An Approach for Crop Prediction in Agriculture – Integrating Genetic Algorithm and Machine Learning

Agriculture plays a vital role in the economy of many countries, especially in regions where a large population depends on farming. However, farmers face challenges such as unpredictable weather conditions, soil variability, and improper crop selection, leading to reduced productivity. This research proposes an intelligent crop prediction system using a combination of Machine Learning, Deep Learning, and Genetic Algorithm techniques. The system utilizes soil parameters such as Nitrogen, Phosphorus, Potassium, and pH along with environmental factors like temperature, humidity, and rainfall to predict suitable crops. A Long Short-Term Memory (LSTM) model is used to capture time-series patterns in weather data, while Random Forest is applied for classification. Genetic Algorithm is used to optimize model parameters and improve performance. The system also integrates Explainable AI techniques to enhance interpretability. Experimental results show improved accuracy and efficiency compared to traditional methods. This approach helps farmers make better decisions and increases agricultural productivity.