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IoAT Enabled Smart Farming: Urdu Language-Based Solution for Low-Literate Farmers

Author

Listed:
  • Sehrish Munawar Cheema

    (Department of Computer Science, University of Management and Technology, Sialkot 54770, Pakistan)

  • Muhammad Ali

    (Department of Software Engineering, The Superior University, Lahore 54600, Pakistan)

  • Ivan Miguel Pires

    (Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal)

  • Norberto Jorge Gonçalves

    (Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal)

  • Mustahsan Hammad Naqvi

    (Department of Computer Science, University of Management and Technology, Sialkot 54770, Pakistan)

  • Maleeha Hassan

    (Department of Nutritional Sciences, University of Sialkot, Sialkot 51310, Pakistan)

Abstract

The agriculture sector is the backbone of Pakistan’s economy, reflecting 26% of its GPD and 43% of the entire labor force. Smart and precise agriculture is the key to producing the best crop yield. Moreover, emerging technologies are reducing energy consumption and cost-effectiveness for saving agricultural resources in control and monitoring systems, especially for those areas lacking these resources. Agricultural productivity is thwarted in many areas of Pakistan due to farmers’ illiteracy, lack of a smart system for remote access to farmland, and an absence of proactive decision-making in all phases of the crop cycle available in their native language. This study proposes an internet of agricultural things (IoAT) based smart system armed with a set of economical, accessible devices and sensors to capture real-time parameters of farms such as soil moisture level, temperature, soil pH level, light intensity, and humidity on frequent intervals of time. The system analyzes the environmental parameters of specific farms and enables the farmers to understand soil and environmental factors, facilitating farmers in terms of soil fertility analysis, suitable crop cultivation, automated irrigation and guidelines, harvest schedule, pest and weed control, crop disease awareness, and fertilizer guidance. The system is integrated with an android application ‘Kistan Pakistan’ (prototype) designed in bilingual, i.e., ‘Urdu’ and ‘English’. The mobile application is equipped with visual components, audio, voice, and iconic and textual menus to be used by diverse literary levels of farmers.

Suggested Citation

  • Sehrish Munawar Cheema & Muhammad Ali & Ivan Miguel Pires & Norberto Jorge Gonçalves & Mustahsan Hammad Naqvi & Maleeha Hassan, 2022. "IoAT Enabled Smart Farming: Urdu Language-Based Solution for Low-Literate Farmers," Agriculture, MDPI, vol. 12(8), pages 1-23, August.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:8:p:1277-:d:894355
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    References listed on IDEAS

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    2. Lee, In & Lee, Kyoochun, 2015. "The Internet of Things (IoT): Applications, investments, and challenges for enterprises," Business Horizons, Elsevier, vol. 58(4), pages 431-440.
    3. Dilshad Ahmad & Muhammad Afzal, 2021. "Impact of climate change on pastoralists’ resilience and sustainable mitigation in Punjab, Pakistan," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(8), pages 11406-11426, August.
    4. Amrita Gupta & Udai B. Singh & Pramod K. Sahu & Surinder Paul & Adarsh Kumar & Deepti Malviya & Shailendra Singh & Pandiyan Kuppusamy & Prakash Singh & Diby Paul & Jai P. Rai & Harsh V. Singh & Madhab, 2022. "Linking Soil Microbial Diversity to Modern Agriculture Practices: A Review," IJERPH, MDPI, vol. 19(5), pages 1-29, March.
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    Cited by:

    1. Dimitre D. Dimitrov, 2023. "Internet and Computers for Agriculture," Agriculture, MDPI, vol. 13(1), pages 1-7, January.

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