Embedded AI for Wheat Yellow Rust Infection Type Classification

Author:

Shafi Uferah1ORCID,Mumtaz Rafia1ORCID,Qureshi Muhammad Deedahwar Mazhar1ORCID,Mahmood Zahid2ORCID,Tanveer Sikander Khan2,Haq Ihsan Ul1ORCID,Zaidi Syed Mohammad Hassan1ORCID

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

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Efficient Disease Detection in Wheat Crops: A Hybrid Deep Learning Solution;2023 3rd International Conference on Technological Advancements in Computational Sciences (ICTACS);2023-11-01

2. Wheat Diseases Recognition Using Optimal Features Assisted Modified Soft Attention Network;2023 14th International Conference on Information and Communication Technology Convergence (ICTC);2023-10-11

3. Integrated digital image processing techniques and deep learning approaches for wheat stripe rust disease detection and grading;Decision Analytics Journal;2023-09

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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