Comparative Analysis of Classifiers in a Plant Recommendation System based on Environmental Factors

With the growing demand for reforestation and a sustainable neighborhood, everyone has begun to grow their own plants. However, the survival of a plant depends on many factors. A common problem faced by general customers is that their purchased plants, in gardens or balconies, fail to live long. This might happen because of many reasons, but the most recurrent one is the plant not adapting to the environmental conditions. Thus, personalizing the plant preferences is essential for users, so that they can buy the plants with high confidence of them surviving long. Here, this research work intends to develop an application with various filtering options, to determine the environmental conditions of the location, and the quality of lifestyle the plants can be provided with. To do so, we performed a comparison of the popular classification algorithms and found that the Random Forest Classifier served our purpose, successfully training an AI Model for predicting plants suiting the given conditions.