Summer Jute Environmental Sensor Dataset from Bangladesh

The Summer Jute Environmental Sensor Dataset from Bangladesh is a field-level agricultural dataset collected using a low-cost Internet of Things (IoT) sensing platform during Kharif-1 (summer) jute cultivation at Sher-e-Bangla Agricultural University, Sher-e-Bangla Nagar, Dhaka, Bangladesh. The dataset contains 5,235 cleaned observations in CSV format. The dataset comprises 10 attributes: Location, Season, Temperature, Humidity, Rainfall, Soil_moisture, Type, Sowing, Growth, and Harvest. Environmental measurements were acquired using an ESP32 S3-WROOM-1, DHT22 AM2302, a capacitive soil moisture sensor, and a rain detection sensor, while the remaining attributes describe the cultivation period. The data were collected under natural field conditions through automated sensor monitoring and cleaned by removing missing values, duplicate records, formatting inconsistencies, and inconsistent categorical labels. The dataset is compatible with Python, R, MATLAB, TensorFlow, PyTorch, and scikit-learn. This dataset supports research in smart agriculture, precision farming, environmental monitoring, agricultural IoT, irrigation management, and agricultural data analytics. It can be reused for exploratory data analysis, statistical modelling, environmental pattern analysis, feature engineering, predictive modelling, and benchmarking machine learning methods. Value of the Data: 1. Provides a real-world field-level environmental dataset collected during summer jute cultivation in Bangladesh using a low-cost IoT sensing platform. 2. Supports research in smart agriculture, precision farming, environmental monitoring, irrigation management, agricultural IoT, and machine learning. 3. Enables exploratory data analysis, statistical modelling, feature engineering, environmental pattern analysis, and benchmarking of agricultural methods. 4. Serves as an educational resource for agricultural data analytics, sensor data processing, visualization, and predictive modelling. 5. Facilitates the development and evaluation of IoT-based agricultural monitoring systems using structured environmental sensor data.