Automated Black Rust Detection in Wheat Using CNNs - Roy, Rupsha;Mondal, Saptarshi
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This research introduces a highly accurate CNN-based model (99.9% accuracy) for early detection of black rust in wheat using image analysis. The model was trained on a diverse, region-specific dataset, ensuring robust performance across varying agro-climatic conditions. It enables early-stage disease detection, reducing yield loss, optimizing fungicide use, and promoting sustainable farming practices. The system is lightweight, deployable on smartphones, and integrates with digital farming ecosystems, empowering farmers with accessible AI tools. Its scalability and compatibility with IoT and…mehr

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Produktbeschreibung
This research introduces a highly accurate CNN-based model (99.9% accuracy) for early detection of black rust in wheat using image analysis. The model was trained on a diverse, region-specific dataset, ensuring robust performance across varying agro-climatic conditions. It enables early-stage disease detection, reducing yield loss, optimizing fungicide use, and promoting sustainable farming practices. The system is lightweight, deployable on smartphones, and integrates with digital farming ecosystems, empowering farmers with accessible AI tools. Its scalability and compatibility with IoT and cloud platforms position it as a vital step toward precision agriculture and national food security.
Autorenporträt
Rupsha Roy, B.Sc (Hons) Agriculture 3rd year student at Adamas University, focuses on climate-resilient farming. Saptarshi Mondal, B.Tech CSE (AIML) 3rd year student at Adamas University, has published a Springer paper on AI for disabled assistance. Both collaborate Automated Black Rust Detection in Wheat using CNNs.