Counterfactual Based Approaches for Feature Attributions of Stress Factors Affecting Rice Yield
ABSTRACT Agriculture is a crucial sector in many countries, particularly in India, where it significantly influences the economy, food supply, and rural livelihoods. The increased integration of Deep Learning (DL) and Machine Learning (ML) into agriculture has enabled substantial advancements in pre...
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Wiley
2025-01-01
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| Series: | Engineering Reports |
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| Online Access: | https://doi.org/10.1002/eng2.13085 |
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| author | Nisha P. Shetty Balachandra Muniyal Ketavarapu Sriyans Kunyalik Garg Shiv Pratap Aman Priyanshu Dhruthi Kumar |
| author_facet | Nisha P. Shetty Balachandra Muniyal Ketavarapu Sriyans Kunyalik Garg Shiv Pratap Aman Priyanshu Dhruthi Kumar |
| author_sort | Nisha P. Shetty |
| collection | DOAJ |
| description | ABSTRACT Agriculture is a crucial sector in many countries, particularly in India, where it significantly influences the economy, food supply, and rural livelihoods. The increased integration of Deep Learning (DL) and Machine Learning (ML) into agriculture has enabled substantial advancements in predicting crop yields and analyzing factors affecting them. The counterfactual reasoning framework of DICE outperforms LIME and DICE in offering finer insights into feature importance and the relative impact of different factors on yield prediction. DICE provided the clearest causal insights, demonstrating how adjustments to attributes like sandy alfisols and surface texture could lead to significant changes in crop yield by affecting water retention and nutrient availability. SHAP ranked features like phosphate and potash based on their average importance across the dataset, offering a global view of influential factors but lacking in‐depth causal understanding. LIME provided localized insights on immediate influences, such as average rainfall and nitrogen content, although it fell short in revealing broader causal interactions essential for targeted agricultural interventions. The findings highlight the significance of counterfactual explanations in agricultural ML models, as they provide a robust understanding of causal relationships, going beyond correlation‐based attributions. The study provides understandable and practical insights, allowing for focused actions to enhance productivity and adaptability in agriculture. By improving the interpretability of agricultural machine learning models, the research ultimately supports the creation of predictive systems that strengthen sustainable practices and economic development within the agricultural industry. |
| format | Article |
| id | doaj-art-49a71d437ba94e12b51e7a35a143e93a |
| institution | Kabale University |
| issn | 2577-8196 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
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| series | Engineering Reports |
| spelling | doaj-art-49a71d437ba94e12b51e7a35a143e93a2025-01-31T00:22:49ZengWileyEngineering Reports2577-81962025-01-0171n/an/a10.1002/eng2.13085Counterfactual Based Approaches for Feature Attributions of Stress Factors Affecting Rice YieldNisha P. Shetty0Balachandra Muniyal1Ketavarapu Sriyans2Kunyalik Garg3Shiv Pratap4Aman Priyanshu5Dhruthi Kumar6Department of Information and Communication Technology, Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka IndiaDepartment of Information and Communication Technology, Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka IndiaDepartment of Information and Communication Technology, Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka IndiaDepartment of Data Science and Computer Applications, Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka IndiaDepartment of Data Science and Computer Applications, Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka IndiaSchool of Computer Science Carnegie Mellon University Pittsburgh Pennsylvania USADepartment of Computer Science and Engineering, Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka IndiaABSTRACT Agriculture is a crucial sector in many countries, particularly in India, where it significantly influences the economy, food supply, and rural livelihoods. The increased integration of Deep Learning (DL) and Machine Learning (ML) into agriculture has enabled substantial advancements in predicting crop yields and analyzing factors affecting them. The counterfactual reasoning framework of DICE outperforms LIME and DICE in offering finer insights into feature importance and the relative impact of different factors on yield prediction. DICE provided the clearest causal insights, demonstrating how adjustments to attributes like sandy alfisols and surface texture could lead to significant changes in crop yield by affecting water retention and nutrient availability. SHAP ranked features like phosphate and potash based on their average importance across the dataset, offering a global view of influential factors but lacking in‐depth causal understanding. LIME provided localized insights on immediate influences, such as average rainfall and nitrogen content, although it fell short in revealing broader causal interactions essential for targeted agricultural interventions. The findings highlight the significance of counterfactual explanations in agricultural ML models, as they provide a robust understanding of causal relationships, going beyond correlation‐based attributions. The study provides understandable and practical insights, allowing for focused actions to enhance productivity and adaptability in agriculture. By improving the interpretability of agricultural machine learning models, the research ultimately supports the creation of predictive systems that strengthen sustainable practices and economic development within the agricultural industry.https://doi.org/10.1002/eng2.13085agriculturecausal learningcounterfactualsinterventions |
| spellingShingle | Nisha P. Shetty Balachandra Muniyal Ketavarapu Sriyans Kunyalik Garg Shiv Pratap Aman Priyanshu Dhruthi Kumar Counterfactual Based Approaches for Feature Attributions of Stress Factors Affecting Rice Yield Engineering Reports agriculture causal learning counterfactuals interventions |
| title | Counterfactual Based Approaches for Feature Attributions of Stress Factors Affecting Rice Yield |
| title_full | Counterfactual Based Approaches for Feature Attributions of Stress Factors Affecting Rice Yield |
| title_fullStr | Counterfactual Based Approaches for Feature Attributions of Stress Factors Affecting Rice Yield |
| title_full_unstemmed | Counterfactual Based Approaches for Feature Attributions of Stress Factors Affecting Rice Yield |
| title_short | Counterfactual Based Approaches for Feature Attributions of Stress Factors Affecting Rice Yield |
| title_sort | counterfactual based approaches for feature attributions of stress factors affecting rice yield |
| topic | agriculture causal learning counterfactuals interventions |
| url | https://doi.org/10.1002/eng2.13085 |
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