Crop Advancement with Machine Learning

Crop prediction is a crucial task in agriculture that can help farmers and agricultural researchers decide which crops to grow based on environmental variables such as soil type, temperature, rainfall, and humidity. In this project, we propose a data-driven approach for crop prediction using K-means clustering and classification algorithms. We use K-means clustering to group the data into different clusters based on similarities in the environmental variables, and then we use a classification algorithm such as decision trees or support vector machines to predict the best crop to grow in a particular location. This approach can help farmers and agricultural researchers make more informed decisions about crop selection and improve crop yields. We validate our model by comparing historical data on crop yields to our predictions and conducting experiments by growing different crops in different locations. Overall, this project demonstrates the potential of machine learning algorithms for crop prediction in agriculture.