An Effective Approach for Crop Recommendation with Using Features of Specific Locations and Seasons and Maximize Crop Yield Production by Using Machine Learning
Keywords:
Crop Prediction, Machine Learning, Deep Learning, Feature Selection, Artificial IntelligenceAbstract
Crop recommendation is a crucial task for farmers to improve their productivity and profitability. However, traditional methods of crop recommendation are often based on heuristic rules or expert knowledge, which may not be accurate or adaptive to the changing environmental and market conditions. Therefore, there is a need for a data-driven approach that can leverage the available information on soil, weather, and crop to provide optimal crop suggestions for farmers. In this paper, we propose a machine-learning algorithm that can recommend suitable crops for a given location and season. The algorithm uses a supervised learning method to train a classification model on a large dataset of historical crop data from various sources. The model takes as input the soil parameters, such as pH, nitrogen, phosphorus, potassium, etc., and the weather parameters, such as temperature, rainfall, humidity, etc., and outputs the most probable crop that can be grown in that location and season[1][8][19][20]. The results show that the algorithm can achieve high accuracy and precision in recommending crops that are suitable for the given conditions.
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