Anomaly detection methods leveraging unsupervised learning are expected to find broad application across diverse sectors, especially in inspecting defects of industrial products. This potential is largely due to their resilience against the unpredictability of anomaly types and the imbalances of learning data across classes. Central to these methods is the premise that feature extractors or image reconstructors, when trained solely on normal data, are incapable of fully replicating the features or inputs of anomalous data. As a result, anomalies could be detected by thresholding the deviations in the extracted features or the reconstructed outputs. However, finding an optimal threshold that effectively separates anomalous from normal data remains a substantial challenge in real-world scenarios. The inherent variability within normal data itself is a significant factor contributing to this challenge. In this study, we introduce a simple yet powerful intra-class variance suppression framework that enables anomaly detection models to suppress intra-class variability by learning compact representations of normal data. We evaluate the proposed framework on three established unsupervised anomaly detection paradigms, namely generative adversarial learning, knowledge distillation, and reverse distillation. Experiments are conducted on multiple benchmark datasets, including handwritten digit images, natural object images, industrial anomaly detection benchmarks, and two additional real-world industrial datasets. The results demonstrate that the proposed framework consistently improves anomaly detection and localization performance, particularly in practical industrial quality inspection scenarios.

Abstract This study proposes a non-invasive method to estimate both color and sensory attributes of Shine Muscat grapes from standard camera images. First, we focus on color estimation by integrating a Vision Transformer (ViT) feature extractor with interquartile range (IQR)-based outlier removal. Experimental results show that our approach achieves 97.2% accuracy, significantly outperforming Convolutional Neural Network (CNN) models. This improvement underscores the importance of capturing global contextual information to differentiate subtle color variations in grape ripeness. Second, we address human sensory evaluation by collecting questionnaire responses on 13 attributes (e.g., “Sweetness,” “Overall taste rating”), each rated on a five-point scale. Because these ratings tend to cluster around midrange values (labels “2,” “3,” and “4”), we initially limit the dataset to the extreme labels “1” (“lowest grade”) and “5” (“highest grade”) for binary classification. Three attributes—“Overall color,” “Sweetness,” and “Overall taste rating”—exhibit relatively high classification accuracies of 79.9%, 75.1%, and 75.7%, respectively. By contrast, the other 10 attributes reach only 50%–66%, suggesting that subjective variations and limited visual cues pose significant challenges. Overall, the proposed approach demonstrates the feasibility of an image-based system that integrates color estimation and sensory evaluation to support more objective, data-driven harvest timing decisions for Shine Muscat grapes.

The size of fruits significantly impacts their market value. For lemons, the size grade is determined by the cross-sectional diameter, necessitating the harvest of lemons that meet specific size criteria. Current manual measurement methods, involving metal rings, may degrade lemon quality and are labor-intensive. This study proposes a non-contact method for estimating lemon diameter using RGB-D images and deep learning. Our approach detects lemons and their tips, utilizing depth information and the position of the tip relative to the boundary of detected lemon mask to estimate size irrespective of the lemon’s tilt. With 2,038 images of indoor and outdoor green lemons, our method achieved a Mean Absolute Error (MAE) of 2.94 mm for fully visible lemons, though accuracy decreased with occlusions. These findings suggest that accurate, tilt-independent lemon size measurement is feasible in field conditions, providing a valuable tool for harvest support.

Grapes, ranking among the top fruits globally, undergo essential grading processes to ensure quality and market readiness. However, conventional grading methods rely heavily on subjective human expertise, leading to inconsistencies and inefficiencies. To address this, we present a novel grape grading system integrating computer vision, artificial intelligence (AI), and IoT technologies. Our system utilizes deep neural networks (DNNs) based prediction model to accurately grade the grapes, and a sensing station equipped with cameras and weight sensors to capture images and weight data of grape bunches. Our research focuses on Shine Muscat grapes, a prominent variety in Japan. The system implements a multimodal grading approach that combines image and weight information. Our system offers portability, affordability, and operational versatility that prioritizes the safety of the grape bunch. Experimentation with different DNN architectures and input data configurations reveals the superiority of ResNet-18 for grading classification using multiple images from different angles, achieving $85.71 \%$ accuracy. On the other hand, when the weight information is included with the images, ResNet-50 performs the best with $82.86 \%$ accuracy. Additionally, we analyze mispredictions across grade classes, highlighting the model’s challenges in discerning subtle differences between closely ranked grades.

This study identifies challenges in user interface design for applying Augmented Reality (AR) technology to support work for individuals with intellectual disabilities. The main focus is on difficulty in self-assessing fatigue levels. The research methodology involved conducting experiments using HoloLens 2, analyzing changes in biometric information during fatigue, and examining the relationship between information display position and head orientation. Results indicate that changes in head height and orientation could potentially serve as fatigue indicators. In conclusion, while AR technology is effective in supporting work for individuals with intellectual disabilities, special considerations are necessary. Fatigue detection using biometric information and optimization of information display positions are crucial, and these findings may lead to safer and less burdensome use of AR.

