Embedded AI for Wheat Yellow Rust Infection Type Classification
Author:
Affiliation:
1. School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan
2. Crop Sciences Institute, National Agricultural Research Centre, Islamabad, Pakistan
Funder
National Center for Artificial Intelligence (NCAI), National University of Sciences and Technology (NUST), Islamabad, Pakistan
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10063855.pdf?arnumber=10063855
Reference36 articles.
1. Leaf-Rust and Nitrogen Deficient Wheat Plant Disease Classification using Combined Features and Optimized Ensemble Learning
2. Assessing the Impact of Segmentation on Wheat Stripe Rust Disease Classification Using Computer Vision and Deep Learning
3. Automating yellow rust disease identification in wheat using artificial intelligence
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4. A spatiotemporal collaborative approach for precise crop planting structure mapping based on multi-source remote-sensing data;International Journal of Remote Sensing;2023-06-19
5. Enhancing Wheat Crop Resilience: An Efficient Deep Learning Framework for the Detection and Classification of Rust Disease;2023 4th International Conference for Emerging Technology (INCET);2023-05-26
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