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Wheat rust disease detection techniques: a technical perspective

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Abstract

Agriculture sector is the second largest sector of Pakistan, which contributes 17.9% of the total Gross Domestic Production. The productivity of major crops such as wheat, maize, rice, sugarcane, and cotton is central to the economic development of the country. Among these crops, wheat is considered as a staple crop of Pakistan, covering almost nine million hectares of the cultivated land. However, its productivity rate is severely affected by rust disease, which is an airborne fungal disease caused by a group of fungi from Pucciniales order. This disease has the ability to decrease the wheat production rate up to 30% and destroy the crop within a month after its first assault, thus posing a serious threat to food security. Owing to conventional farming practices, there is a recurring concern for an early arrest of this disease to minimize the yield losses and to feed the growing population. Several precision farming solutions are available worldwide to timely identify the rust attack, mitigate its catastrophic effects, and invoke the remedial action in a site-specific manner. In this paper, the technical review of cutting edge techniques used for detecting wheat rust attacks is presented, which include Remote Sensing, Machine Learning, Deep Learning, and Internet of Things. Additionally, the challenges and limitations associated with these techniques are discussed to highlight the practical implications of implementing these techniques.

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Acknowledgements

This research work is conducted by NUST-SEECS, IoT Lab, Islamabad, Pakistan, in collaboration with National Agriculture Research Center (NARC), Islamabad, Pakistan.

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Authors and Affiliations

  1. School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Uferah Shafi, Rafia Mumtaz, Zunaira Shafaq, Syed Mohammad Hassan Zaidi & Muhammad Owais Kaifi

  2. National Agriculture Research Center (NARC), Islamabad, Pakistan

    Zahid Mahmood

  3. School of Electronic and Electrical Engineering, University of Leeds, Leeds, L2 9JT, United Kingdom

    Syed Ali Raza Zaidi

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  1. Uferah Shafi
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  2. Rafia Mumtaz
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  4. Syed Mohammad Hassan Zaidi
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Correspondence to Rafia Mumtaz.

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Shafi, U., Mumtaz, R., Shafaq, Z. et al. Wheat rust disease detection techniques: a technical perspective. J Plant Dis Prot 129, 489–504 (2022). https://doi.org/10.1007/s41348-022-00575-x

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