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

Paper recorded by Signals 4 on 2026-11-02 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-11-02 on arXiv · recorded by Signals 4 on 2026-11-03

Category: cs.AI · 人工智能 · first seen 2026-11-03

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. Ho

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#34 most recent of 920 cs.AI papers we have recorded · ↑ newer: Sonar-MoE: In-Backward Expert Updates for Memory-Efficient Mixture-of- · ↓ older: MCP Tool Poisoning in Agentic AI Frameworks: A Cross-Framework Measure

Cite this page: PlantVLM-Edge: Quantized Vision-Language Models for On-Device Crop Health Diagnosis: the #34 most recent of 920 cs.AI papers we have recorded (as of 2026-11-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/plantvlm-edge-quantized-vision-language-models-for-on-device-crop-health-diagnos.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json

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