This repository contains a crop prediction model that utilizes soil nutrient values, including soil pH, nitrogen, phosphorus, soil moisture, and potassium, to predict suitable crops for a given soil condition. The model aims to assist farmers and agricultural practitioners in making informed decisions about crop selection based on soil characteristics.
Introduction, Installation, Usage, Model Architecture, Data Preparation, Training, Evaluation, Prediction, Contributing
The crop prediction model is designed to predict suitable crop options based on soil nutrient values. By considering soil pH, nitrogen, phosphorus, soil moisture, and potassium levels, the model can provide recommendations for crops that thrive under specific soil conditions. This information can assist farmers in optimizing their agricultural practices and maximizing crop yields.
To use the crop prediction model, please ensure you have the following prerequisites:
Python (version 3.10 +) Required Python packages (NumPy, Pandas, Scikit-learn, TensorFlow) You can install the necessary packages using the following command:
pip install -r requirements.txt
To use the crop prediction model, follow these steps:
Prepare your soil nutrient data, including soil pH, nitrogen, phosphorus, soil moisture, and potassium values for the target soil samples. Input them in a prompt while running the code. Load the pre-trained model weights (if available) or train the model using your soil nutrient data (see Training). Receive recommendations for suitable crops based on the model's predictions.
The crop prediction model architecture consists of a neural network model, specifically a multi-layer perceptron (MLP) model. The MLP model comprises several dense layers with appropriate activation functions to capture the complex relationships between soil nutrient values and crop suitability. The model is implemented using a deep learning framework such as TensorFlow.
To train and evaluate the crop prediction model, you need soil nutrient data. The dataset should include samples of soil nutrient values, along with corresponding crop labels or crop suitability information.
Ensure that the dataset is properly formatted and preprocessed, with the necessary features (soil pH, nitrogen, phosphorus, soil moisture, and potassium) and corresponding target labels.
To train the crop prediction model, follow these steps:
Load the preprocessed soil nutrient dataset. Split the dataset into training and validation sets. Normalize the input features using appropriate scaling techniques (e.g., Min-Max scaling or Standardization). Define the model architecture, including the number of layers, number of neurons, and activation functions. Compile the model by specifying the loss function, optimizer, and evaluation metrics. Train the model using the training dataset. Monitor the training process, including training loss and accuracy.
To evaluate the crop prediction model, follow these steps:
Load the preprocessed soil nutrient dataset. Normalize the input features using the same scaling technique applied during training. Load the trained model weights. Use the trained model to predict crop suitability for the evaluation dataset. Compare the predicted crop labels with the actual labels. Calculate evaluation metrics such as accuracy, precision, recall, or F1-score to assess the model's performance.
To make crop predictions using the trained model, follow these steps:
Collect the soil nutrient values for a specific location or field. Normalize the input features using the same scaling technique applied during training. Load the trained model weights. Use the trained model to predict the suitability of various crops for the given soil nutrient values. Rank the predicted crop options based on their suitability scores. Provide the crop recommendations to the user.
Contributions to this crop prediction model are welcome. If you find any issues or have suggestions for improvement, please open an issue or submit a pull request. Your contributions can help enhance the accuracy and functionality of the model. kindy contact [email protected]