A Machine Learning based project (Website) that recommends the best crop to grow in your farm by considering some input parameters.
- Farmers face the challenge of determining which crop to plant in a particular soil based on its ingredients. Different crops have different requirements for soil characteristics such as pH level, nutrient content like N,P,K, humidity ,temperature and moisture retention capacity.
- If a farmer chooses the wrong crop for their soil type, the crop may not grow well, leading to low yield and reduced profitability. Additionally, planting the wrong crop in a soil type can lead to soil degradation, reducing its fertility for future crops. Hence, it is crucial for farmers to select the right crop for their soil type to maximize yield and maintain soil health.
- A Crop Recommendation System can assist farmers in making informed decisions by analyzing soil characteristics and providing recommendations for the most suitable crops.
Through extensive research and analysis, we have developed an intelligent algorithm that takes various input of soil condition such as Nitrogen, Potassium, Phosphorus and another parameters such as temperature,humidity,pH and rainfall. And model suggest the most suitable crops for a given farm. Our system not only increases crop yield and profitability but also promotes sustainable farming practices
The methodology for ML model for analyzing crop plantation in suitable conditions would typically involve the following steps:
1.Data collection: In this step we've gathered data on various factors that affect crop growth such as soil quality, weather conditions, water availability, crop type, etc.
2.Data pre-processing: In this step, the collected data is processed to remove any outliers, errors or missing values, and converted into a suitable format for analysis.
3.Feature selection: Based on the data collected, relevant features or variables are selected for analysis. For instance, the selected features may include temperature, humidity, soil pH, rainfall, etc.
4.Model training: We've trained machine learning model on the data.We've used algorithms such as decision trees, random forest, support vector machines, etc.
Model evaluation: After training the model, it is evaluated for its performance in predicting crop growth in different environmental conditions.
Model deployment: We've integrated our ML model on website using Django Framework and deployed on Google Cloud & used AMD instance
