Agricultural Image Analysis

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5 papers in the last four weeks, up 67% on the four weeks before. 0.0% of all new papers.

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Hallucination Behavior in Multimodal LLMs Across Agricultural Image Interpretation and Generation Tasks
May 26, 2026Partho Ghose, Al Bashir, Prem Raj +1Agricultural Image Analysis VLM Hallucination
Department of Biological & Agricultural Engineering, Texas A&M AgriLife Research, Texas A&M University System, Dallas, TX 75252, USA.
YOLO26-RipeLoc Lite: A lightweight architecture for tomato ripeness detection and picking point localization in greenhouse robotic harvesting
May 26, 2026Rajmeet Singh, Manveen Kaur, Shahpour Alirezaee +1Agricultural Image Analysis Robotic Grasping
Department of Mechanical Engineering Khalifa University Abu Dhabi, UAE · Department of Mechanical Engineering University of Windsor Windsor, Canada
ViViD-5K: Vineyard vision dataset for field-based berry detection and segmentation and grape cluster closure estimation
May 23, 2026Xiangzhi Tong, Chengrui Zhang, Mac Flaherty +5Agricultural Image Analysis Plant Phenotyping
School of Electrical and Computer Engineering, Cornell University, NY, USA. · Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, UK. · Horticulture Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA. +1
AgroVG: A Large-Scale Multi-Source Benchmark for Agricultural Visual Grounding
May 21, 2026Haocheng Li, Juepeng Zheng, Zenghao Yang +5Agricultural Image Analysis Set Prediction
China Agricultural University · Sun Yat-sen University · National Supercomputing Center in Shenzhen +3
3D Reconstruction and Knowledge Distillation to Improve Multi-View Image Models to Explore Spike Volume Estimation in Wheat
May 20, 2026Olivia Zumsteg, Jannis Widmer, Yann Bourdé +4Agricultural Image Analysis Plant Phenotyping
ETH Zurich, Switzerland · Swiss Data Science Center, ETH Zurich, Switzerland
FruitEnsemble: MLLM-Guided Arbitration for Heterogeneous ensemble in Fine-Grained Fruit Recognition
May 20, 2026Enhui Yu, Junhui Li, Ruitong Lu +2Agricultural Image Analysis Ensemble Learning
University of Science and Technology Liaoning · Chuzhou University · Yeshiva University
PlantPose: Universal Plant Skeleton Estimation via Tree-constrained Graph Generation
May 18, 2026Xinpeng Liu, Hiroaki Santo, Yosuke Toda +1Agricultural Image Analysis Graph Structure Learning
Graduate School of Information Science and Technology, Osaka University, Suita, Osaka, 5650871, Japan. · Phytometrics, Hamamatsu, Shizuoka, 4350036, Japan. · Institute of Transformative Bio-Molecules, Nagoya University, Nagoya, Aichi, 4648601, Japan.
SAGE: Scalable Agentic Grounded Evaluation for Crop Disease Diagnosis
May 10, 2026Muhammad Arbab Arshad, Tirtho Roy, Yanben Shen +7VLM Evaluation Agricultural Image Analysis
Iowa State University, USA · New York University, USA
Pest-Thinker: Learning to Think and Reason like Entomologists via Reinforcement Learning
May 7, 2026Xueheng Li, Yu Wang, Tao Hu +6Agricultural Image Analysis VLM Reasoning
Institute of Intelligent Machines, Hefei Institute of Physical Science, Chinese Academy of Sciences · University of Science and Technology of China · 4Hefei University of Technology +1
ReLeaf: Benchmarking Leaf Segmentation across Domains and Species
May 5, 2026Robert Martinko, Daniel Steininger, Julia Simon +2Agricultural Image Analysis Instance Segmentation
1AIT Austrian Institute of Technology, Center for Vision, Automation & Control · University of Applied Sciences Technikum Wien, Computer Science & Applied Mathematics
CropVLM: A Domain-Adapted Vision-Language Model for Open-Set Crop Analysis
May 5, 2026Abderrahmene Boudiaf, Sajd JavedOpen-Vocabulary Object Detection Agricultural Image Analysis
Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
Lightweight Distillation of SAM 3 and DINOv3 for Edge-Deployable Individual-Level Livestock Monitoring and Longitudinal Visual Analytics
Apr 29, 2026Haiyu Yang, Miel HostensAgricultural Image Analysis Edge ML
College of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14853 USA
Evaluation of Convolutional and Transformer-Based Detectors for Weed Detection in Tomato Plantations
Apr 29, 2026Alcides Toledo Espinosa, Gerardo Antonio Álvarez Hernández, Ángel Eduardo Zamora-Suárez +2Agricultural Image Analysis Weed Detection
Instituto Politécnico Nacional · CIDETEC-IPN · Mexico City, Mexico +1
FruitProM-V2: Robust Probabilistic Maturity Estimation and Detection of Fruits and Vegetables
