Developing a Machine Learning Model for Pest and Disaster Management in Crop Farming

[riyasachdeva04]

💥 Proposal

The goal of this project is to develop a machine learning model that utilizes historical data and environmental factors to aid in pest and disaster management for crop farming. The model will leverage the Gaussian Naïve Bayes algorithm to analyze and predict the likelihood of pest outbreaks and potential crop disasters based on various input parameters.

Tasks:

Data Collection and Preprocessing:

Collect historical data on pest occurrences, disease outbreaks, and crop disasters from relevant sources. Gather environmental factors such as weather data (temperature, humidity, rainfall), soil characteristics, and crop rotation history. Preprocess the collected data to ensure it is in a suitable format for model training and evaluation.

Feature Engineering and Selection:

Identify key features that are influential in predicting pest occurrences and crop disasters, such as weather conditions, soil properties, and previous pest and disease records. Perform feature engineering techniques to extract useful information from the selected features, such as calculating aggregate statistics or deriving new features.

Model Development:

Implement a Gaussian Naïve Bayes algorithm to build the predictive model. Train the model using the prepared dataset, where the input features are the environmental factors, and the target variable represents the occurrence of pests or crop disasters. Fine-tune the model by optimizing hyperparameters to achieve the best performance.

Model Evaluation and Validation:

Evaluate the performance of the developed model using appropriate evaluation metrics such as accuracy, precision, recall, and F1-score. Perform cross-validation techniques to ensure the model's robustness and generalizability. Validate the model against unseen data to assess its real-world applicability and reliability.

Integration and Deployment:

Create a user-friendly interface that allows users (e.g., farmers, agricultural experts) to input the required environmental factors and receive predictions and recommendations for pest and disaster management. Document the model's usage instructions, including input requirements and interpretation of output. Documentation and Collaboration:

Write clear and comprehensive documentation for the project, including the problem statement, data sources, methodology, and model implementation details. Encourage collaboration and seek feedback from the open-source community to enhance the model's accuracy, efficiency, and usability.

[riyasachdeva04]

I would like to work on this under GSSOC'23

[theyashwanthsai]

@riyasachdeva04 Can you please provide the dataset link

[riyasachdeva04]

Crop_recommendation.csv

[bhavika2502]

I would like to work on this under GSSOC'23. Kindly assign this task to me. Thanks:)

[theyashwanthsai]

@riyasachdeva04 - you can go ahead! We are assigning you 21 days for this project, after which it will be assigned to someone else if not completed. All the best! Name the file as: algorithm_dataset.ipynb and link it in the readme of the labeled directory as algorithm - dataset.

@bhavika2502 since we are following the first-come-first-serve policy, we will not be able to assign you this issue. However, you can create another issue and use the same algorithm on a different dataset.

[holkarjuhi1712]

I would like to work for this project

[holkarjuhi1712]

I would like to work for this project under gssoc'23