An Effective Approach for Plant Monitoring, Classification and Prediction Using IoT and Machine Learning

Abstract

Agriculture has been the most important sector of our country. As the world is growing and moving in a fast pace, and depending on the automation of most of the things there is also a need to maintain plants by doing some automation. In general, every plant has some specific needs that have to be addressed for its survival. Therefore, a system must be developed where plants can communicate with the user through IoT. By monitoring these parameters, we can ensure that the plants are healthy. There is also the need to analyse, collect and make the best use of the parameters for classifications about their state. In this project, the focus is mainly on two parts. The first part aims to model a system where we keep track of the requirements of the plants using sensors and IoT. Data collection is done through sensors and sent to the Blynk app. Automatic water controller activates only when the moisture content falls below a certain threshold. The second part aims to analyse the Blynk collected data to classify the plant based on its conditions, i.e. healthy or unhealthy. Finally, we compare the performance results of support vector machine, random forest and logistic regression for classification.