Talha Akram
Papers
1
Total Citations
3
H-Index
1
About
Talha Akram’s research lies at the intersection of agricultural technology, computer vision, and machine learning, with a focus on developing intelligent systems for crop disease detection and classification. His most cited work, “An Optimized Method for Segmentation and Classification of Apple Diseases Based on Strong Correlation and Genetic Algorithm Based Feature Selection” (2020), introduces a novel framework that combines feature selection via genetic algorithms with strong correlation analysis to enhance the accuracy and efficiency of apple disease diagnosis. This approach addresses critical challenges in precision agriculture by reducing computational complexity while maintaining high classification performance. Although the paper has garnered 3 citations, its methodological innovation—particularly the integration of evolutionary algorithms with image segmentation—has laid groundwork for subsequent studies in automated plant pathology. Akram’s contributions are notable for their practical applicability, aiming to equip farmers with real-time, low-cost diagnostic tools. His work exemplifies how computational techniques can bridge the gap between data-driven research and agricultural sustainability, offering scalable solutions for food security.
Research Focus
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Top Papers
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