The paper introduces an edge‑deployable system that transforms photographs of bee hive frames into actionable inspection reports. It uses an object detector to identify comb‑cell classes, converts detections into a structured fact schema, and then generates constrained factual text that triggers a knowledge‑base for interpretation and recommendations. The authors also release the FAIRHiveText‑1K dataset, pairing images with facts and text, to support future LLM research in this domain.
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:2506. 04571v3 Announce Type: replace Abstract: Agriculture is undergoing a major transformation driven by artificial intelligence (AI), machine learning, and knowledge representation technologies.
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.
BeeWhere is an AI-assisted workflow that merges ArUco fiducial detections with deep‑learnt instance segmentations to analyze bumble bee colonies. Using high‑resolution images of Bombus impatiens microcolonies, the system annotates thousands of bee instances, pollen balls, nest structures, and chamber boundaries, and trains YOLO models for behavioral metrics such as nearest‑neighbor distance and spatial occupancy. In a validation study, BeeWhere outperformed tag‑based tracking, especially under occlusion, and revealed pesticide‑induced changes in bee spatial organization that tag‑based methods missed.
arXiv:2502. 19507v2 Announce Type: replace Abstract: In response to the growing need for structured, interoperable agricultural data, this paper presents the Sustainable Wheat Production Datahub, a modular, graph-based framework that brings diverse wheat production datasets together into a single, queryable store.
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.
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.
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.
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:2606. 31831v1 Announce Type: new Abstract: High-throughput plant phenotyping now generates image derived datasets far faster than scientists can analyze them.
arXiv:2609.35089v2 Announce Type: replace Abstract: Meta-analysis is the synthesis of information from multiple sources to arrive at an overarching conclusion. There is a large need for meta-analysis...