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Automated Black Rust Detection in Wheat Using CNNs

By Rupsha Roy

Automated Black Rust Detection in Wheat Using CNNs

By Rupsha Roy

0 - Default Title
Description
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.
Product details
Binding:
Paperback
Number of Pages:
56
Release Date:
2025-08-02
Publication Date:
2025-08-02
Publisher:
LAP LAMBERT Academic Publishing
Languages:
Original: English
ISBN10:
6207843932
ISBN13:
9786207843930
GPSR Manufacturer Reference:
[email protected]
Weight:
102 g
Height:
150 cm
Width:
220 cm
Thickness:
4 cm
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