PLANT DISEASE PREDICTION USING CONVOLUTIONAL NEURAL NETWORK

  • Akshalin Jefita RJ Final Year UG Scholars, Department of Computer Science and Engineering, Hindusthan College of Engineering and Technology, Coimbatore, India
  • Deepti S Final Year UG Scholars, Department of Computer Science and Engineering, Hindusthan College of Engineering and Technology, Coimbatore, India
  • Indhumathi M Final Year UG Scholars, Department of Computer Science and Engineering, Hindusthan College of Engineering and Technology, Coimbatore, India
  • Muthu Petchi G Final Year UG Scholars, Department of Computer Science and Engineering, Hindusthan College of Engineering and Technology, Coimbatore, India
  • Magesh D Assistant Professor, Department of Computer Science and Engineering, Hindusthan College of Engineering and Technology, Coimbatore, India
DOI: https://doi.org/10.53555/cse.v11i1.6324
Keywords: Plant Disease, Machine Learning, Ensemble Learning, Random Forest, Naive Bayes, SVM, Agriculture

Abstract

Plant diseases significantly impact global agriculture, causing economic losses, reduced yields, and food insecurity. Early and precise detection is crucial for effective disease management and sustainable farming. This research introduces a deep learning-based plant disease prediction system using a Convolutional Neural Network (CNN), a powerful image processing algorithm. The CNN model, trained on leaf image datasets, enables automated, real-time disease diagnosis with 99.20% accuracy. By extracting intricate features, it enhances predictive precision, aiding farmers and agricultural experts in early detection, minimizing crop damage, and reducing dependence on chemical treatments, thereby promoting healthier and more sustainable crop production.

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Published
2025-06-16
How to Cite
Akshalin Jefita RJ, Deepti S, Indhumathi M, Muthu Petchi G, & Magesh D. (2025). PLANT DISEASE PREDICTION USING CONVOLUTIONAL NEURAL NETWORK. IJRDO -Journal of Computer Science Engineering, 11(1), 20-26. https://doi.org/10.53555/cse.v11i1.6324
Issue
Vol. 11 No. 1 (2025): IJRDO - Journal of Computer Science Engineering (ISSN: 2456-1843)
Section
Articles

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