# PlantVLM-Edge: Quantized Vision-Language Models for On-Device Crop Health Diagnosis

**Authors:** Gaurab Wagle, Bishal Adhikari, Jiajia Li, Xiaolei Huang, Dong Chen  
**Lab:** Mississippi State University · **Published:** 2 Nov 2026  
**Links:** [arXiv](https://arxiv.org/abs/2611.03557) · [PDF](https://arxiv.org/pdf/2611.03557) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.03557.html)

## Key numbers

- **98.6%**: retains 98.6% of full-precision answer quality on both domains while reducing the weight memory of the…
- **49%**: …answer quality on both domains while reducing the weight memory of the 2B backbone by 49%.
- **4-bit**: and 4-bit activation-aware weight quantization

## Abstract

Timely and accurate crop health diagnosis is essential for sustainable agricultural production, yet accurate diagnosis often requires expert knowledge that is not readily available. Recent vision-language models (VLMs) have demonstrated remarkable capabilities in visual understanding, reasoning, and natural-language interaction, creating new opportunities for intelligent crop health assessment. However, deploying VLMs on resource-constrained devices remains challenging due to their substantial computational and memory requirements. This paper presents PlantVLM-Edge, a quantized vision-language framework for conversational crop health diagnosis that can run entirely on resource-constrained edge devices. PlantVLM-Edge combines agricultural domain adaptation, post-training quantization, and on-device serving with an offline chat interface, allowing growers to ask open-ended questions and receive diagnoses, symptom explanations, and management guidance without cloud connectivity. The framework is backbone-agnostic: we validate it with two different VLM types with varying scale and architecture (Qwen3-VL-2B and LLaVA-1.5-7B), four quantization schemes, and two diagnostic domains (plant diseases and insect pests), and we deploy all variants on an NVIDIA Jetson AGX Orin. Eight-bit quantization affects answer quality minimally relative to the full-precision model, and 4-bit activation-aware weight quantization (AWQ) retains 98.6% of full-precision answer quality on both domains while reducing the weight memory of the 2B backbone by 49%. In its most compact form, PlantVLM-Edge requires 2.01 GiB of weight memory, returns its first token in about 0.12 s, and completes a 64-token diagnostic response in 0.78 s. These results demonstrate that timely and accurate crop health diagnosis is practical on edge devices, providing a foundation for next-generation crop monitoring and decision support systems.

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Notes written by AI from the paper, numbers copied from the abstract. Check the paper before you rely on them. From AI Papers Daily.