Pre-harvest protein and moisture content in wheat jointly determine market value and optimal harvest timing. However, existing detection methods are limited to expensive laboratory spectrometers or handheld devices requiring threshed grains, rendering practical in-field quality assessment inaccessible to smallholder farmers. Achieving low-cost, in-situ measurement requires overcoming three critical challenges: strong water absorption in fresh spikes masking weaker protein signals, morphological variations across cultivars causing spectral baseline shifts, and scattering-related distortions introduced by variable probe contact during handheld operation. To address these issues, we present WheatScout, a co-designed system integrating optimized optics with task-aware learning. The hardware features a diffuse optical path to minimize geometric bias, while the processing pipeline employs a moisture-first framework to explicitly decouple water interference prior to protein estimation. Furthermore, we integrate domain-invariant representation learning to enhance cross-cultivar generalization and a trainable scattering correction module to compensate for residual field-trial errors. During a 15-day harvest window, we systematically collected 600 wheat ear samples, covering 10 different varieties and five functional categories, thus constructing a dataset with broad spatiotemporal representativeness and varietal diversity. On this dataset, WheatScout achieved an R2 > 97% for moisture and an R2 > 92% for protein, which are comparable to lab-grade spectrometers at two orders of magnitude lower cost.

Flour adulteration poses significant health risks and economic losses for consumers, yet current detection methods are often impeded by high costs, limited sensitivity, and impractical laboratory requirements, leaving end users vulnerable. The difficulty is exacerbated by the need for extremely low detection limits, often at parts-per-million (ppm) levels, to identify adulterants. To address these challenges, we introduce FlourSpec, a low-cost and user-friendly system designed for on-site detection of flour adulteration at the ppm level. FlourSpec employs a spectral reconstruction algorithm to extract pertinent information from coarse-grained spectral data collected using a low-cost spectrometer. Recognizing that existing spectral reconstruction algorithms often struggle with significant errors that obscure trace adulterant information, we develop a novel end-to-end architecture that balances spectral fidelity and classification performance while minimizing the propagation of reconstruction errors. Additionally, we incorporate a hybrid attention mechanism to capture harmonic correlations within the spectra, effectively suppressing cross-band reconstruction errors. A supervised contrastive learning module is also incorporated to enhance the discriminability of trace features. Experimental evaluations demonstrate that FlourSpec achieves 97.44% accuracy in detecting multiple adulteration types and 93.12% accuracy in identifying various BPO concentrations. Notably, it is only 0.63% lower than expensive solutions and 18.74% higher than baseline systems at the same price, highlighting its effectiveness for trace-level detection.

Estimating the soluble solids content (SSC) in fruits is essential for meeting consumer expectations, ensuring the quality of processed products, and minimizing food waste. Current methods often rely on destructive sampling, expensive equipment, and complex procedures, which limit their practicality. This paper presents VNiScan-Fruit, a non-invasive, low-cost, and easy-to-use optical sensing system that utilizes visible-near-infrared (Vis-NIR) spectroscopy to estimate SSC by analyzing the interaction of light with fruit tissues to generate characteristic absorption spectra. Our approach diverges from traditional high-precision spectrometers, employing commercial LEDs and photodetectors (PD) to construct the optical sensing unit. We design and implement innovative ring-shaped rubber enclosure, interference elimination algorithms, and reconstruction strategy to extract low-dimensional reflectance spectral features from various fruits. These features are then processed using a specially designed nonlinear regression model, incorporating sucrose values obtained from a commercial refractometer to estimate SSC accurately. We tested VNiScan-Fruit on a range of fruits with diverse peel characteristics, demonstrating its ability to penetrate exocarps and effectively analyze fruits with rough surfaces and delicate tissues. The system achieved normalized mean absolute errors (NMAE) of 8%, imperceptible to consumers in sweetness difference, and remained stable under varying lighting and temperature conditions. Our findings highlight the potential of VNiScan-Fruit as a practical tool for non-destructive fruit quality assessment.

