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We provide abstract for support vector algorithm in this paper.
Introduction: This plan aims to leverage the capabilities of machine learning to enhance accuracy and reliability in predicting agricultural outputs.
Data Collection and Preprocessing: The first step involves collecting diverse datasets encompassing factors such as weather patterns, soil quality, historical yield data, and crop-specific variables. Subsequently, employ data preprocessing techniques to clean, normalize, and standardize the datasets, ensuring optimal input for machine learning algorithms.
Feature Selection and Engineering: Utilize feature selection methods to identify the most influential variables impacting crop yields. Concurrently, explore feature engineering techniques to create new meaningful features that may enhance model performance. This step is crucial for maximizing the predictive power of the selected machine learning algorithms.
Algorithm Selection: Evaluate and select appropriate machine learning algorithms based on the nature of the data and the prediction task.
Model Training and Validation: Implement a robust training and validation process for the selected machine learning models. Utilize historical data to train the models, employing cross-validation techniques to ensure generalizability. Evaluate model performance using metrics like accuracy, precision, and recall, refining models iteratively.
Integration with Remote Sensing and IoT: Incorporate remote sensing technologies and Internet of Things (IoT) devices to continuously monitor and update input data. so This integration enhances the dynamic adaptability of the models, enabling real-time adjustments to changing environmental conditions.
Deployment and Continuous Improvement: Once validated, deploy the machine learning models in real-world agricultural settings. Establish a feedback loop for continuous improvement by collecting new data and refining models over time. This iterative process ensures adaptability to evolving agricultural landscapes.
Conclusion: Thus This comprehensive plan synthesizes key components, from data collection and preprocessing to model deployment and continuous improvement, providing a roadmap for developing effective machine learning-based crop yield prediction systems. Implementation of this plan holds the potential to revolutionize agricultural practices, fostering sustainability and food security in the face of global challenges.