Precision in Rice Variety Classification using Stacking-Based Ensemble Learning
Rice, a staple food for a significant portion of the global population, exhibits remarkable diversity in its varieties, presenting substantial challenges for accurate identification by consumers, traders, and farmers. This complexity often facilitates fraudulent practices, such as the unauthorized mixing of rice types, which undermines quality and trust in the supply chain. Despite its critical importance, existing research falls short of providing robust and efficient methods for precise rice variety classification based on external characteristics like color, size, and texture. To address th
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- PossiblePossibly related (embedding) · 45%A deep learning optimized model for classification and detection of rice leaf diseases - Nature →
- FuzzySimilar title/name (fuzzy) · 84%amitness/learning →
“Fuzzy title match (0.92): “Precision in Rice Variety Classification using Stacking-Base” ≈ “amitness/learning””
- LinkedLinked via arxiv author · 85%Md. Masudul Islam →
“Precision in Rice Variety Classification using Stacking-Based Ensemble Learning”
- LinkedLinked via arxiv author · 85%Galib Muhammad Shahriar Himel →
“Precision in Rice Variety Classification using Stacking-Based Ensemble Learning”
- LinkedLinked via arxiv author · 85%Md. Golam Moazzam →
“Precision in Rice Variety Classification using Stacking-Based Ensemble Learning”
- LinkedLinked via arxiv author · 85%Mohammad Shorif Uddin →
“Precision in Rice Variety Classification using Stacking-Based Ensemble Learning”