Abstract: Agriculture is the foundation of many large economies, like India and Maharashtra. The newcomers in the agricultural sector face the challenge of deciding which crops to cultivate on their farms. This problem needs addressing, and we are taking steps to solve it. To address this, we have developed a system that predicts suitable crops for farmers based on the natural content present in the soil and another parameters like weather, rainfall, humidity and many more. This solution aims to reduce farmers' losses and enhance production. Unlike existing systems, which are not fully functional and cannot effectively guide farmers in selecting crops, ours utilizes classification and regression algorithms for crop prediction. Our system can be used by the farmer’s on the web as well as the android phone’s as well. Proposed system uses a dataset which contains the samples of the crops with the required nutrition’s such as Potassium. Phosphorus, Nitrogen, pH, Humidity, Rainfall and many more features. We are using the K-Nearest Neighbor (KNN) machine learning algorithm which is Supervised Learning algorithm used for the classification and regression. System uses a pickle library of python to create the Machine Learning Model which we actually recommend the crop to be cultivate. Model then takes the input as the ingredient in the soil as a parameter and then KNN finds the best suitable crop for that particular type of soil.
Keywords: Machine Learning, Crop Prediction, K-Nearest Neighbour, Crop Recommendation, Classification, Regression, Machine Learning Model, Supervised Algorithm.
|
DOI:
10.17148/IJARCCE.2024.13476
