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

Sehrish Munawar Cheema (), Muhammad Ali, Ivan Miguel Pires (), Norberto Jorge Gonçalves, Mustahsan Hammad Naqvi and Maleeha Hassan
Additional contact information
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

Agriculture, 2022, vol. 12, issue 8, 1-23

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.

Keywords: smart farming; precision agriculture; IoT; sensor network; semi-literate farmers; interactive interface; User Interface (UI); Android apps (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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