Smart Agriculture and Plant Health Monitoring using IoT (Comprehensive Sensor Dataset)
Catalog Summary
"Comprehensive multi-sensor dataset with 9 parameters including environmental (temperature, humidity, light) and soil measurements (moisture, temperature, pH) plus solar battery voltage and water TDS, collected via Arduino-ESP8266 system with cloud integration."
Catalog Notes
Overview
The Smart Agriculture and Plant Health Monitoring dataset published on Mendeley Data in October 2024 (widely used in 2025 research) provides real-time multi-sensor measurements from an Arduino-based IoT system designed for comprehensive agricultural monitoring.
Data Collection System
- Arduino microcontroller integrated with ESP8266 Wi-Fi module for cloud data transmission.
- Sensors measuring both environmental and soil-specific parameters for holistic plant health assessment.
- Data transmitted to ThingSpeak cloud platform using HTTP requests for remote monitoring and visualization.
- Timestamped readings at regular intervals enabling time-series analysis of agricultural conditions.
Measured Parameters (9 Sensors)
- Environmental Temperature: Ambient air temperature around plants.
- Environmental Humidity: Relative humidity of the air.
- Environmental Light Intensity: Illumination levels affecting photosynthesis.
- Soil Moisture: Water content in the root zone.
- Soil Temperature: Underground temperature affecting root development.
- Soil Humidity: Moisture content measurement using resistive or capacitive sensors.
- Soil pH: Acidity/alkalinity levels critical for nutrient availability.
- Water TDS (Total Dissolved Solids): Water quality measurement for irrigation systems.
- Solar Panel Battery Voltage: Energy harvesting system status for off-grid deployments.
Data Format
- Excel (.xlsx) file format with columns for each sensor parameter and timestamp rows.
- Each row represents a synchronized multi-sensor reading snapshot.
- Suitable for import into data analysis tools (Python, R, MATLAB) and machine learning frameworks.
Use Cases
- Plant health monitoring: Correlating environmental and soil conditions with crop growth and stress indicators.
- Irrigation optimization: Using soil moisture and weather data to schedule watering efficiently.
- Predictive analytics: Training ML models to forecast plant diseases, nutrient deficiencies, or yield outcomes.
- IoT system education: Teaching agricultural IoT concepts with real-world sensor integration examples.
- Off-grid agriculture: Evaluating solar-powered IoT system performance in remote farming areas.
View Data Structure
To explore column names, data types, and sample rows, visit the official dataset page on Kaggle.
Preview on KaggleCite This Dataset
DY, S., Kathole, A. B., Patil, S., Mohite, H., Kadam, G., Jawale, A., & Solapure, V. (2024). Smart Agriculture and Plant Health Monitoring using IoT. [Dataset]. Mendeley Data. https://doi.org/10.17632/65jxyrxv7b.1
Source metadata: Mendeley Data (2024) · DOI: 10.17632/65jxyrxv7b.1
Indexed by IoTDataset.com on Jan 31, 2026
Review the Source Record
Confirm the licence, version, access conditions, file format, and provenance at the source before use.