Summary of Agroxai: Explainable Ai-driven Crop Recommendation System For Agriculture 4.0, by Ozlem Turgut et al.
AgroXAI: Explainable AI-Driven Crop Recommendation System for Agriculture 4.0
by Ozlem Turgut, Ibrahim Kok, Suat Ozdemir
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract proposes an edge computing-based explainable crop recommendation system called AgroXAI, which suggests suitable crops for a region based on weather and soil conditions. The system integrates machine learning (ML) models with explainability techniques such as ELI5, LIME, SHAP to provide local and global explanations of ML model decisions. Additionally, the paper presents regional alternative crop recommendations using counterfactual explainability method. This innovative approach aims to improve operational efficiency and productivity in agriculture, addressing the critical issue of crop diversification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Crop diversity is crucial for ensuring food safety and quality as demand increases. The proposed AgroXAI system uses IoT, ML, and XAI to recommend suitable crops based on weather and soil conditions. This innovative approach provides local and global explanations of model decisions, making it easier to understand why certain recommendations are made. By offering regional alternative crop options, AgroXAI aims to improve agricultural efficiency and productivity. |
Keywords
» Artificial intelligence » Machine learning
