An IoT and AI-Based Smart Plant Care and Monitoring System for Real-Time Crop Recommendation and Intelligent Decision Support

Abstract

The fast progress of the Internet of Things (IoT) and Artificial Intelligence (AI) has allowed the creation of smart farming and gardening applications that can be used to improve production, sustainability, and the precision of decisions. The conventional methods applicable in farming and plant care make use of manual observation, the most suitable method, but frequently result in the inefficient utilisation of water, fertilisers, and land materials. In this paper, the design, implementation, and evaluation of an IoT and AI-based Smart Plant Care and Monitoring System, including real-time environmental sensing, machine learning-based crop and fertiliser recommendations, and an AI-powered chatbot to interact with the farmer, have been presented. The suggested system gathers real-time temperature and humidity data from sensors connected to a microcontroller and sends it to a Flask server on the cloud. To improve accuracy in decisions, soil moisture and pH parameters are added. There is the use of a Random Forest machine learning model is used to forecast appropriate crops and fertilisers according to the environmental conditions, and a chatbot built using Naive Bayes is used to give instant replies to queries posed by farmers. An online dashboard can also be used to monitor and communicate in real-time. The system has been experimentally tested to enhance the selection accuracy of crops, thereby lowering the workload on the hands of the people and ultimately allowing sustainable farming processes. The offered architecture is scalable, inexpensive and can be applied both to smart farming and to residential gardening.