AI for Predicting Crop Yield

In this mini project, we will develop an AI model to predict crop yield using machine learning techniques. The project will begin by collecting historical agricultural data, including factors such as weather conditions, soil quality, crop type, and farming practices. We will preprocess the data to handle missing values and normalize the features. Next, we will explore various machine learning algorithms, such as linear regression, decision trees, and random forests, to identify the best-performing model for our predictions. The model’s accuracy will be evaluated using metrics like Mean Absolute Error (MAE) and R-squared values. Finally, we will visualize the predictions against actual yields to assess the model’s performance and provide insights that can help farmers make informed decisions, ultimately contributing to sustainable agricultural practices.