Crops Nutrition Management

The Crops Nutrition Management Model is a machine learning-based system designed to optimize fertilizer and nutrient recommendations for crops based on soil conditions, crop type, and environmental factors. By analyzing soil nutrient levels, pH, moisture, weather conditions, and crop growth stages, the model helps farmers apply the right nutrients at the right time, improving yield and sustainability.

Key Features:
Data Processing & Feature Engineering: Cleans and structures soil and crop data, handling missing values and seasonal variations.
Machine Learning Algorithms: Uses Random Forest, Decision Trees, Support Vector Machines (SVM), and Deep Learning to predict nutrient deficiencies and suggest optimal fertilization.
Model Evaluation: Assesses accuracy using precision, recall, RMSE, and correlation metrics.
Deployment: Integrated with Flask, FastAPI, or mobile applications for real-time recommendations.
This model supports precision agriculture, reduc…

The Crops Nutrition Management Model is a machine learning-based system designed to optimize fertilizer and nutrient recommendations for crops based on soil conditions, crop type, and environmental factors. By analyzing soil nutrient levels, pH, moisture, weather conditions, and crop growth stages, the model helps farmers apply the right nutrients at the right time, improving yield and sustainability.

Key Features:
Data Processing & Feature Engineering: Cleans and structures soil and crop data, handling missing values and seasonal variations.
Machine Learning Algorithms: Uses Random Forest, Decision Trees, Support Vector Machines (SVM), and Deep Learning to predict nutrient deficiencies and suggest optimal fertilization.
Model Evaluation: Assesses accuracy using precision, recall, RMSE, and correlation metrics.
Deployment: Integrated with Flask, FastAPI, or mobile applications for real-time recommendations.
This model supports precision agriculture, reduces excessive fertilizer use, enhances soil health, and maximizes crop productivity through data-driven nutrient management strategies.