Meat adulteration is a significant problem that can pose health risks economic losses to consumers. Current detection methods are hindered by high costs, limited capabilities, or time-consuming sample preparation, making them only accessible in laboratory tests and can not protect the safety of end-users. This paper introduces MeatSpec, a low-cost and user-friendly system for detecting meat adulteration using spectral imaging, to move the adulteration inspection out of laboratories. MeatSpec employs a multispectral camera to reduce costs while quickly capturing spectral images, but this leads to a decrease in spectral resolution and coverage. To solve this challenge, the system uses spectral reconstruction technology and innovative designs tailored for meat adulteration detection. This includes involving adulteration-related prior information during the reconstruction training phase and incorporating contrastive learning to enlarge the distances among reconstructed samples belonging to various adulteration types. Additionally, we devise distinct feature extractors for different bands based on characteristics of the reconstructed spectra and employ knowledge distillation to mitigate error in full-band reconstructed spectra while capturing features related to adulteration. Further, we extend our system to MeatSpec-G to improve its generalizability to varied adulteration conditions and unknown adulterants. To achieve this, we first propose a feature alignment-based training scheme to reduce the feature gap among samples of diverse concentrations and admixture patterns. Then, we propose a cascaded open-set recognition framework that decouples uncertainty quantification and anomaly feature discrimination, to address the limitations of softmax confidence in detecting distribution shifts and reconstruction artifacts. Experimental evaluations on 347 paired spectral images demonstrate that our system achieves a 91.06% accuracy in detecting multiple adulteration types, merely 7.78% inferior to the expensive professional solution, yet 21.58% superior to the baseline at the same price point. Moreover, our system can generalize to achieve an 88.89% detection accuracy in unknown adulteration conditions with a 27.78% improvement, and an 83.33% detection accuracy for unknown adulterants.

Nasogastric tube (NGT) feeding supports over 10 million patients globally, particularly those with stroke-induced dysphagia, Parkinson's disease, and cognitive impairments. However, current practices face three fundamental limitations. First, caregivers often lack knowledge of the nutritional content of homemade blended meals, leading to high malnutrition rates among long-term NGT patients. Second, commercial solutions struggle to analyze blended meals in large containers due to light path distortion. Third, gastric residual volume (GRV) assessments rely on subjective nurse evaluations, resulting in incomplete records and increased feeding intolerance. To address these challenges, this demo introduces NutriBump, an innovative system designed to enhance NGT feeding through closed-loop control. Utilizing advanced spectral analysis technology, the system accurately assesses food nutrients and employs multi-modal sensors for automatic gastric fluid aspiration and analysis. By integrating long-term nutritional intake, digestion data, and health status, NutriBump leverages large language models and AI agents to generate personalized nutrition reports automatically. This closed-loop nutrition management solution improves the accuracy of blended meal analysis and reduces reliance on subjective digestion assessments.

Photovoltaic (PV) defect detection is crucial for preventing power efficiency loss and fire hazards. The industry primarily relies on the fusion of infrared and visible images for defect localization and diagnosis. However, current detection methods exhibit poor generalizability in new site environments or with altered imaging setups. While recent infrared and vision foundation models (FM) facilitate domain-invariant feature maps extraction, directly concatenating them and fine-tuning achieves limited generalizability gain to PV defect detection, due to the asymmetric dual-modal semantics of defects. In this paper, we present the first large infrared-visible model DD-LIVM to enable cross-domain defect detection. The key innovation of DD-LIVM lies in its defect-specific three-step fine-tuning strategy, which utilizes alternating modality masking. Prior to feature fusion and joint fine-tuning, the infrared and visible FM encoders are alternately masked and optimized to enhance their individual semantic utility for defect localization visibility and classification granularity, with feature distances among different defect types regulated through contrastive learning. This approach allows for the extraction of generalizable and defect-specific feature maps. Moreover, for practical employment of DD-LIVM, we propose a domain-agnostic spatial alignment algorithm for infrared-visible images before dual-modal fusion, and develop source data augmentation and adaptive detection head selection schemes based on defects' infrared characteristics to further enhance the generalizability. Extensive experiments on 7,078 dual-modal images from 9 real-world scenarios across 4 cities' PV stations demonstrate that DD-LIVM achieves an accuracy of 87.7% for cross-domain defect detection, surpassing state-of-the-art methods by 17.3%.

