Enhancing Disease Management in Mango Cultivation: A Machine Learning Approach to Classifying Leaf Diseases

  • Gst. Ayu Vida Mastrika Giri Universitas Udayana
  • Izmy Alwiah Musdar UIN Alauddin Makassar
  • Husni Angriani STMIK Kharisma Makassar
  • Medi Taruk Universitas Mulawarman

Keywords: Machine Learning, Mango Leaf Diseases, Gradient Boosting Classifier, Image Segmentation, Precision Agriculture

Abstract

This study explores the application of machine learning techniques in the agricultural domain, focusing on the classification of two common diseases in mango leaves: Powdery Mildew and Sooty Mould. Utilizing the MangoLeafBD dataset, the research employs a Gradient Boosting Classifier, enhanced with mean shift image segmentation and Hu moments for feature extraction. The performance of the model was rigorously evaluated through 5-fold cross-validation, yielding insights into its accuracy, precision, recall, and F1-score. The results demonstrate moderate success, with the highest accuracy and precision observed in the initial fold, indicating the model's potential for reliable disease identification. The study addresses the challenge of distinguishing between diseases with similar symptomatic appearances, offering a novel, data-driven approach for disease management in mango cultivation. This research contributes to the growing field of precision agriculture, highlighting the potential of machine learning in enhancing disease diagnosis and treatment strategies, thus supporting sustainable agricultural practices.

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Published
2023-12-31
How to Cite
Mastrika Giri, G. A. V., Musdar, I. A., Angriani, H., & Taruk, M. (2023). Enhancing Disease Management in Mango Cultivation: A Machine Learning Approach to Classifying Leaf Diseases. Indonesian Journal of Data and Science, 4(3), 160-168. https://doi.org/10.56705/ijodas.v4i3.111