Apr 28, 2026Rahul Harsha Cheppally, Sidharth Rai, Sudan Baral +2Agricultural Image Analysis Object Detection
Kansas State University, Manhattan, Kansas, USA
Agri-CPJ: A Training-Free Explainable Framework for Agricultural Pest Diagnosis Using Caption-Prompt-Judge and LLM-as-a-Judge
Apr 26, 2026Wentao Zhang, Qi Zhang, Mingkun Xu +6Agricultural Image Analysis VLM Reasoning
Business School, Shandong University of Technology, Shandong, China · Faculty of Data Science, City University of Macau, Macau SAR, China · Guangdong Institute of Intelligent Science and Technology, Zhuhai, China +2
PestVL-Net: Enabling Multimodal Pest Learning via Fine-grained Vision-Language Interaction
Apr 19, 2026Xueheng Li, Tao Hu, Ke Cao +5Agricultural Image Analysis Vision-Language Models
Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences · University of Science and Technology of China · 3Zhongke Hefei Institute of Technology Innovation Engineering
StomaD2: An All-in-One System for Intelligent Stomatal Phenotype Analysis via Diffusion-Based Restoration Detection Network
Apr 18, 2026Quanling Zhao, Meng'en Qin, Yanfeng Sun +2Agricultural Image Analysis Plant Phenotyping
1Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms, School of Mathematics and Statistics, Henan University, Kaifeng 475004, China. · 3State Key Laboratory of Cotton Biology, School of Life Sciences, Henan University, Kaifeng 475004, China. · 2International Joint Research Laboratory for Global Change Ecology, School of Life Sciences, Henan University, Kaifeng 475004, China.
SWNet: A Cross-Spectral Network for Camouflaged Weed Detection
Apr 17, 2026Henry O. Velesaca, Luigi Miranda, Angel D. SappaAgricultural Image Analysis Weed Detection
ESPOL Polytechnic University, Campus Gustavo Galindo, 090902, Guayaquil, Ecuador · Software Engineering Department, Research Center for Information and Communication Technologies (CITIC-UGR), University of Granada, 18071, Granada, Spain · Computer Vision Center, Universitat Aut`onoma de Barcelona, 08193-Bellaterra, Barcelona, Spain
SPROUT: A Scalable Diffusion Foundation Model for Multi-Crop Plant Phenotyping
Mar 29, 2026Shuai Xiang, James Burridge, Shouyang Liu +4Agricultural Image Analysis Plant Phenotyping
Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Midori-cho, Nishitokyo, Tokyo 188-0002, Japan · Engineering Research Center of Plant Phenotyping, Ministry of Education; Jiangsu Collaborative Innovation Center for Modern Crop Production; Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing 210095, China · National Key Laboratory of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China +3
Using Vision Language Foundation Models to Generate Plant Simulation Configurations via In-Context Learning
Mar 9, 2026Heesup Yun, Isaac Kazuo Uyehara, Earl Ranario +4VLM Evaluation Agricultural Image Analysis
University of California, Davis
LeafTrackNet: A Deep Learning Framework for Robust Leaf Tracking in Top-Down Plant Phenotyping
Dec 15, 2025Shanghua Liu, Majharulislam Babor, Christoph Verduyn +4Agricultural Image Analysis Plant Phenotyping
Leibniz Institute for Agricultural Engineering and Bioeconomy, Germany · BASF Belgium Coordination Center CommV, Belgium · BASF Metabolome Solutions GmbH, Germany +2
DWFF-Net: A Multi-Scale Farmland System Habitat Identification Method with Adaptive Dynamic Weight Feature Fusion
Nov 11, 2025Kesong Zheng, Zhi Song, Peizhou Li +4Agricultural Image Analysis Multi-Scale Feature Fusion
National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan, China · College of Science, Shenyang Agricultural University, Liaoning, China · College of Engineering, Shenyang Agricultural University, Liaoning, China +2
PlantExpertVQA: A Visual Question Answering Dataset for Benchmarking Vision-Language Models in Plant Science
Aug 23, 2025Syed Nazmus Sakib, Nafiul Haque, Mohammad Zabed Hossain +1VLM Evaluation Agricultural Image Analysis
Department of Robotics and Mechatronics Engineering, University of Dhaka · Department of Botany, University of Dhaka
$T^{3}S$ : Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series
Jun 15, 2025Mehmet Ozgur Turkoglu, Selene Ledain, Jeffrey Zweidler +2Agricultural Image Analysis Agricultural Remote Sensing
Earth Observation of Agroecosystems Team, Agroscope, Switzerland · ETH Zurich, Switzerland
Comparing YOLOv11 and YOLOv8 for instance segmentation of occluded and non-occluded immature green fruits in complex orchard environment
Oct 24, 2024Ranjan Sapkota, Manoj KarkeeAgricultural Image Analysis Instance Segmentation
Washington State University Department of Biological Systems Engineering

