Abstract :

Rice Is One Of The Most Widely Consumed Staple Foods Across The Globe, Especially In Asian Countries. Accurate Identification Of Rice Varieties Is Essential For Improving Quality Control, Seed Selection, Supply Chain Management, And Ensuring Food Authenticity. Traditional Methods Of Rice Classification, Often Relying On Manual Observation, Are Time-consuming, Error-prone, And Inefficient For Large-scale Applications. To Address These Challenges, This Project Titled "Rice Varieties Identification Using Deep Learning" Proposes An Automated, Highly Accurate Classification System Utilizing State-of-the-art Deep Learning Techniques. The System Is Developed Using Python For Backend Logic, With A User-friendly Web Interface Built Using HTML, CSS, And JavaScript, And Deployed Using The Flask Web Framework. Two Powerful Convolutional Neural Network Models, DenseNet121 And MobileNet, Are Implemented And Evaluated. The DenseNet-121 Model Achieved A Training Accuracy Of 99.0% And A Test Accuracy Of 99.3%, While The Lightweight MobileNet Model Outperformed With A Training Accuracy Of 99.4% And A Test Accuracy Of 99.5%, Making It An Ideal Candidate For Real-time, Resource-efficient Applications.The Dataset Used Consists Of 60,000 Rice Grain Images, Equally Distributed Across Five Classes: Arborio, Basmati, Ipsala, Jasmine, And Karacadag, With 12,000 Images Per Class For Training. The Data Preparation Phase Involved Systematic Steps Including Defining Data Directories, Setting Uniform Image Dimensions, Image Rescaling, Structured Loading, And Preprocessing, Ensuring High Model Generalization And Accuracy.The Results Demonstrate That The Proposed Deep Learningbased System Can Reliably Distinguish Between Closely Related Rice Varieties With High Precision. The Project Not Only Contributes To Agricultural Digitalization But Also Paves The Way For Scalable Deployment In Food Quality Assurance, Automated Grain Sorting, And Supply Chain Monitoring Applications

Published:

27-3-2026

Issue:

Vol. 26 No. 3 (2026)

Page Nos:

769-776

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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