Monitoring sashimi freshness,i.e., histamine levels, in showcases poses a critical challenge for sushi restaurants and fresh food stores. Current histamine monitoring methods involve labor-intensive chemical experiments or expensive devices, making affordable on-site monitoring difficult. This paper proposes FreshSpec, a low-cost and automatic spectral imaging system capable of precisely monitoring histamine levels in sashimi with minimal human intervention. The low concentration of histamine, combined with the potential for other ingredients to mask its spectral characteristics, complicates precise histamine level predictions using coarse or redundant spectral data from low-cost devices. To address this issue, FreshSpec employs an innovative feature- wise spectral reconstruction (SR) framework that effectively eliminates irrelevant and redundant data while preserving critical histamine-related spectral features. Specifically, we redefine the SR reconstruction target by utilizing features derived from the encoder of the spectral foundation model that is enhanced to focus on histamine-related spectral features. Furthermore, inspired by the monotonic accumulation properties of histamine over time, we propose a histamine regression model with unsupervised continual adaptation to new sashimi samples during practical deployment. Experimental results from 240 samples of salmon, tuna, and snapper demonstrate that FreshSpec achieves an R2 of 0.9319 and an RMSE of 3.101 mg/100 g, comparable to laboratory spectral imaging systems, while outperforming baseline schemes with a 46.95% RMSE reduction and a 0.1631 R2 improvement.

Meat adulteration is a significant problem that can pose health risks economic losses to consumers. Current detection methods are hindered by high costs, limited capabilities, or time-consuming sample preparation, making them only accessible in laboratory tests and can not protect the safety of end-users. This paper introduces MeatSpec, a low-cost and user-friendly system for detecting meat adulteration using spectral imaging, to move the adulteration inspection out of laboratories. MeatSpec employs a multispectral camera to reduce costs while quickly capturing spectral images, but this leads to a decrease in spectral resolution and coverage. To solve this challenge, the system uses spectral reconstruction technology and innovative designs tailored for meat adulteration detection. This includes involving adulteration-related prior information during the reconstruction training phase and incorporating contrastive learning to enlarge the distances among reconstructed samples belonging to various adulteration types. Additionally, we devise distinct feature extractors for different bands based on characteristics of the reconstructed spectra and employ knowledge distillation to mitigate error in full-band reconstructed spectra while capturing features related to adulteration. Experimental evaluations on 347 paired spectral images demonstrate that our system achieves a 91.06% accuracy in detecting multiple adulteration types, merely 7.78% inferior to the expensive professional solution, yet 21.58% superior to the baseline at the same price point.

The calculation of calorie consumption is of paramount importance for the human diet and health management. Most existing solutions use image-processing techniques to identify the food type and refer to the nutrition table to compute the total calorie, which is quite challenging to differentiate foods that look the same but contain vastly different quantities of calories. To address this issue, we propose to leverage near-infrared spectroscopy (NIRS) to derive the concentration of nutrients based on the unique absorption spectrum of foods. Instead of using the professional NIRS system that is bulky, expensive, and impractical for daily use by nonexpert users, we develop a low-cost portable NIRS system using commercial LEDs. As the quality of the signals of the low-power LEDs is relatively poor, we carefully design modulation schemes and interference elimination algorithms to improve the signal quality and remove the interference. Extensive experiments show that NIRSCAM outperforms the image-based baseline in achieving more accurate calorie estimation, especially for look-alike foods and is robust to various environmental factors.