Implementation of Smart Hydroponic Farming Using AI Controller with Mobile Application System
DOI:
https://doi.org/10.21467/proceedings.7.6.24Keywords:
DLCNN, IoT, TDSAbstract
Agriculture is the foundation of human civilization and enables the production of high-quality food for the world's population. Three parts to the intelligent hydroponic system can automated by the Internet of Things. A camera module, sunlight, turbidity, pH, temperature, and NPK soil sensors will be part of the initial stage of the hardware environment. By a web app, data collected by sensors can be analysed to define parameters for automation depending on the requirements of different plants for maintaining a healthy life cycle. To ensure optimal growth cycle all treatments can be done precisely and promptly using the acquired data, these models can also be further refined to categorize plant health and forecast possible illnesses. Linear Regression, XG Boost and Prediction-Based Deep Learning models like Deep Learning Convolutional Neural Networks (DLCNN) have been utilized to analyse the performance of Smart Hydroponic Farming. It has been observed that DLCNN outperforms in comparison with other techniques in terms of accuracy and F-measure.
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