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.
By Gia-Han Truong, Khang Nguyen Quoc, Luyl-Da Quach
arXiv:2506. 04571v3 Announce Type: replace Abstract: Agriculture is undergoing a major transformation driven by artificial intelligence (AI), machine learning, and knowledge representation technologies.
By Srikanth Thudumu, Jason Fisher
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.
By Roberta Hunt, August Easton-Calabria, James Crall
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.
By Nirmal Gelal, Aastha Gautam, Soheil Abadifard, Nico Giordano, Moumita Sen Sarma, Sanaz Saki Norouzi, Claudio Dias da Silva Jr, Jean Ribert Francois, Kathleen M. Jagodnik, Katherine Nelson, Terry Griffin, Xiaomao Lin, Stacy Hutchinson, Stephen M. Welch, Kelsey Andersen Onofre, Romulo Lollato, Pascal Hitzler, Hande K\"u\c{c}\"uk McGinty
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.
By Jayant Ghadge, Soumyashree Kar, Surya S. Durbha