Abstract
Agriculture is the most crucial and vital occupation in India since it balances both the human population's need for food and the supply of essential raw materials for numerous industries. The development of creative farming methods is gradually increasing crop output, increasing its profitability and lowering irrigation waste. The suggested model is a smart irrigation system that uses machine learning to estimate how much water a crop would need. The three most important variables to consider when estimating the amount of water needed in each agricultural crop are moisture, temperature, and humidity. This system consists of sensors for temperature, humidity, and moisture that are placed in an agricultural field and relay data via a microprocessor to a cloud-based IoT device. To effectively forecast outcomes, the decision tree method, a powerful machine learning technique, is applied to data collected from the field. Farmers receive a mail alert with the findings of the decision tree algorithm, which aids in making decisions on water supply in advance. Index Terms-- Irrigation System, IoT, Soil Moisture, Temperature, Humidity, Decision Tree Algorithm, Mail alert.
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