significant outbreaks throughout Thailand, and Brown Spot Crop disease (BSR), is classified employing image classification in this study. Additionally, image processing technology is used for identifying different types of crop leaf disease. These algorithms include the Random Forest, Decision Tree, Gradient Boost, and Naive-Bayes algorithms, and their accuracy, precision, and recall are tested. A number of image processing techniques and classification algorithms are investigated, such as Naive Bayes, Random Forest, Decision Tree, and Gradient Boosting. Metrics like accuracy, precision, and recall are used to gauge how well each algorithm performs in terms of differentiating between various crop leaf diseases. The best result of performance in the image classification of Crop leaf diseases is CNN algorithm equal to69.44 percent. Crop diseases pose significant threats to global food security by affecting crop yield and quality. Early and accurate detection of these diseases is crucial for timely intervention and effective management. Machine learning (ML) techniques have emerged as powerful tools in agricultural research, offering predictive capabilities that can assist farmers in identifying and mitigating crop diseases promptly. This research article explores various ML approaches used for crop disease prediction, highlighting their benefits, challenges, and future directions

Keywords: Crop leaf disease: algorithm, image processing, classification, cnn algorithm.

[This article belongs to Trends in Machine design ]

How to cite this article:
Sanika Deshmukh, Yash Katkar, Atharva Ubale, Mangesh Kondhalkar, Santosh S Shinde. Crop Disease Prediction by Machine Learning. Trends in Machine design. 2024; 11(02):21-25.
How to cite this URL:
Sanika Deshmukh, Yash Katkar, Atharva Ubale, Mangesh Kondhalkar, Santosh S Shinde. Crop Disease Prediction by Machine Learning. Trends in Machine design. 2024; 11(02):21-25. Available from: https://journals.stmjournals.com/tmd/article=2024/view=183611


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Regular Issue Subscription Review Article
Volume 11
Issue 02
Received 27/05/2024
Accepted 06/08/2024
Published 16/11/2024
174

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