Authors - Tien-Dao Luu, Viet Truong Xuan, Doan Hoang Phuc Nguyen, Nghi Huynh Quang, Do Chau Giang Nguyen, Nghia Nguyen Khoi, Huu Hoa Nguyen Abstract - The Mekong Delta agroecosystem faces compounding climate stressors and acute data fragmentation. While agricultural data exists online, it remains largely unstructured and unverified. This study introduces WikiCrop-AI, an integrated data-processing and machinelearning framework designed to consolidate heterogeneous agronomic information. The architecture comprises three interconnected modules: an Agricultural Notebook utilizing a Retrieval-Augmented Generation pipeline for multi-format data ingestion, a browser-based computational environment (WikiLab) for reproducible workflows, and a client-side analytics module for multivariate clustering. Evaluation of the ingestion pipeline yielded a String Similarity Score of 0.99 for structured text extraction. Furthermore, generative outputs assessed via the Automatic LLMs Citation Evaluation framework achieved average scores of 0.94 for both Citation Recall and Precision, alongside a Claim Recall of 0.92, indicating reliable knowledge synthesis under the tested conditions. A hierarchical clustering case study on 22 soybean cultivars further illustrates the platform’s utility in supporting non-programming local agronomists. Ultimately, WikiCrop-AI provides a decentralized infrastructure to translate scattered digital resources into actionable, verified agricultural intelligence. The complete open-source code for the WikiCrop-AI ecosystem is publicly accessible on GitHub at: https://github.com/mekonglab-vn/ WikicropAI.
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