Crop Yield Prediction for Cultivating Alternative Crops Based on Weather and Soil Conditions Using Machine Learning Algorithm
Keywords:Agricultural, Crop recommendation, Crop yield prediction, Machine Learning, Random Forest, Weather
The impacts of climate change in India have severely impacted most crop production over the past two decades. Predicting crop yields before harvest helps policymakers and farmers take appropriate measures for marketing and storage. This project helps farmers to know the yield before planting crops in the field, which helps them make the right decisions. We are trying to solve this problem by building a prototype of an interactive prediction system. Implementation of such a system is done using an easy-to-use web-based graphical user interface and machine learning algorithms. Forecast results are provided to farmers. Therefore, there are various techniques or algorithms for this kind of data analysis in crop forecasting, with the help of which crop yield can be predicted. In this, we have made use of random forest algorithm. Even after analyzing all the issues of weather, temperature, humidity, precipitation, and humidity, there is no right solution or technology to deal with the situation we are facing. India has many opportunities to boost economic growth in the agricultural sector. Data mining can also help predict yields. In general, data mining is the process of analyzing data from different angles and summarizing them into key pieces of information. Random forest is the most popular and powerful supervised machine learning algorithm that can perform both classification and regression tasks. It works by building a large number of decision trees during training and producing an output for the class that is the mode of the class (classification) or the mean prediction of each tree (regression). The second part of the work builds a crop recommendation system that analyzes data including temperature, humidity, pressure, and soil parameters such as N, P, and K.
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Copyright (c) 2023 Akshara R. Nair, Attla Anjali, Aditi Kashyap, K. Mohammed Afzal Ahmed, R. Suma
This work is licensed under a Creative Commons Attribution 4.0 International License.