PREDICTIVE ANALYTICS IN AGRICULTURE-”CROP PREDICTION”…

“Enhancing Agricultural Productivity through Predictive Analytics” ~ Jayaswaroopaa V

INTRODUCTION:

Agriculture faces challenges in optimizing resource allocation and adapting to changing environmental conditions. This case study explores the application of predictive analytics to predict general crop yields, providing farmers with valuable insights for informed decision-making.

DATA DESCRIPTION:

1. Historical Crop Yields:

• Data spanning the past 10 years, detailing crop yields for various crops.

2. Weather Data:

• Daily records of temperature, precipitation, and humidity.

3. Soil Quality Data:

• Information on soil composition, nutrient levels, and pH.

4. Crop Management Practices:

• Details on planting dates, irrigation schedules, and fertilizer usage.

5. Satellite Imagery:

• Images capturing crop growth, density, and potential anomalies.

PRE PROCESSING:

1. Data Cleaning:

• Removal of missing or inconsistent data points to ensure dataset integrity.

2. Feature Engineering:

• Creation of new features such as growing degree days, soil health indices, and vegetation indices from satellite imagery.

3. Data Integration:

• Combining data from different sources into a unified dataset for analysis.

ALGORITHM USED:

Algorithm: Random Forest Regression

Overview: Random Forest is an ensemble learning algorithm that builds multiple decision trees during training and merges them together to obtain more accurate and stable predictions. In the case of regression tasks, it is referred to as Random Forest Regression.

Key Characteristics:

1. Decision Trees:

• The basic building blocks are decision trees, which are trained on different subsets of the data.
• Each tree is trained independently, making predictions based on its own set of rules.

2.Randomization:

• Random Forest introduces randomness in two ways: by using random subsets of the data for training each tree (bootstrap aggregating or bagging) and by considering only a random subset of features at each split.
• This helps prevent overfitting and improves the model’s generalization performance.

3. Ensemble Method:

• The predictions from multiple decision trees are combined to produce the final prediction.
• The ensemble nature of Random Forests often results in better accuracy and robustness compared to individual decision trees.

4. Regression Task:

• In regression tasks, Random Forests predict a continuous variable, making them suitable for predicting crop yields, which are continuous values.

Reasoning:

• Random Forests handle complex relationships in data and are robust to overfitting.
• They can handle both numerical and categorical variables, making them suitable for diverse agricultural datasets.
• The ensemble nature of Random Forests provides stability and improved generalization.

CONCLUSION:

The application of predictive analytics in general crop yield prediction presents a promising avenue for enhancing agricultural practices. The Random Forest Regression model, trained on a comprehensive dataset, demonstrated its ability to predict crop yields accurately. This approach empowers farmers with valuable information to optimize resource allocation, mitigate risks, and ultimately improve overall agricultural productivity.

REFERENCE:

https://www.researchgate.net/publication/368937696_Analysis_of_Crop_Yield_Prediction_Using_Random_Forest_Regression_Model

Crop Selection and Yield Prediction using Machine Learning Approach