Singapore

High-throughput plant phenotyping is an emerging interdisciplinary field of research that lies at the intersection of computer vision, plant science, artificial intelligence, data analytics and visualization, and genomics. To develop novel algorithms that advance image-based high-throughput plant phenotyping research with an aim of precision farming and global food security, the publication of benchmark datasets is indispensable. Thus, this paper introduces ReproPheno, a large-scale, open-source dataset comprising multi-view image sequences of plants spanning their entire life cycle captured using three modalities of cameras, i.e., visible light, fluorescent, and hyperspectral, within the LemnaTec Scanalyzer 3D high-throughput plant phenotyping facility. By enabling comprehensive analysis across multiple sensing modalities and temporal scales, this dataset opens the door to several novel research directions in computer vision, including multimodal co-segmentation, hyperspectral dataset distillation, and 3D model reconstruction of living organisms with growing architectural complexity. Furthermore, the paper presents ReproPhenoNet, a novel algorithm that uses You Only Look Once (YOLO) deep neural-network-based object detector to detect flowers and fruits from visible light and hyperspectral image sequences, respectively. These detections form the foundation for the quantitative computation of reproductive-stage plant phenotypes.

AAAI 2026

ReproPheno and ReproPhenoNet: A Large-Scale Multimodal Benchmark Dataset and Deep Learning Framework for Reproductive-Stage Plant Phenotyping

deep neural networks.

multimodal image sequence analysis

reproductive-stage phenotyping

benchmark dataset

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Speaker anonymization aims to modify the speech signal in
order to protect the identity of a speaker while preserving
the linguistic content. Despite the increasing use of
children's voices in educational applications, such as oral
reading fluency (ORF) assessment, there is little work on
anonymization aspects. In this work, we investigate the
effectiveness of available speaker anonymization methods
drawing from traditional speech-production based approaches
and a neural codec based method. We investigate the
trade-off between privacy protection, measured as the
degree of anonymity, and utility preservation, which in the
current context of ORF assessment, includes the segmental
and suprasegmental features of children’s read speech
utterances. We report objective and subjective evaluations
using two child-speaker datasets: MPS and SpeechOcean. Our
objective evaluation results indicate that the
speech-production based method of vocal tract length
normalization coupled with pitch-transposition achieves the
best balance between privacy and utility. Subjective
listening results indicate that naturalness is achievable
across methods while the neural method fails to preserve
age characteristics, which are more easily controlled by
the speech-production driven methods.

Speaker Anonymization for Children's Oral Reading Assessment

Timely detection of plant diseases in greenhouse environments is essential for minimizing crop loss, reducing chemical use, and supporting sustainable production. This paper presents an AI-enabled, reconfigurable edge device designed for leaf-level disease assessment using multimodal sensing and embedded intelligence. A 3D-printed handheld prototype was developed with a custom PCB integrating VIS/NIR spectral sensors, a thermal module, and an RGB camera for noninvasive measurement of physiological stress. A lightweight deep learning model combined with handcrafted spectral indices performs fully on-device analysis to detect early indicators such as abnormal thermal patterns, pigment loss, and reflectance changes linked to infections. The system is built on a low-power Raspberry Pi platform and supports model reconfiguration for rapid adaptation to different crops and disease types. Real-time inference is achieved through a segmentation-based pipeline deployed on both an Android app and an Ubuntu graphical interface. Validation trials demonstrated early disease detection with previously collected images and an average latency of 2.52 seconds on Android and 2.19 seconds on Ubuntu. We are currently calibrating our framework in the greenhouse environment for real-time assessments. A web dashboard provides live visualization of thermal overlays, spectral response, and health scores. By combining custom hardware, multimodal sensing, and on-device AI, the system offers a portable and scalable solution for early disease detection in greenhouse crops.

AI-Enabled Reconfigurable Edge Device for Plant Health Assessment in Greenhouse Environment

Weed detection is a critical task in precision agriculture, as weeds significantly reduce crop yields and increase production costs. This work presents an efficient, low-cost, and low-power UAV-deployable weed detection pipeline that relies solely on consumer grade RGB cameras. We propose novel lightweight U-Net and Attention U-Net architectures optimized for real-time semantic segmentation on an edge device. To enhance segmentation accuracy, we integrate additional RGB derived features. Experiments on the CoFly-WeedDB detection dataset demonstrate that both architectures perform effectively on RGB imagery, with further improvements when incorporating hue and edge detection features. The proposed lightweight U-Net architectures, made more efficient through quantization, achieves IoU scores above 50% and Dice scores exceeding 60% on the CoFly-WeedDB dataset across multiple augmentation levels. These findings highlight the practicality of deploying lightweight deep learning models for precision weed detection in resource-constrained agricultural environments.

