AgroXAI: Explainable AI-Driven Crop Recommendation System for Agriculture 4.0
Uloženo v:
| Vydáno v: | arXiv.org (Dec 16, 2024), p. n/a |
|---|---|
| Hlavní autor: | |
| Další autoři: | , |
| Vydáno: |
Cornell University Library, arXiv.org
|
| Témata: | |
| On-line přístup: | Citation/Abstract Full text outside of ProQuest |
| Tagy: |
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3148979179 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3148979179 | ||
| 045 | 0 | |b d20241216 | |
| 100 | 1 | |a Turgut, Ozlem | |
| 245 | 1 | |a AgroXAI: Explainable AI-Driven Crop Recommendation System for Agriculture 4.0 | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 16, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a 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. | |
| 653 | |a Soil lime | ||
| 653 | |a Arable land | ||
| 653 | |a Recommender systems | ||
| 653 | |a Internet of Things | ||
| 653 | |a Agriculture | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Machine learning | ||
| 653 | |a Explainable artificial intelligence | ||
| 653 | |a Natural resources | ||
| 653 | |a Edge computing | ||
| 653 | |a Soil conditions | ||
| 700 | 1 | |a Kok, Ibrahim | |
| 700 | 1 | |a Ozdemir, Suat | |
| 773 | 0 | |t arXiv.org |g (Dec 16, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3148979179/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.16196 |