Title: Crop Yield Prediction using Machine Learning and IoT Sensors
Objective:
Develop a machine learning model for predicting crop yield based on sensor data (temperature, soil pH, and humidity) along with other relevant data such as area, total production, and geographic location.
1-Data Preprocessing:
a. Data Cleaning: Remove any missing or inconsistent values.
b. Feature Engineering: Extract additional features if required (e.g., average temperature, average humidity, etc.).
c. One-hot Encoding: Convert categorical variables (state and district) to numerical representations.
d. Data Normalization: Normalize the features to ensure that they are on the same scale.
2-Exploratory Data Analysis (EDA):
a. Analyze the dataset to identify patterns, trends, and relationships between different variables.
b. Visualize the data using various charts and graphs to better understand the distribution of the variables.
3-Model Selection and Training:
a. Split the dataset into training and testing sets (e.g., 80% training, 20% testing).
b. Choose appropriate machine learning algorithms for regression tasks such as Linear Regression, Decision Trees, Random Forest, Support Vector Machines, and Artificial Neural Networks.
c. Train the models on the training dataset using cross-validation to tune hyperparameters.
4-Model Evaluation:
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5-Model Deployment:
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6-Conclusion and Future Work:
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