IoT Based Smart Plant Irrigation System with Enhanced learning

In this study, we propose a smart plant irrigation IoT system that autonomously adapts itself to a defined irrigation habit. The automated plant irrigation systems generally make decisions based on static models derived from the plant's characteristics. In contrast, in our proposed solution, irrigation decisions are dynamically adjusted based on the changing environmental conditions. The learning mechanism of the model reveals the mathematical connections of the environmental variables used in the determination of the irrigation habit and progressively enhances its learning procedure as the irrigation data accumulates in the model. We evaluated the success of our irrigation model with four different supervised machine learning algorithms and adapted the Gradient Boosting Regression Trees(GBRT) method in our IoT solution. We established a test bed for the sensor edge, mobile client, and the decision service on the cloud to analyze the overall system performance. The early results from our prototype system that is tested with two indoor plants; namely Sardinia and Peace-lily are very encouraging. The results reveal that the proposed system can learn the irrigation habits of different plants successfully.