Revolutionizing Holy-Basil Cultivation With AI-Enabled Hydroponics System

Abstract

This research study focuses on the design and implementation of an IoT-based hydroponic system specifically optimized for the growing of exotic and medicinal plants. The system utilizes the functionality of Azure IoT Hub, Azure Container, and Azure DataBricks, where we employ a logistic regression model on sensor data to classify the upcoming seasonal parameters such as nutrient dispensation, water stream, and light for optimal plant growth. Cloud decision-making is used on the sensor data to identify any potential N, P, and K deficiencies in the plant and calculate the ‘HealthinessScore’ for the plant. The main boards used in the system are based on the ESP32S chipset with inbuilt Wi-Fi and Bluetooth, which also includes a high-resolution camera module for capturing images of the plants. Additionally, a React-based monitoring portal has been developed to allow for remote monitoring of system parameters and plant health. The chosen plant for this work is the Rama Tulsi (Holy Basil), a medicinal plant known for its medicinal properties. The use of IoT and ML in this hydroponic system aims to improve the efficiency and effectiveness of plant growth while also reducing the need for manual monitoring and intervention. The required nutrients are added to the water according to the crop, and the same water is recycled. Since there is no soil and only water is used, roots will absorb the nutrients faster than those cultivated in the soil. The water will be checked for any contamination or metal ions before being used in the hydroponic farm. The proposed hydroponic system will reduce the time from field to market for the crop, and it requires minimal space compared to conventional agricultural procedures.

Keywords

• Hydroponics
• Internet of Things
• holy-basil plants
• machine leaning (ML)
• healthiness score