Impact of Internet of Things and Machine Learning in Smart Agriculture

Abstract

Using irrigation, water is delivered to the roots at the proper moment. Plants use evapo-transpiration (ET) to pull water from moist soil and release water into the atmosphere at the same time as absorbing nutrients from the soil with water for root zone growth. There is a critical threshold beyond which plants cannot get the nutrients and water they need for growth. The root zone must be supplied with high-quality water before the limit is reached, as a consequence. Species, soil, and climate all influence this limit. The threshold cap differs by plant kind. The application of the proper amount of water at the right time and place inside the facility is a requirement of scientific scheduling. Monitoring the soil moisture content at the root zone needs a predetermined irrigation schedule based on the plant's nature, its growth, the kind of soil, and its climatic conditions. As a consequence, sensors near the soil's root zone are required to acquire a representative moisture condition for scientific irrigation scheduling. Deep learning and machine learning are two of the most popular techniques to artificial intelligence. People, companies, and governments all use these models to predict and learn from data. Complex and diverse data sets in the food business need the development of machine learning algorithms. The irrigation system shown here is based on machine learning and the Internet of Things. The data owner may then take necessary action depending on the results provided.