AgriGPT-Omni: Unified Agricultural AI

• AgriGPT-Omni is a comprehensive agricultural AI framework integrating speech, vision, and text for multimodal reasoning across diverse languages and regional contexts.
• Its architecture leverages adaptive modality encoders and retrieval-augmented generation to achieve high accuracy in agricultural QA, image analysis, and field recommendations.
• The system's robust data pipeline synthesizes extensive multimodal datasets, supporting applications like digital twin integration, policy dashboards, and precision agronomy.

AgriGPT-Omni is a unified, large-scale agricultural intelligence framework designed to perform multimodal, multilingual, and context-aware reasoning across speech, vision, and text for global agriculture. The system addresses critical challenges in agricultural AI, including the lack of comprehensive multimodal datasets, harmonized tri-modal architectures, task-specific instruction tuning, and robust evaluation protocols. Its technical advances make it suitable as a foundation model and engineering blueprint for applications ranging from field-scale recommendation systems and policy dashboards to conversational agronomy assistants and genomic prediction pipelines. AgriGPT-Omni brings together diverse research threads in domain-specific data pipelines, advanced LLM architectures, retrieval-augmented generation (RAG), reinforcement learning, and precision agriculture integration.

1. Model Architecture and Multimodal Fusion

AgriGPT-Omni’s architecture integrates three primary modalities: speech, vision, and text, supporting at least six languages (Mandarin, Sichuanese, Cantonese, English, Japanese, Korean) (Yang et al., 11 Dec 2025). The core design combines:

• Audio encoder (conformer-style) for speech comprehension and transcription tasks.
• Vision encoder (ViT-style) for processing agricultural imagery, including landscape, field, and object-level data.
• Autoregressive LLM backbone that fuses multimodal inputs via cross-modal adapters.

The fusion mechanism uses lightweight adapters that align the audio and visual streams into the LLM’s embedding space, enabling unified reasoning across modalities. In contrast to single-modal agricultural LLMs, AgriGPT-Omni employs a staged progressive alignment strategy:

1. Stage 1 – Textual and Speech–Text Alignment: The model is continued pre-trained over 2.2 billion tokens. Speech–text alignment is achieved by joint tuning so that the model can map raw speech (encoded by a conformer) to textual outputs.
2. Stage 2 – Multimodal Progressive Alignment: Through contrastive vision–language and audio–language alignment using image–caption and audio–text pairs, the visual and audio adapters are refined while the core LLM is frozen. The loss functions include:

$$\mathcal{L}_{\rm VL} = -\sum_{i}\log\frac{\exp(\mathrm{sim}(v_i, t_i)/\tau)}{\sum_{j}\exp(\mathrm{sim}(v_i, t_j)/\tau)}$$

and analogously for speech–text.

1. Stage 3 – Group Relative Policy Optimization (GRPO) RL: The policy is fine-tuned on high-quality reward-annotated samples with a group-relative advantage objective. The GRPO loss incorporates variance normalization and a KL penalty to enforce conservative policy updates:

$$\mathcal{L}_{\rm GRPO}(\phi) =\mathbb{E}_{y_j\sim\pi_{\rm ref}}\left[\frac{1}{M}\sum_j \min\left(\varrho_j\tilde A_j,\,\mathrm{clip}(\varrho_j,1-\epsilon,1+\epsilon)\tilde A_j\right)\right] -\lambda\,\mathrm{KL}[\pi_\phi\,\|\,\pi_{\rm ref}]$$

where $$\varrho_j = \frac{\pi_\phi(y_j\mid q)}{\pi_{\rm ref}(y_j\mid q)}$$.

The multimodal fusion is thus not static; modality-specific encoders are adaptively aligned to serve downstream QA, VQA, and ASR tasks (Yang et al., 11 Dec 2025, Awais et al., 10 Oct 2024, Sharma et al., 2022).

