Next-gen agriculture: integrating AI and XAI for precision crop yield predictions
Climate change poses significant challenges to global food security by altering precipitation patterns and increasing the frequency of extreme weather events such as droughts, heatwaves, and floods. These phenomena directly affect agricultural productivity, leading to lower crop yields and economic...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1451607/full |
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| author | R. N. V. Jagan Mohan Pravallika Sree Rayanoothala R. Praneetha Sree |
| author_facet | R. N. V. Jagan Mohan Pravallika Sree Rayanoothala R. Praneetha Sree |
| author_sort | R. N. V. Jagan Mohan |
| collection | DOAJ |
| description | Climate change poses significant challenges to global food security by altering precipitation patterns and increasing the frequency of extreme weather events such as droughts, heatwaves, and floods. These phenomena directly affect agricultural productivity, leading to lower crop yields and economic losses for farmers. This study leverages Artificial Intelligence (AI) and Explainable Artificial Intelligence (XAI) techniques to predict crop yields and assess the impacts of climate change on agriculture, providing a novel approach to understanding complex interactions between climatic and agronomic factors. Using Exploratory Data Analysis (EDA), the study identifies temperature as the most critical factor influencing crop yields, with notable interactions observed between rainfall patterns and macronutrient levels. Advanced regression models, including Decision Tree Regressor, Random Forest Regressor, and LightGBM Regressor, achieved exceptional predictive performance, with R² scores reaching 0.92, mean squared errors as low as 0.02, and mean absolute errors of 0.015. Additionally, XAI techniques such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) enhanced the interpretability of the predictions, offering actionable insights into the relative importance of key features. These insights inform strategies for agricultural decision-making and climate adaptation. By integrating AI-driven predictions with XAI-based interpretability, this research presents a robust and transparent framework for mitigating the adverse effects of climate change on agriculture, emphasizing its potential for scalable application in precision farming and policy development. |
| format | Article |
| id | doaj-art-420c63520aad4c94b80270a7d9621937 |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-420c63520aad4c94b80270a7d96219372025-01-08T06:12:03ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-01-011510.3389/fpls.2024.14516071451607Next-gen agriculture: integrating AI and XAI for precision crop yield predictionsR. N. V. Jagan Mohan0Pravallika Sree Rayanoothala1R. Praneetha Sree2Department of Computer Science and Engineering, Sagi Rama Krishnam Raju Engineering College, Bhimavaram, IndiaDepartment of Plant Pathology, MS Swaminathan School of Agriculture, Centurion University of Technology and Management, Odisha, IndiaDepartment of Computer Science and Engineering, Indian Institute of Information Technology Design and Manufacturing (III TDM), Kurnool, Andhrapradesh, IndiaClimate change poses significant challenges to global food security by altering precipitation patterns and increasing the frequency of extreme weather events such as droughts, heatwaves, and floods. These phenomena directly affect agricultural productivity, leading to lower crop yields and economic losses for farmers. This study leverages Artificial Intelligence (AI) and Explainable Artificial Intelligence (XAI) techniques to predict crop yields and assess the impacts of climate change on agriculture, providing a novel approach to understanding complex interactions between climatic and agronomic factors. Using Exploratory Data Analysis (EDA), the study identifies temperature as the most critical factor influencing crop yields, with notable interactions observed between rainfall patterns and macronutrient levels. Advanced regression models, including Decision Tree Regressor, Random Forest Regressor, and LightGBM Regressor, achieved exceptional predictive performance, with R² scores reaching 0.92, mean squared errors as low as 0.02, and mean absolute errors of 0.015. Additionally, XAI techniques such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) enhanced the interpretability of the predictions, offering actionable insights into the relative importance of key features. These insights inform strategies for agricultural decision-making and climate adaptation. By integrating AI-driven predictions with XAI-based interpretability, this research presents a robust and transparent framework for mitigating the adverse effects of climate change on agriculture, emphasizing its potential for scalable application in precision farming and policy development.https://www.frontiersin.org/articles/10.3389/fpls.2024.1451607/fullagricultureartificial intelligenceclimate changecrop yield predictionexploratory data analysisdecision tree regressor |
| spellingShingle | R. N. V. Jagan Mohan Pravallika Sree Rayanoothala R. Praneetha Sree Next-gen agriculture: integrating AI and XAI for precision crop yield predictions Frontiers in Plant Science agriculture artificial intelligence climate change crop yield prediction exploratory data analysis decision tree regressor |
| title | Next-gen agriculture: integrating AI and XAI for precision crop yield predictions |
| title_full | Next-gen agriculture: integrating AI and XAI for precision crop yield predictions |
| title_fullStr | Next-gen agriculture: integrating AI and XAI for precision crop yield predictions |
| title_full_unstemmed | Next-gen agriculture: integrating AI and XAI for precision crop yield predictions |
| title_short | Next-gen agriculture: integrating AI and XAI for precision crop yield predictions |
| title_sort | next gen agriculture integrating ai and xai for precision crop yield predictions |
| topic | agriculture artificial intelligence climate change crop yield prediction exploratory data analysis decision tree regressor |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1451607/full |
| work_keys_str_mv | AT rnvjaganmohan nextgenagricultureintegratingaiandxaiforprecisioncropyieldpredictions AT pravallikasreerayanoothala nextgenagricultureintegratingaiandxaiforprecisioncropyieldpredictions AT rpraneethasree nextgenagricultureintegratingaiandxaiforprecisioncropyieldpredictions |