On-Edge Weed Detection Using Unmanned Aerial Vehicles

The performance of 3D reconstruction using Neural Radiance Fields (NeRFs) for outdoor phenotyping of plants is strongly influenced by the imaging modality used for data collection. We compare drone, handheld, and 360Â° ground robot datasets collected over soybean and mungbean plots, and evaluate reconstruction quality using 2D metrics PSNR, SSIM, LPIPS, and 3D geometric metrics precision, recall, and F1 score.
Drone imagery produced the highest geometric fidelity, handheld captures achieved the strongest 2D appearance quality, and the 360Â° captures lagged in both metrics due to spherical distortion and motion artifacts. The consistency of the drone-based reconstructions highlights its suitability for field-scale 3D modeling and positions it as a practical foundation for future phenotyping applications.

Drone-based 3D Reconstruction of Plants in Field Conditions Using Neural Radiance Fields (NeRFs)

Self-supervised learning (SSL) is attractive for plant disease detection as it can exploit large collections of unlabeled leaf images, yet most existing SSL methods are built on CNNs or vision transformers that are poorly matched to agricultural imagery. CNN-based SSL struggles to capture disease patterns that evolve continuously along leaf structures, while transformer-based SSL introduce quadratic attention cost from high-resolution patches. To address these limitations, we propose StateSpace-SSL, a linear-time SSL framework that employs a Vision Mamba state-space encoder to model long-range lesion continuity through directional scanning across the leaf surface. A prototype-driven teacherâ€“student objective aligns representations across multiple views, encouraging stable and lesion-aware features from labelled data. Experiments on three publicly available plant disease datasets show that StateSpace-SSL consistently outperforms the CNN- and transformer-based SSL baselines in various evaluation metrics. Qualitative analyses further confirm that it learns compact, lesion-focused feature maps, highlighting the advantage of linear state-space modelling for self-supervised plant disease representation learning.

StateSpace-SSL: Linear-Time Self-supervised Learning for Plant Disease Detection

Accurate crop yield prediction is crucial for global food security and agricultural planning. This study benchmarks modern tabular foundation models and automated machine learning frameworks across three diverse agricultural datasets: (1) soybean yields with 86,101 temporal sequences, (2) global multi-crop data with 28,242 samples across 101 countries, and (3) EU-27 regional crops with 8,656 samples and significant missing data. We evaluate TabPFNv2 (an improved implementation of the TabPFN architecture), AutoGluon, and PyCaret to determine which approach works best under different data conditions. Our results show that model performance is highly context-dependent. AutoGluon achieves superior results on large-scale complete data, PyCaret excels on diverse multi-crop scenarios, while TabPFNv2 demonstrates distinct advantages on datasets with missing values (about a two percentage point gain in $R^2$ on EU-27). These findings challenge assumptions about universally superior approaches and reveal that foundation models offer robust zero-shot prediction, especially valuable for handling incomplete dataâ€”a critical capability for real-world agricultural AI deployment.

Benchmarking Tabular Foundation Models for Agricultural Yield Prediction

Scrap quality directly affects energy use, emissions, and safety in steelmaking. Today, the share of non-metallic inclusions (contamination) is judged visually by inspectors - an approach that is subjective and hazardous due to dust and moving machinery. We present an assistive computer vision pipeline that estimates contamination (per percent) from images captured during railcar unloading and also classifies scrap type. The method formulates contamination assessment as a regression task at the railcar level and leverages sequential data through multi-instance learning (MIL) and multi-task learning (MTL). Best results include MAE 0.27 and R2 0.83 by MIL; and an MTL setup reaches MAE 0.36 with F1 0.79 for scrap class. Also we present the system in near real time within the acceptance workflow: magnet/railcar detection segments temporal layers, a versioned inference service produces railcar-level estimates with confidence scores, and results are reviewed by operators with structured overrides; corrections and uncertain cases feed an active-learning loop for continual improvement. The pipeline reduces subjective variability, improves human safety, and enables integration into acceptance and melt-planning workflows.

