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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Technical Advances in Plant Science
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1451607

Next-Gen Agriculture: Integrating AI and XAI for Precision Crop Yield Predictions

Provisionally accepted
R N V Jagan R N V Jagan 1sree sree sree sree 2*R Praneetha Sree R Praneetha Sree 3
  • 1 Sagi Ramakrishnam Raju Engineering College, Bhimavaram, Andhra Pradesh, India
  • 2 Faculty of Agriculture and Allied Sciences, C.V. Raman Global University, Bhubaneswar, Odisha, India
  • 3 Indian Institute of Information Technology Design and Manufacturing, Kurnool, Kurnool, Andhra Pradesh, India

The final, formatted version of the article will be published soon.

    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.

    Keywords: Agriculture, artificial intelligence, Climate Change, Crop yield prediction, Exploratory data analysis, Decision tree regressor, LightGBM Regressor

    Received: 19 Jun 2024; Accepted: 17 Dec 2024.

    Copyright: © 2024 Jagan, sree and Sree. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: sree sree, Faculty of Agriculture and Allied Sciences, C.V. Raman Global University, Bhubaneswar, 752 054, Odisha, India

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.