May 26, 2026, cs.CVJ/K move · Enter open · S save

Hallucination Behavior in Multimodal LLMs Across Agricultural Image Interpretation and Generation Tasks

Partho Ghose, Al Bashir, Prem Raj, Azlan Zahid

Department of Biological & Agricultural Engineering, Texas A&M AgriLife Research, Texas A&M University System, Dallas, TX 75252, USA.

Agricultural Image Analysis VLM Hallucination Multimodal Large Language Models Multimodal Hallucination T2I Generation Evaluation

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Large Language Models (LLMs) are being rapidly adopted in agricultural imaging applications, ranging from crop interpretation to synthetic field image generation. However, these models frequently exhibit hallucinations outputs that appear confident yet deviate from biological or environmental reality potentially leading to misinformed agronomic insights. This study investigates such hallucinations in two complementary directions: image-to-text, where LLMs interpret crop or field imagery to describe conditions such as biotic and abiotic stresses, and text-to-image, where models generate synthetic agricultural scenes based on descriptive prompts. We examine errors involving biological inconsistency, contextual inaccuracy, and agronomic implausibility, evaluating the outputs under domain-informed criteria across multiple imaging modalities. Our analysis identifies recurring hallucination patterns within both interpretive and generative tasks. In image interpretation, LLMs (e.g., Gemma, LLAVA, Qwen, and MiniCPM) achieved modest zero-shot accuracy (63 to 75 percent), whereas few-shot prompting improved performance up to 86.8 percent, exhibiting false detections and missed infections, indicating residual hallucination effects. In text-to-image tasks, advanced models such as GPT-5 and Gemini 2.5 Flash generate up to 91 percent biologically inconsistent scenes under relaxed prompt constraints, revealing fundamental weaknesses in current LLMs. This systematic assessment of visual reasoning and generation offers critical insights toward enhancing the reliability and trustworthiness of LLM-based agricultural imaging platforms.

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