Intelligent Crop Prediction and Smart Irrigation System Using IoT, K-Means Clustering, and Random Forest Algorithms
Keywords:
K-Means Clustering, Random Forest, Decision Tree, RaspberryPi, Information and Communication Technology, Internet of ThingsAbstract
Agriculture covers more than 60.43% of the land in India. But we can't meet the country's growing needs if we keep using old farming methods. We need to sustainably and efficiently harvest most of the available farmland. The goal of this project is to set up a cost-effective system that uses high-tech sensors and the Internet of Things to make crop harvesting easier. The most important parts of the system are that it is a module that can be controlled by a mobile app and uses a set of sensors (electromagnetic, NPK, optical, and electrochemical) to measure the soil's texture and nutritional content. Advanced K-Means Clustering, Random Forest, and Decision Tree algorithms are used to analyse it and guess which crop can be grown. It uses infrared and laser sensors to figure out the best way to plant seeds so that they grow the most. It also keeps an eye on how wet the soil is and sets up irrigation systems like drip irrigation. A built-in weather forecasting system will predict rain and change the irrigation cycle to lower the risk of over- or under-irrigation. The module alerts the farmer when the crop needs something during the growth phase. It also has a flexible infrared motion sensor to detect movement and an alarm system to keep predators away, which is another way to keep it safe. Mobile apps control all the functions, while Machine Learning algorithms manage and oversee them. High-tech sensors and actuators, along with microcontrollers and Raspberry Pi, carry out the tasks.
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