arXiv:2607. 03245v1 Announce Type: new Abstract: High-throughput plant phenotyping generates valuable data that often remains trapped in unstructured text and isolated RGB images.
Artificial intelligence for plant disease analysis has advanced from task-specific classifiers to multi-modal models capable of jointly interpreting visual and textual information. However, practical...
The paper introduces SOLAR, a multimodal generative model that jointly interprets visual and textual data to understand tomato leaf diseases across six question‑answering tasks. SOLAR aligns visual features with task‑aware language representations using a Fusion Expert module based on a mixture‑of‑experts, enabling it to generate contextually relevant answers for diverse diagnostic tasks. Evaluated on 41,677 images and 216,209 QA pairs, SOLAR outperforms state‑of‑the‑art vision‑only, vision‑language, and task‑specific models in both closed and open‑ended settings, demonstrating superior accuracy, robustness, and multimodal reasoning.
AgriScope is a unified pixel‑grounded multimodal framework designed for agricultural image understanding. It supports image‑level, region‑level, and pixel‑level tasks such as grounded caption generation, referring expression segmentation, and multi‑turn multimodal interaction. The authors also introduce AgriGround, a large‑scale dataset with over 500K images and 11M instruction‑following samples, created via an automatic annotation pipeline that combines caption generation, phrase‑level grounding, segmentation mask creation, and instruction synthesis.
AgriScope is a unified pixel‑grounded multimodal framework designed for agricultural image understanding, supporting image‑level, region‑level, and pixel‑level tasks such as grounded caption generation, referring expression segmentation, and multi‑turn multimodal interaction. It incorporates biologically specialized semantic representations with dense spatial grounding through biological‑semantic encoding, dense spatial representations, and pixel decoding. The authors also introduce AgriGround, a large‑scale dataset of over 500K images and 11M instruction‑following samples, created via an automatic annotation pipeline that combines caption generation, phrase‑level grounding, segmentation mask generation, and task‑oriented instruction synthesis to provide densely grounded supervision for agricultural vision‑language learning.
The paper presents a benchmark to test whether vision‑language models can produce plant simulation configurations from images using in‑context learning. It focuses on cowpea plot reconstruction, requiring the models to output structured JSON that includes field and plant details. Open‑source multimodal models from the Gemma 4 and Qwen3.5 families are evaluated on synthetic and real drone datasets, using five in‑context methods, and the results show that while VLMs can generate valid JSON and estimate key agronomic metrics, their performance varies and often lags behind dataset baselines.
arXiv:2608. 08727v1 Announce Type: cross Abstract: To address this gap, we introduce TomaMMU, a large-scale Tomato leaf disease MultiModal Understanding dataset, alongside TomaBench, a benchmark for evaluating VLMs on tomato disease understanding.
arXiv:2605. 05627v2 Announce Type: replace-cross Abstract: Sustainable forest management relies on precise species composition mapping, yet traditional ground surveys are labour-intensive and geographically constrained.
arXiv:2603. 14342v2 Announce Type: replace-cross Abstract: Modern agricultural data is sourced from diverse platforms and spans multiple spatial scales, ranging from ground-level close-up photography to Unmanned Aerial Vehicle (UAV) aerial observation and satellite remote sensing imagery.
To address this gap, we introduce TomaMMU, a large-scale Tomato leaf disease MultiModal Understanding dataset, alongside TomaBench, a benchmark for evaluating VLMs on tomato disease understanding. TomaMMU comprises 28,808 high-quality images spanning 15 categories and 213,119 human-annotated visual question-answer pairs, generated through a three-stage pipeline comprising Data Collection, Human Annotation, and Question-Answer Generation.
HERBIOME is a modular, end‑to‑end pipeline that automates the digitization of herbarium labels. It combines YOLOv8 for component detection, CRAFT Hezar for word‑level text localization, a fine‑tuned TrOCR model for mixed handwritten and printed text recognition, and GPT‑4o Mini for structuring metadata into standardized fields. Evaluation on 450 French specimens shows high surface similarity (MWS ≈ 0.616) and moderate semantic accuracy (SMA ≈ 0.442), with taxonomic fields identified as the main challenge.
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.
arXiv:2507. 20804v3 Announce Type: replace Abstract: Large Language Models (LLMs) suffer from hallucinations due to their static parametric knowledge.