Singapore

Rice is one of the most widely cultivated crops globally and has been developed into numerous varieties. The quality of rice during cultivation is primarily determined by its cultivar and characteristics. Traditionally, rice classification and quality assessment rely on manual visual inspection, a process that is both time-consuming and prone to errors. However, with advancements in machine vision technology, automating rice classification and quality evaluation based on its cultivar and characteristics has become increasingly feasible, enhancing both accuracy and efficiency. This study proposes a real-time evaluation mechanism for comprehensive rice grain assessment, integrating a one-stage object detection approach, a deep convolutional neural network, and traditional machine learning techniques. The proposed framework enables rice variety identification, grain completeness grading, and grain chalkiness evaluation. The rice grain dataset used in this study comprises approximately 20,000 images from six widely cultivated rice varieties in China. Experimental results demonstrate that the proposed mechanism achieves a mean Average Precision (mAP) of 99.14\% in the object detection task and an accuracy of 97.89\% in the classification task. Furthermore, the framework attains an average accuracy of 97.56\% in grain completeness grading within the same rice variety, contributing to an effective quality evaluation system.

AAAI 2026

An Overall Real-Time Mechanism for Classification and Quality Evaluation of Rice

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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.

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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

Pig counting is an important task in pig marketing and farming. Most of the existing methods use supervised object detection for counting. However, supervised pig counting relies heavily on expensive data labeling, especially in the dense counting scenario. To address this issue, we propose BU-CLIP, which adopts the Contrastive Language Image Pre-training model (CLIP) for unsupervised pig counting. We tailor the image encoder of CLIP and the loss function to make it more suitable for pig counting. Our method replaces CLIP's image encoder with a bilinear model combining ConvNeXt and ResNet50 backbones, while the pooling operation is performed via a multi-head attention module. We reconstruct the loss function by using a multi-modal full-ranking loss, which captures the intrinsic correspondence between text and image. The proposed model is tested on a dense pig counting dataset, and extensive experiments demonstrate that our method outperforms unsupervised state-of-the-art counting methods and achieves almost the same results as supervised state-of-the-art counting methods.

Bilinear Magic Meets CLIP: Unsupervised Learning in Pig Counting Scenario

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.

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

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.

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