Sustania: A Digital Farming Ecosystem for Smart Agriculture

We have developed an innovative Smart Irrigation System to address recent challenges in agricultural water management and to meet the growing need for sustainable farming practices, particularly for small- and medium-scale farmers. The system integrates Internet of Things (IoT)-based sensors, machine learning algorithms, and a user-friendly web interface to optimize water usage in agricultural settings. It uses soil moisture and temperature sensors along with a Random Forest classifier, ensuring precise and reliable scheduling. The platform features a modular architecture with an SQLite backend and a React-based front-end, making it a cost-effective solution. Initial testing was conducted using a DHT11 temperature sensor and a resistive soil moisture sensor; however, subsequent evaluations demonstrated that the DHT22 temperature sensor provided better performance and durability. After extensive comparison, the Random Forest classifier was selected for model training, as it proved to be the fastest and most accurate option with an accuracy of 91 %. Core features of the system include real-time temperature and moisture updates and predictive capabilities based on these readings. Our system has been named “Sustania” due to its potential to serve as a valuable tool for farmers transitioning to the modern era of sustainable and innovative farming.

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Publication Details

Published
2025-07-18
DOI
https://doi.org/10.1109/accthpa65749.2025.11168623
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
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article

Sustania: A Digital Farming Ecosystem for Smart Agriculture

David James, M Rohith, E. Santhosh, Amisha Angel Pius et al.
Smart Agriculture and AI
article

Sustania: A Digital Farming Ecosystem for Smart Agriculture

David James, M Rohith, E. Santhosh, Amisha Angel Pius, Ronn Mathew Sino
article en

Abstract

We have developed an innovative Smart Irrigation System to address recent challenges in agricultural water management and to meet the growing need for sustainable farming practices, particularly for small- and medium-scale farmers. The system integrates Internet of Things (IoT)-based sensors, machine learning algorithms, and a user-friendly web interface to optimize water usage in agricultural settings. It uses soil moisture and temperature sensors along with a Random Forest classifier, ensuring precise and reliable scheduling. The platform features a modular architecture with an SQLite backend and a React-based front-end, making it a cost-effective solution. Initial testing was conducted using a DHT11 temperature sensor and a resistive soil moisture sensor; however, subsequent evaluations demonstrated that the DHT22 temperature sensor provided better performance and durability. After extensive comparison, the Random Forest classifier was selected for model training, as it proved to be the fastest and most accurate option with an accuracy of 91 %. Core features of the system include real-time temperature and moisture updates and predictive capabilities based on these readings. Our system has been named “Sustania” due to its potential to serve as a valuable tool for farmers transitioning to the modern era of sustainable and innovative farming.

Rajagiri Hospital (IN)
Openalex Percentile: Top 81%
Smart Agriculture and AI
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