Enhancing crop recommendation systems with explainable artificial intelligence: a study on agricultural decision-making
参考中译:用可解释的人工智能增强作物推荐系统：农业决策研究


          

刊名：Neural Computing & Applications
作者：Mahmoud Y. Shams（Faculty of Artificial Intelligence, Kafrelsheikh University）
Samah A. Gamel（Faculty of Engineering, Horus University）
Fatma M. Talaat（Faculty of Computer Science and Engineering, New Mansoura University）
刊号：738E0033
ISSN：0941-0643
出版年：2024
年卷期：2024, vol.36, no.11
页码：5695-5714
总页数：20
分类号：TP18
关键词：Crop recommendation systems；Machine learning；EXplainable artificial intelligence；Agriculture；Decision support system
参考中译：农作物推荐系统;机器学习;可解释人工智能;农业;决策支持系统
语种：eng
文摘：Crop Recommendation Systems are invaluable tools for farmers, assisting them in making informed decisions about crop selection to optimize yields. These systems leverage a wealth of data, including soil characteristics, historical crop performance, and prevailing weather patterns, to provide personalized recommendations. In response to the growing demand for transparency and interpretability in agricultural decision-making, this study introduces XAI-CROP an innovative algorithm that harnesses eXplainable artificial intelligence (XAI) principles. The fundamental objective of XAI-CROP is to empower farmers with comprehensible insights into the recommendation process, surpassing the opaque nature of conventional machine learning models. The study rigorously compares XAI-CROP with prominent machine learning models, including Gradient Boosting (GB), Decision Tree (DT), Random Forest (RF), Gaussian Naive Bayes (GNB), and Multimodal Naive Bayes (MNB). Performance evaluation employs three essential metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R2). The empirical results unequivocally establish the superior performance of XAI-CROP. It achieves an impressively low MSE of 0.9412, indicating highly accurate crop yield predictions. Moreover, with an MAE of 0.9874, XAI-CROP consistently maintains errors below the critical threshold of 1, reinforcing its reliability. The robust R~2 value of 0.94152 underscores XAI-CROP's ability to explain 94.15% of the data's variability, highlighting its interpretability and explanatory power.
参考中译：作物推荐系统对农民来说是无价的工具，帮助他们做出关于作物选择的明智决定，以优化产量。这些系统利用丰富的数据，包括土壤特性、历史作物表现和流行的天气模式，提供个性化的建议。为了应对农业决策中对透明度和可解释性日益增长的需求，本研究引入了XAI-CROP，这是一种利用可解释人工智能(XAI)原理的创新算法。Xai-Crop的根本目标是让农民能够对推荐过程有可理解的见解，超越传统机器学习模型的不透明性质。该研究严格比较了XAI-CROP与著名的机器学习模型，包括梯度提升(GB)、决策树(DT)、随机森林(RF)、高斯朴素贝叶斯(GNB)和多模式朴素贝叶斯(MNB)。性能评估使用三个基本度量：均方误差(MSE)、平均绝对误差(MAE)和R平方(R2)。实证结果毫不含糊地确立了夏作物的优越业绩。它实现了令人印象深刻的0.9412的低均方误差，表明高度准确的作物产量预测。此外，MAE为0.9874，XAI-CROP始终将误差保持在1的临界阈值以下，从而增强了其可靠性。稳健的R~2值为0.94152，突显了Xai-Crop“S对94.15%的数据的解释能力”，突显了它的可解释性和解释力。



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