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Automated Black Rust Detection in Wheat Using CNNs: Advanced CNN-Based Approach for Early Detection and Management of Black Rust in Wheat - Softcover
Synopsis
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.
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Automated Black Rust Detection in Wheat Using CNNs
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LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6207843932
ISBN 13: 9786207843930
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Automated Black Rust Detection in Wheat Using CNNs
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LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6207843932
ISBN 13: 9786207843930
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Automated Black Rust Detection in Wheat Using CNNs: Advanced CNN-Based Approach for Early Detection and Management of Black Rust in Wheat
Published by
LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6207843932
ISBN 13: 9786207843930
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Softcover
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Automated Black Rust Detection in Wheat Using CNNs
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Omniscriptum, LAP Lambert Academic Publishing, 2025
ISBN 10: 6207843932
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 56 pp. Englisch. Seller Inventory # 9786207843930
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Automated Black Rust Detection in Wheat Using CNNs : Advanced CNN-Based Approach for Early Detection and Management of Black Rust in Wheat
Published by
LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6207843932
ISBN 13: 9786207843930
New
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Seller: AHA-BUCH GmbH, Einbeck, Germany
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Seller Inventory # 9786207843930
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Automated Black Rust Detection in Wheat Using CNNs
Published by
LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6207843932
ISBN 13: 9786207843930
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Automated Black Rust Detection in Wheat Using CNNs
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ISBN 10: 6207843932
ISBN 13: 9786207843930
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Automated Black Rust Detection in Wheat Using CNNs (Paperback)
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LAP Lambert Academic Publishing, 2025
ISBN 10: 6207843932
ISBN 13: 9786207843930
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Paperback. Condition: new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9786207843930
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Automated Black Rust Detection in Wheat Using CNNs
Published by
LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6207843932
ISBN 13: 9786207843930
New
Softcover
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Automated Black Rust Detection in Wheat Using CNNs
Published by
LAP LAMBERT Academic Publishing Aug 2025, 2025
ISBN 10: 6207843932
ISBN 13: 9786207843930
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Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 56 pp. Englisch. Seller Inventory # 9786207843930