FUTUREFARM AI: PREDICTIVE ANALYTICS FOR SMART AGRICULTURE USING SENSOR DATA

Abstract

The main economic activity is agriculture. It is necessary for maintaining the ecosystem.  Almost 
every element of people's life is dependent on a vast range of agricultural products. In addition to 
responding to climate change, farmers must handle the rising need for more food of higher quality. 
Farmers must be aware of the weather conditions in order to boost agricultural output and growth 
because this will allow them to choose the best crop to sow in those conditions. Smart farming 
powered by IOT improves the entire agricultural system with real-time field monitoring. It displays 
numerous parameters in crystal-clear real-time, including temperature, humidity, and soil, among 
others. It is possible to recommend crops by using the right algorithms on sensed data. The project 
intends to develop a system that predicts agricultural productivity using Internet of Things sensors 
that collect data on numerous environmental factors, such as temperature, rainfall, and pH. The 
suggested method seeks to help farmers boost agricultural productivity while decreasing waste and 
boosting profitability. The project's provision of reliable and timely information about crop yields is 
one of its primary goals. Farmers now make manual estimates of agricultural production, which can 
be tedious and imprecise. The proposed system might employ IoT sensors to collect data in real-time, 
giving farmers precise and current information on crop yields. One of the other objectives of the 
project is to deal with the unpredictable nature of weather patterns. Weather patterns have become 
more erratic as a result of climate change, making it difficult for farmers to schedule when to plant 
and harvest their crops. By examining current practises and adapting them to the current weather 
patterns, farmers can boost crop yields and decrease waste with the help of the suggested approach. 
Using machine learning algorithms and environmental data gathered by IoT sensors, the suggested 
method forecasts crop output.  Machine learning algorithms can analyse large datasets and generate 
precise projections that assist farmers in making decisions. The system can be used by farmers with 
any degree of technological competency because it is accessible and user-friendly.  Farmers may 
easily access and examine the data collected by the Internet of Things sensors thanks to the user
friendly system interface. Additionally, the system has the ability to provide farmers with immediate 
feedback, allowing them to alter their agricultural practises in reaction to the current environmental 
conditions.