2. Data Collection, Synthesis, and Multilingual Coverage

AgriGPT-Omni leverages a comprehensive, multi-source data pipeline that addresses the chronic scarcity of annotated multimodal and multilingual agricultural datasets (Yang et al., 11 Dec 2025). The pipeline synthesizes and curates the following corpora:

Modality	Dataset Size	Languages	Key Features
Text (QA)	342,000 pairs	Six (EN/CH/JP/KR)	Curated Q&A of agri topics
Vision-Language	150,000 pairs	Six	Image–caption agri pairs
Synthetic Speech	492,000 samples	Six	TTS with CosyVoice2-0.5B
Real Human Speech	1,431 samples	Six	Field-condition recordings

All text and vision corpora are machine-translated into target languages using Qwen2.5-72B before speech synthesis, ensuring that the speech dataset covers both everyday and technical vernacular relevant to agriculture. Real-world speech is recorded under controlled field conditions to introduce realistic accent and noise variability for robust speech recognition and reasoning (Yang et al., 11 Dec 2025).

Image curation often originates from datasets such as MM-LUCAS and AgriBench, which provide 1,784 landscape images annotated with semantic segmentation masks, depth maps, and land use/cover meta-data (Zhou et al., 30 Nov 2024).

3. Instruction Tuning, Knowledge Integration, and RAG

AgriGPT-Omni employs a rich instruction tuning paradigm reflecting the diversity of agricultural tasks and knowledge sources. The pipeline explicitly integrates:

• Instruction tuning on hierarchical task structures: text-only, speech–text, vision–text, and tri-modal (speech + image + text).
• Retrieval-Augmented Generation: For open-domain reasoning and region/context-dependent advice, external agricultural corpora (e.g., Embrapa Q&A, AgriExam, sensor logs, regulatory handbooks) are indexed. A hybrid retrieval algorithm mixes BM25 and vector similarity to assemble context chunks, which supply the prompt context for LLM reasoning (Silva et al., 2023). Prompt templates are specialized per task, supporting both MCQ and open generation. Passage selection is scored by:

$$\text{score}(p) = \alpha\,\text{norm(BM25}(p)) + (1-\alpha)\,\cos(q, p)$$

• Ensemble Refinement (ER): Multiple generations under chain-of-thought (CoT) prompting are refined via a second-stage prompt to reduce hallucination and improve answer consistency. Votes are aggregated either by plurality or logit-weighted confidence (Silva et al., 2023).

Knowledge infusion from domain ontologies (e.g. FoodOn via entity linking and SPARQL) and dynamic retrieval keeps the system current with regulatory limits, crop rotation constraints, and region-specific best practices (Rezayi et al., 2023).

4. Benchmarks, Evaluation Protocols, and Metrics

AgriGPT-Omni is evaluated on dedicated multi-modal and multilingual benchmarks with rigorous protocols and tooling (Yang et al., 11 Dec 2025, Zhou et al., 30 Nov 2024):

• AgriBench-Omni-2K: A tri-modal benchmark with 1,500 samples partitioned among audio QA, audio+text MC, multimodal (speech+image→text/MC) tasks, all with strict language coverage and zero-overlap between train/test splits (deduplicated at ROUGE-L < 0.7 and manually verified by experts). Evaluation comprises:
  • Audio QA: pairwise win-rate, EM accuracy, WER/CER for transcription.
  • Multimodal MC: classification accuracy.
  • Vision/Text Tasks: BLEU, ROUGE-1, segmentation mIoU/F1 in future releases (Zhou et al., 30 Nov 2024).
• AgriBench: Five hierarchical levels from basic recognition (object naming) to human-aligned suggestion (strategic planning, sustainability) (Zhou et al., 30 Nov 2024). Each level comprises tasks across modality types (T→T, I→T, I→I, T+I→T, T+I→I), with standardized prompts and either exact-match or human-panel scoring.
• General-domain evaluation indicates AgriGPT-Omni attains MMLU 62.8% (+12.8% over Qwen2.5-VL), and on domain tasks such as speech QA achieves a 76% win-rate against Qwen2.5-Omni-7B, with real-world robustness validated on field-recorded audio (Yang et al., 11 Dec 2025).

