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Optimizing Crop Yield Prediction: An In-Depth Analysis of Outlier Detection Algorithms on Davangere Region.
C S Anu1,2, C R Nirmala1,2, A Bhowmik3,4
1Department of Computer Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, Karnataka, India.
Thescientificworldjournal
|July 7, 2025
Summary
Accurate crop yield prediction is vital for agriculture. Elliptic envelope best identified outliers in Davangere
Area of Science:
- Agricultural Science
- Data Science
- Machine Learning
Background:
- Accurate crop yield prediction is essential for agricultural planning and resource management.
- Outlier detection algorithms are crucial for enhancing the precision of predictive models.
Purpose of the Study:
- To optimize crop yield prediction in the Davangere region.
- To evaluate the performance of six outlier detection algorithms on local agricultural data.
Main Methods:
- Systematic evaluation of six outlier detection algorithms: isolation forest, elliptic envelope, one-class SVM, iterative R, spatial singular value decomposition (SSVD), and spatial multiview outlier detection (SMVOD).
- Performance assessment using precision, recall, accuracy, and F1 score metrics.
Main Results:
- Elliptic envelope demonstrated superior performance in identifying and removing outliers within the Davangere agricultural dataset.
- The application of elliptic envelope led to improved refinement of the crop yield prediction model.
Conclusions:
- Elliptic envelope is effective for outlier detection in Davangere's agricultural data.
- Optimized crop yield prediction through outlier removal enhances agricultural planning in the region.
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