From Images to Decisions: Assistive Computer Vision for Non‑Metallic Content Estimation in Scrap Metal

Multimodal Procedural Planning (MPP) facilitates human learning by integrating multiple modalities, such as text and video, to enhance comprehension and execution of procedural tasks. Instructional Videos (IVs) are crucial in MPP as they provide rich visual and auditory cues, making complex procedures more accessible. Large Language Models (LLMs) have promised significant potential for MPP by generating context-aware procedural plans and bridging multimodal information gaps. However, major limitations are the high cost of dataset collection and the under-utilization of the vast number of IVs available on platforms such as YouTube, where procedural content remains largely unstructured. To address this gap, we propose a novel framework, Visually Grounded MPP (VG-MPP), which leverages publicly available raw IVs, thereby eliminating the need for manually curated datasets and enabling the use of a broader range of MPP content. Our method harnesses the zero-shot reasoning capability of LLMs, the video-to-text generation ability of video captioning models, and the text-to-video generation capability of diffusion models, selectively filtering unnecessary information to deliver concise and essential multimodal guidance. Thereby, enabling AI-assisted MPP and skill learning is critical for human-centric manufacturing, where multimodal reasoning can assist humans by adapting to human variability and procedural uncertainty. Comprehensive evaluations demonstrate that VG-MPP provides a substantial advantage over baselines, outperforming existing approaches without depending on any specifically curated dataset.

VG-MPP: Visually Grounded Multimodal Procedural Planning from Unstructured Instructional Videos

Reinforcement learning (RL) has demonstrated great potential in robotic operations. However, its data-intensive nature and reliance on the Markov Decision Process (MDP) assumption limit its practical deployment in real-world scenarios involving complex dynamics and long-term temporal dependencies, such as multi-robot manipulation. Decision Transformers (DTs) have emerged as a promising offline alternative by leveraging causal transformers for sequence modeling in RL tasks. However, their applications to multi-robot manipulations still remain underexplored. To address this gap, we propose a novel framework, Symbolically-Guided Decision Transformer (SGDT), which integrates a neuro-symbolic mechanism with a causal transformer to enable deployable multi-robot collaboration. In the proposed SGDT framework, a neuro-symbolic planner generates a high-level task-oriented plan composed of human-understandable symbolic subgoals. Guided by these subgoals, a goal-conditioned decision transformer (GCDT) performs low-level sequential decision-making for multi-robot manipulation. This hierarchical architecture enables structured, interpretable, and generalizable decision making in complex multi-robot collaborative tasks. We evaluate the performance of SGDT across zero-shot and few-shot scenarios across multiple benchmark tasks analogous to human-centric manufacturing processes. To our knowledge, this is the first work to explore DT-based technology for multi-robot manipulation.

Toward Reliable Multi-Robot Collaboration via a Symbolically-Guided Decision Transformer

Manufacturing planners face complex operational challenges that require seamless collaboration between human expertise and intelligent systems to achieve optimal performance in modern production environments. Traditional approaches to analyzing simulation-based manufacturing data often create barriers between human decision-makers and critical operational insights, limiting effective partnership in manufacturing planning. Our framework establishes a collaborative intelligence system integrating Knowledge Graphs and Large Language Model-based agents to bridge this gap, empowering manufacturing professionals through natural language interfaces for complex operational analysis. The system transforms simulation data into semantically rich representations, enabling planners to interact naturally with operational insights without specialized expertise. A collaborative LLM agent works alongside human decision-makers, employing iterative reasoning that mirrors human analytical thinking while generating precise queries for knowledge extraction and providing transparent validation. This partnership approach to manufacturing bottleneck identification, validated through operational scenarios, demonstrates enhanced performance while maintaining human oversight and decision authority. For operational inquiries, the system achieves near-perfect accuracy through natural language interaction. For investigative scenarios requiring collaborative analysis, we demonstrate the framework's effectiveness in supporting human experts to uncover interconnected operational issues that enhance understanding and decision-making. This work advances collaborative manufacturing by creating intuitive methods for actionable insights, reducing cognitive load while amplifying human analytical capabilities in evolving manufacturing ecosystems.

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Speaker Anonymization for Children's Oral Reading Assessment

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Exploring avenues to increase the relevance of high-throughput plant phenotyping