Crop Prediction to Aid Farmers using Hybrid Model

This study used Machine Learning (ML) algorithms to predict the suitable crop by inputting the potassium, nitrogen, phosphorous ratios, humidity, rainfall, and soil type. The data are collected, preprocessed, and then trained using algorithms with good accuracy. Some algorithms are used, like decision trees, random forests, support vector machines, and neural networks. To get more accuracy using the performance evaluation with this best-accurate algorithm, the crop that is best suited is predicted. This will help the agriculture department and final users get the cultivation done in an efficient way, and people can do this type of work and select the crop based on the yields. It can lead to the improvement of agriculture in the country. It will be passed on to the next generation and help those who are going to do the agriculture research.