Smart Crop Recommendation and Optimal Route for Market Access

Abstract

Crop Recommendation Systems powered by machine learning have transformed agriculture by offering farmers data-driven insights to make informed decisions about optimal crop selection. In the existing system, the Support Vector Machine (SVM) algorithm has been employed due to its high prediction accuracy for recommending suitable crops based on various factors such as soil quality, weather conditions, and historical crop data. However, by incorporating the Long Short-Term Memory (LSTM) algorithm, the system can further enhance the accuracy and efficiency of predictions. LSTM’s ability to capture temporal dependencies in data allows for more reliable predictions over time, accounting for trends and patterns that improve crop recommendations. Additionally, the integration of Ant Colony Optimization (ACO) aids in identifying the most profitable regions for farming by analyzing the dataset to predict areas with the highest potential yield. This combination of advanced machine learning techniques significantly improves the overall effectiveness of crop recommendation systems, enabling farmers to maximize productivity and profitability.