Crop Yield Forecast Using Machine Learning

Over the past two decades, climate change has had a significant negative impact on the general public’s perception of the performance of India’s agricultural products. Predictions of crop yields earlier than harvest can help farmers and policymakers pick the best advertising and storage strategies. This project will aid farmers in learning about the production in their crop before they start nurturing just on agricultural subject, allowing them to make better decisions. It seeks a resolution by creating a working model of an engaging prediction system. It will be used to implement a system with a graphical user interface (GUI) and machine learning rules that are accessible via the web. Prediction results could be shared with the farmer. For this reason, we can rely on crop production with the help of various methodologies or techniques for this kind of required to display in crop prediction. It makes use of a ruleset based on an unpredictable forest environment. Despite the evaluation of nearly all these worries and troubles, including weather, heat, humidity, rainfall, and moisture, there are no appropriate remedies or innovations to deal with the situation we're in. Agricultural growth can be aided in India’s economy in several ways. Increases in crop yields can be predicted with the help of data mining. Modern data mining is the process of analysing large amounts of data in order to extract useful information. Random woodland, the most popular and potent monitored handset mastering set of rules, can perform all classification and regression tasks.