5. End-to-End Application Workflows

AgriGPT-Omni enables several usage scenarios (Potamitis, 2023, Silva et al., 2023):

1. Conversational Data Analytics: Users submit voice queries from field/mobile devices; speech is transcribed and normalized, LLM generates executable code (often Python/Pandas) to interrogate sensor databases (e.g. insect counts, microclimate readings), and results are visualized or synthesized back to speech for iterative refinement.
2. Automated Agronomist Assistant: Through RAG+ER, the system retrieves up-to-date domain knowledge (research bulletins, regulatory caps) and generates context-cited answers or detailed management guidelines (e.g. fertilizer dosages, IPM strategies), with regulatory and cross-step consistency enforced in post-processing.
3. Digital Twin Integration: When interfaced with precision agriculture digital twins, AgriGPT-Omni consumes fused real-time streams (soil sensors, NPK, GPS, weather) and delivers crop recommendation or scenario simulation (irrigation optimization, pest management). The system supports predictive control policies via hybrid ML-control (e.g., Model Predictive Control) (Banerjee et al., 6 Feb 2025).
4. Farm-to-Fork NLP: The architecture supports semantic matching between product labels and nutrition tables (e.g., linking retail scanner data to nutrient composition), recipe-to-cuisine classification, and ingredient extraction, utilizing BERT-derived models and GPT-augmented datasets (Rezayi et al., 2023).

6. Model Performance, Limitations, and Scientific Impact

Experimental results demonstrate significant gains over generalist baselines. AgriGPT-Omni achieves:

• BLEU scores of 9.69 (text gen) and 16.89 (image→text) vs. 7.69/13.42 for Qwen2.5-VL (Yang et al., 11 Dec 2025).
• Speech-centric tasks see accuracy improvements of 75.7% on tri-modal MC tasks compared to ~59% for baseline models.
• In pilot precision-agriculture deployments, digital-twin connected optimizations (e.g., MPC for irrigation) reduced water use by 18% without yield penalty and cut pesticide events by 22% (Banerjee et al., 6 Feb 2025).

Limitations remain, including incomplete disease and soil annotations in MM-LUCAS, underexplored Level 4–5 benchmark tasks where no single "correct" answer exists, and susceptibility to hallucinations, especially in open-ended or low-resource dialect task slices (Zhou et al., 30 Nov 2024, Yang et al., 11 Dec 2025). The platform’s core strengths are the modularity of its encoders and adapters, continual learning from domain-specific data, and its ability to incrementally integrate new modalities (sensor, genomic, environmental, policy) as dictated by research and deployment needs (Sharma et al., 2022).

7. Future Directions and Extensibility

Next steps for AgriGPT-Omni as outlined by multiple studies include (Yang et al., 11 Dec 2025, Awais et al., 10 Oct 2024, Zhou et al., 30 Nov 2024, Sharma et al., 2022):

• Extending language coverage to additional regional dialects and low-resource languages with further TTS and ASR data synthesis and continuous domain-adaptive pretraining.
• Plugging domain-adapted object detectors (e.g. YOLO, Detectron) and generative models into the vision stack for precise pest/disease identification.
• Incorporating fine-tuned audio analysis pipelines for species/infestation state recognition.
• Integrating digital twin submodules for scenario simulation, closed-loop feedback, and dynamic optimization—allowing field-deployable, edge intelligence.
• Adopting modular mixture-of-experts architectures to route queries across domain sub-experts (soils, crop protection, supply chain).
• Enhancing responsible AI features: domain- and region-specific explainability, bias assessment, transparency modules for decision justification, and human-in-the-loop evaluation for high-stakes tasks.

AgriGPT-Omni thus defines a comprehensive, extensible blueprint for agricultural foundation models, setting a reproducibility and capability reference point for future multimodal, multilingual, and end-to-end integrated agricultural AI systems (Yang et al., 11 Dec 2025).