ULTRASONIC BASED CROP PREDICTION USING RASPBERRY PI

Abstract

In India, the majority of people rely mostly on agriculture as their source of income and subsistence. While some nations have already embraced precision agriculture, more IoT and cloud computing technologies are still required for improved crop productivity. The current climate varies in many parts of India because to a variety of natural and human-caused factors, including proximity to the equator, wind direction, and the distance to the sea. Human-caused elements include air pollution, deforestation, and sewage. A farmer has to forecast which crop should be planted when based on climate variations. The crop details that must meet the standards, including maximum and minimum temperatures, maximum and minimum rainfall, soil type, and location, are stored in the dataset. Using a DHT11 temperature sensor and a soil moisture sensor attached to a Raspberry Pi, data on the current temperature and rainfall range may be gathered. The location, temperature, and range of precipitation of the gathered data are saved in AWS IoT. Message Queue Telemetry Transport, or MQTT, is one messaging protocol that makes it simple to establish connections with distant places. It takes a message broker to implement the publish-subscribe pattern. Depending on the subject of a message, the broker is in charge of sending it to prospective clients. In order to anticipate the crop that should be grown in a given place in accordance with climatic variations, decision trees are a flexible machine learning technique that can handle both classification and regression problems. By comparing the data with the training data, Amazon QuickSight facilitates data visualization.