SCIENCE@home

IoT-Enabled Adaptive Watering System With ARIMA-Based Soil Moisture Prediction for Smart Agriculture

M. Afzal,Iftikhar Ahmed Saeed,Muhammad Noman Sohail,Mohamad Hanif Md Saad,M. R. Sarker

Published 2025 in IEEE Access

ABSTRACT

Agriculture is the backbone of every country as it fulfills the necessities of human beings as well as animals and also supports the country’s economy in terms of revenue by exporting food products to the international market. But due to rapid growth of industries and population food and water shortage is becoming a very critical issue. Especially in Pakistan shortage of water and unpredicted weather conditions are affecting agricultural areas, and as a result, food shortage is increasing. A solution to handle the water shortage is a controlled watering system for crops. In this paper, we describe two different IoT-based systems for crop soil moisture, soil temperature, and outdoor temperature and humidity monitoring, one is Wi-Fi based while the other is a GSM-based system. This helps us monitor the soil moisture content and other parameters in real-time which will help agriculture researchers and farmers to use water according to system reports. The system uses an ARIMA (autoregressive integrated moving average) machine learning model to predict the soil moisture for the next ten days which will help the farmers to arrange the water in Hyetal areas where canal water is not reachable. The predictions are compared with real-time data and the success rate of the ARIMA ML model is from $80-90\%$ . The proposed research will open the door for agriculture researchers to gather new datasets and predictions by adopting this proposed research which will be beneficial to farmers to cultivate the crops and fruits in hyetal lands according to data recommendation.

PUBLICATION RECORD

  • Publication year

    2025

  • Venue

    IEEE Access

  • Publication date

    Unknown publication date

  • Fields of study

    Agricultural and Food Sciences, Computer Science, Engineering, Environmental Science

  • Identifiers
    DOI 10.1109/ACCESS.2025.3532447
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-20 of 20 references · Page 1 of 1

CITED BY

Showing 1-9 of 9 citing papers · Page 1 of 1