用可解释AI帮农民选作物，提升农业4.0的决策透明度
AgroXAI: Explainable AI-Driven Crop Recommendation System for Agriculture 4.0
- 基于边缘计算和XAI技术，结合天气土壤数据推荐适种作物
- 引入ELI5、LIME、SHAP等方法实现模型决策的局部与全局解释
- 通过反事实解释提供区域替代作物方案，助力作物多样性
当前，粮食需求增长、自然资源减少及气候变化导致耕地有限，作物多样化成为农业4.0的关键挑战。本文提出一种基于边缘计算的可解释作物推荐系统AgroXAI，融合物联网（IoT）、机器学习（ML）与可解释人工智能（XAI）技术，根据气象与土壤条件为区域推荐适宜作物。系统采用ELI5、LIME、SHAP等方法实现模型决策的局部与全局解释，并利用反事实解释法提供区域替代作物建议。该系统旨在提升农业运营效率与生产力，推动下一代农业中的作物多样性发展。
原文摘要 · Abstract (English)
Today, crop diversification in agriculture is a critical issue to meet the increasing demand for food and improve food safety and quality. This issue is considered to be the most important challenge for the next generation of agriculture due to the diminishing natural resources, the limited arable land, and unpredictable climatic conditions caused by climate change. In this paper, we employ emerging technologies such as the Internet of Things (IoT), machine learning (ML), and explainable artificial intelligence (XAI) to improve operational efficiency and productivity in the agricultural sector. Specifically, we propose an edge computing-based explainable crop recommendation system, AgroXAI, which suggests suitable crops for a region based on weather and soil conditions. In this system, we provide local and global explanations of ML model decisions with methods such as ELI5, LIME, SHAP, which we integrate into ML models. More importantly, we provide regional alternative crop recommendations with the counterfactual explainability method. In this way, we envision that our proposed AgroXAI system will be a platform that provides regional crop diversity in the next generation agriculture.
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