A Low-Cost Smart Plant Health Monitoring and Early Warning System for Tulsi Plant Using ESP32

Unfavourable environmental conditions, such as inadequate soil moisture and unsuitable temperature and humidity, can affect plant health and often remain unnoticed until visible symptoms appear. This paper presents the design and implementation of a low-cost smart plant health monitoring and early warning system for the tulsi plant using an ESP32 microcontroller, a soil moisture sensor module and a DHT22 temperature and humidity sensor to monitor the key environmental parameters. The ESP32 compares the readings with predefined plant-specific threshold ranges and classifies the growing conditions as Normal, Attention Required, or Stress Warning. A 1.3-inch I2C SH1106 OLED display shows the readings and status, green and red LEDs indicate the state, and an active buzzer gives an audible warning under critical conditions and can be muted with a push button. The threshold values can be adjusted for other plant species. In testing, the prototype reported the expected STRESS status for dry and very wet soil. The system is built from readily available components and operates locally without Wi-Fi, internet, or cloud connectivity, making it suitable for standalone use. The proposed system offers an affordable way to detect unfavourable growing conditions early and support timely plant care; it does not diagnose plant disease.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-06
DOI
https://doi.org/10.5281/zenodo.24014907
Primary Topic
Smart Agriculture and AI
Type
preprint
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preprint

A Low-Cost Smart Plant Health Monitoring and Early Warning System for Tulsi Plant Using ESP32

Kirti Sawlani, Vipin Nair, Ninad Mehendale, Pranav Bagrecha et al.
1 links
Zenodo (CERN European Organization for Nuclear Research)
Smart Agriculture and AI
preprint

A Low-Cost Smart Plant Health Monitoring and Early Warning System for Tulsi Plant Using ESP32

Kirti Sawlani, Vipin Nair, Ninad Mehendale, Pranav Bagrecha, Hardik Bhutada, Angel Mariam Shibu
preprint en

Abstract

Unfavourable environmental conditions, such as inadequate soil moisture and unsuitable temperature and humidity, can affect plant health and often remain unnoticed until visible symptoms appear. This paper presents the design and implementation of a low-cost smart plant health monitoring and early warning system for the tulsi plant using an ESP32 microcontroller, a soil moisture sensor module and a DHT22 temperature and humidity sensor to monitor the key environmental parameters. The ESP32 compares the readings with predefined plant-specific threshold ranges and classifies the growing conditions as Normal, Attention Required, or Stress Warning. A 1.3-inch I2C SH1106 OLED display shows the readings and status, green and red LEDs indicate the state, and an active buzzer gives an audible warning under critical conditions and can be muted with a push button. The threshold values can be adjusted for other plant species. In testing, the prototype reported the expected STRESS status for dry and very wet soil. The system is built from readily available components and operates locally without Wi-Fi, internet, or cloud connectivity, making it suitable for standalone use. The proposed system offers an affordable way to detect unfavourable growing conditions early and support timely plant care; it does not diagnose plant disease.

Zenodo (CERN European Organization for Nuclear Research)
Somaiya Vidyavihar University (IN)
Zero hunger
Smart Agriculture and AI
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