Revolutionizing Holy-Basil Cultivation With AI-Enabled Hydroponics System

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

sTetro-Deep Learning Powered Staircase Cleaning and Maintenance Reconfigurable Robot

Staircase cleaning is a crucial and time-consuming task for maintenance of multistory apartments and commercial buildings. There are many commercially available autonomous cleaning robots in the market for building maintenance, but few of them are designed for staircase cleaning. A key challenge for automating staircase cleaning robots involves the design of Environmental Perception Systems (EPS), which assist the robot in determining and navigating staircases. This system also recognizes obstacles and debris for safe navigation and efficient cleaning while climbing the staircase. This work proposes an operational framework leveraging the vision based EPS for the modular re-configurable maintenance robot, called sTetro. The proposed system uses an SSD MobileNet real-time object detection model to recognize staircases, obstacles and debris. Furthermore, the model filters out false detection of staircases by fusion of depth information through the use of a MobileNet and SVM. The system uses a contour detection algorithm to localize the first step of the staircase and depth clustering scheme for obstacle and debris localization. The framework has been deployed on the sTetro robot using the Jetson Nano hardware from NVIDIA and tested with multistory staircases. The experimental results show that the entire framework takes an average of 310 ms to run and achieves an accuracy of 94.32% for staircase recognition tasks and 93.81% accuracy for obstacle and debris detection tasks during real operation of the robot