Integration of Sensing Framework with a Decision Support System for Monitoring Water Quality in Agriculture

Siti Nadhirah Zainurin, Wan Zakiah Wan Ismail (), Siti Nurul Iman Mahamud, Irneza Ismail, Juliza Jamaludin and Nor Azlina Ab. Aziz
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Siti Nadhirah Zainurin: Advanced Devices and System, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Nilai 71800, Negeri Sembilan, Malaysia
Wan Zakiah Wan Ismail: Advanced Devices and System, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Nilai 71800, Negeri Sembilan, Malaysia
Siti Nurul Iman Mahamud: Western Digital Media, Bayan Lepas 11900, Pulau Pinang, Malaysia
Irneza Ismail: Advanced Devices and System, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Nilai 71800, Negeri Sembilan, Malaysia
Juliza Jamaludin: Advanced Devices and System, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Nilai 71800, Negeri Sembilan, Malaysia
Nor Azlina Ab. Aziz: Faculty of Engineering and Technology, Multimedia University, Ayer Keroh 75450, Melaka, Malaysia

Agriculture, 2023, vol. 13, issue 5, 1-14

Abstract: Water is an essential element for every plant to survive, absorb nutrients, and perform photosynthesis and respiration. If water is polluted, plant growth can be truncated. The aim of this research is to develop a water quality monitoring system for agriculture purposes based on integration of sensing framework with a smart decision support method. This research consists of three stages: (1) the first stage: developing sensing framework which has four different water quality parameter sensors such as potential hydrogen (pH), electrical conductivity (EC), temperature, and oxidation-reduction potential (ORP), (2) the second stage: developing a hardware platform that uses an Arduino for sensor array of data processing and acquisition, and finally (3) the third stage: developing soft computing framework for decision support which uses python applications and fuzzy logic. The system was tested using water from many sources such as rivers, lakes, tap water, and filtered machine. Filtered water shows the highest value of pH as the filtered machine produces alkaline water, whereas tap water shows the highest value of temperature because the water is trapped in a polyvinyl chloride (PVC) pipe. Lake water depicts the highest value of EC due to the highest amount of total suspended solids (TSS) in the water, whereas river water shows the highest value of ORP due to the highest amount of dissolved oxygen. The system can display three ranges of water quality: not acceptable (NA), adequate (ADE) and highly acceptable (HACC) ranges from 0 to 9. Filtered water is in HACC condition (ranges 7–9) because all water quality parameters are in highly acceptable ranges. Tap water shows ADE condition (ranges 4–7) because one of the water quality parameters is in adequate ranges. River and lake water depict NA conditions (ranges 0–4) as one of the water quality parameters is in not acceptable ranges. The research outcome shows that filtered water is the most reliable water source for plants due to the absence of dissolved solids and contaminants in the water. Filtered water can improve pH and reduce the risk of plant disease. This research can help farmers to monitor the quality of irrigated water which eventually prevents crop disease, enhances crop growth, and increases crop yield.

Keywords: water pollution; sensors; fuzzy logic; Arduino; membership function (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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