CROP YIELD PREDICTION USING MACHINE LEARNING AND SAGE MAKER
Manoj R
School of Computer Science and IT, Jain (Deemed-to-be University), Bangalore, Karnataka, INDIA
Manojstar205@gmail.com
Murugan R
School of Computer Science and IT, Jain (Deemed-to-be University), Bangalore, Karnataka, INDIA
muruganraam75@gmail.com
Abstract: Impact of climate change in India, many agricultural crops are badly affected by their performance over the past two decades. Predicting the harvest ahead of time can help policymakers and farmers to take appropriate marketing and storage measures. This project will help farmers to know the yield of their crop before sowing in the field and help them to make the right decisions. It tries to solve the problem by building a prototype of an interactive guessing system. Implementation of an easy-to-use web-based user interface and machine learning algorithm will be implemented. The results of the forecast will be made available to the farmer. Therefore, in such a type of data analysis in crop prediction, there are different methods or algorithms, and with the help of those strategies we can predict crop yields. Using a random forest algorithm. By analyzing all these problems and problems such as climate, temperature, humidity, rain, humidity, there is no suitable solution and technology to overcome the situation we are facing. In India, there are many ways to increase economic growth in the agricultural sector. Data mining also helps predict crop yields. In general, data mining is the process of analyzing data with different perspectives and summarizing it into important information. Random Forest is a popular and powerful machine learning algorithm capable of performing both subdivision and decontamination activities, which works by building dozens of Decision Trees during training and producing classroom output which is class mode (planning) or mean prediction (descent) of individual trees.
Keywords: Agriculture, Machine Learning, Cloud Computing, AWS sage maker, python IDE'S, Crop Prediction
