AI and IoT Based Monitoring System for Increasing the Yield in Crop Production

Artificial Intelligence (AI) and Internet of things (IoT) based monitoring systems are in great demand and gives a precise extraction and analysis of data. In this paper, the research is performed on a marigold plant to detect the most suitable conditions for plant growth. The philosophy behinds this work is to reduce the risks in agriculture and to promote smart farming practices. The effect of physical conditions like humidity, temperature, soil temperature and moisture and light intensity on the plant growth, is monitored using IoT based monitoring system. The data responsible for the plant growth is obtained using different sensors units like DHT11, LDR, DS18B20, Soil Moisture sensors, Noir camera, single-board microcontrollers and Application Programming Interfaces (APIs). The variation of plant growth rate w.r.t. the intensity of sunlight was observed within the range of 1000 lx-1200 lx, category-2 (best). The further analysis of the extracted parameters is done using different Machine Learning (ML) algorithms. Logistic Regression, Gradient Boosting Classifier and Linear Support Vector Classifier (SVC) algorithms are found best for analysis of physical parameters responsible for the marigold plant growth.