arXiv AI June 30, 2026 By Zhaoyang Li, Ruijie Zhang, Jiaqi Liu, Zhaoji Sun

Fine-Tuning General-Purpose Large Language Models for Agricultural Applications:A Reproducible Framework and Evaluation Protocol Based on Qwen3-8B

arXiv:2606. 28992v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) have demonstrated strong abilities in opendomain question answering, information extraction, and text generation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Aug 12

KrishokChat: A Provenance-Traceable Multi-Task Bengali Agricultural Benchmark with Safety-Critical Chemical Advisory

arXiv:2606. 29243v2 Announce Type: replace Abstract: We introduce KrishokChat, an 85,979-instance Bengali agricultural benchmark built from 284 government publications, 13 institutions, and six regional dialects.

By Khan Raiyan Ibne Reza, Sumaiya Tabassum Nimi, Omar Ibne Shahid

fine-tuning benchmarks safety

arXiv AI

Aug 13

OpenAg: Democratizing Agricultural Intelligence

arXiv:2506. 04571v3 Announce Type: replace Abstract: Agriculture is undergoing a major transformation driven by artificial intelligence (AI), machine learning, and knowledge representation technologies.

By Srikanth Thudumu, Jason Fisher

llms rag agents fine-tuning

arXiv AI

Sep 23

Agentic Explainable Artificial Intelligence (Agentic XAI) Approach To Explore Better Explanation: A Case Study in Decision Support for Rice Cultivation in Japan

arXiv:2512. 21066v4 Announce Type: replace Abstract: Explainable artificial intelligence (XAI) reveals how explanatory variables relate to a response variable, yet communicating XAI outputs to laypersons remains difficult, limiting trust in AI-based predictions.

By Tomoaki Yamaguchi, Yutong Zhou, Masahiro Ryo, Keisuke Katsura

llms agents multimodal safety

arXiv Machine Learning

Jun 30

KrishokChat: A Citation-Grounded Dataset and Benchmark for Bengali Agricultural Advisory

arXiv:2606. 29243v1 Announce Type: new Abstract: We present KrishokChat, the first citation-grounded Bengali agricultural instruction-tuning dataset for crop advisory in low-resource settings.

By Khan Raiyan Ibne Reza, Omar Ibne Shahid

rag fine-tuning benchmarks safety

arXiv Computer Vision

Sep 23

BananaVLM: A Domain-Adapted Vision Language Model for Banana Crop Disease Diagnosis

arXiv:2609.25040v1 Announce Type: cross Abstract: Banana crop diseases threaten food security across the world, yet field diagnosis remains difficult because of limited expert access and visual simil...

By Sangam Kumar Jena, Pandarasamy Arjunan

llms fine-tuning multimodal benchmarks

arXiv AI

Jun 30

PlantExpertVQA: A Visual Question Answering Dataset for Benchmarking Vision-Language Models in Plant Science

arXiv:2508. 17117v3 Announce Type: replace-cross Abstract: Existing plant-disease datasets target classification and detection, leaving vision-language models unable to support interactive, reasoning-based diagnosis.

By Syed Nazmus Sakib, Nafiul Haque, Mohammad Zabed Hossain, Shifat E. Arman

llms nlp fine-tuning multimodal benchmarks

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Related stories

KrishokChat: A Provenance-Traceable Multi-Task Bengali Agricultural Benchmark with Safety-Critical Chemical Advisory

OpenAg: Democratizing Agricultural Intelligence

Agentic Explainable Artificial Intelligence (Agentic XAI) Approach To Explore Better Explanation: A Case Study in Decision Support for Rice Cultivation in Japan

KrishokChat: A Citation-Grounded Dataset and Benchmark for Bengali Agricultural Advisory

BananaVLM: A Domain-Adapted Vision Language Model for Banana Crop Disease Diagnosis

PlantExpertVQA: A Visual Question Answering Dataset for Benchmarking Vision-Language Models in Plant Science