This study presents an advanced automatic thrips counting and classification system, leveraging a novel SuperResolution (SR) technique, named KSVD_DR to enhance image analysis accuracy. We developed and validated detection models using a diverse dataset that included high-resolution scanned images and smartphone-captured images of blue and yellow traps, both with and without plastic wrap. This approach ensured robust performance across various real-world agricultural settings. The application of SR improved the detection accuracy from $66.5 \%$ to $\mathbf{8 9. 7} \%$, as measured by the mean Average Precision at $\mathbf{5 0 \%}$ Intersection over Union (mAP50). The overall testing accuracy achieved was $81.2 \%$, with specific accuracies of $80.3 \%$ for images with plastic wrap and $83.4 \%$ for those without confirming the system’s effectiveness in both laboratory and field conditions. Additionally, SR processing enhanced thrips classification accuracy from $58.5 \%$ to $\mathbf{6 5. 3 \%}$ across six distinct thrips classes, demonstrating its potential to refine species-specific identification. Future developments will focus on expanding outdoor data collection to validate and enhance system performance under varying environmental conditions and to improve detection accuracy at higher confidence levels. The study also aims to refine the classification model by incorporating more diverse data inputs and exploring advanced machine learning techniques, enhancing the ability to differentiate between thrips species effectively.

Currently, skilled farmers judge the ripeness of the Shine Muscat grape variety by looking at the color on the surface of the grapes. However, the color of Shine Muscat grapes does not change much as they grow, and there are individual differences in the way the color is perceived. Furthermore, the same color can look very different depending on the exposure to sunlight and shadows. Therefore, there is a need for a system that can quantitatively determine the color of Shine Muscat grapes to pass on the harvesting techniques of experienced farmers to amateurs and inexperienced farmers. This research aims to improve the accuracy of the color estimation of Shine Muscat grapes using deep learning. We propose a method to estimate the color of individual grapes using a color estimation model with a self-attention mechanism, from which the color of the whole bunch is estimated. A Vision Transformer model with a self-attention mechanism was found to improve the color estimation accuracy to $96.9 \%$. Furthermore, by eliminating outliers using the interquartile range, a color estimation accuracy of $97.2 \%$ could be achieved, demonstrating the effectiveness of the new color estimation model.

Recent advancements in deep neural networks (DNN) and augmented reality (AR) have improved the efficiency and automation of agriculture. This study proposes an AR grape thinning support system to assist in grape thinning operations. The proposed system uses DNN to predict grape berries that need to be thinned and uses the optical see-through Head-Mounted Display (HMD) HoloLens 2 to superimpose contour information over the real target berry, making it easy for users to identify the berry to be thinned. Additionally, the hand-tracking function of HoloLens 2 is utilized to monitor the thinning operation in real-time and provide voice instructions to improve work efficiency and user experience. The evaluation experiment compared three interfaces: “image only”, “image with contour overlay”, and “image with contour overlay and voice instructions”, evaluating using the metric of time taken to thin one grape cluster and the usability and user experience. The results showed that the image with contour overlay and voice instructions could significantly improve usability.

We have identified two problems for people with intellectual disabilities when using Augmented Reality (AR) devices. One is not noticing the presented information and the other is the difficulty in pressing buttons in the AR space. To solve the first problem, we propose two methods to attract a user's attention, one is to place the information window near the user's gaze point, and dynamically adapt the window based on eye-tracking data. The other is to blink the information window. As a solution to the second problem, we propose a method that detects the user's button-pressing gesture, and when the gesture is detected, the system presses the button on behalf of the user. The effectiveness of the proposed methods was validated through the user studies with 7 participants of varying degrees of intellectual disability involved.

Abstract The color of a bunch of grapes is a very important factor when determining the appropriate time for harvesting. However, judging whether the color of the bunch is appropriate for harvesting requires experience and the result can vary by individuals. In this paper, we describe a system to support grape harvesting based on color estimation using deep learning. To estimate the color of a bunch of grapes, bunch detection, grain detection, removal of pest grains, and color estimation are required, for which deep learning-based approaches are adopted. In this study, YOLOv5, an object detection model that considers both accuracy and processing speed, is adopted for bunch detection and grain detection. For the detection of diseased grains, an autoencoder-based anomaly detection model is also employed. Since color is strongly affected by brightness, a color estimation model that is less affected by this factor is required. Accordingly, we propose multitask learning that uses metric learning. The color estimation model in this study is based on AlexNet. Metric learning was applied to train this model. Brightness is an important factor affecting the perception of color. In a practical experiment using actual grapes, we empirically selected the best three image channels from RGB and CIELAB (L*a*b*) color spaces and we found that the color estimation accuracy of the proposed multi-task model, the combination with “L” channel from L*a*b color space and “GB” from RGB color space for the grape image (represented as “LGB” color space), was 72.1%, compared to 21.1% for the model which used the normal RGB image. In addition, it was found that the proposed system was able to determine the suitability of grapes for harvesting with an accuracy of 81.6%, demonstrating the effectiveness of the proposed system.

The color of grape bunches is a significant factor when harvesting grapes at the appropriate timing. Judging the suitable color for shipment requires experience and varies from one person to another. We herein describe a support system for grape harvesting based on color estimation. To estimate the color of a bunch of grapes, bunch detection, grain detection, removal of diseased grains, and color estimation should be performed. Models based on deep learning are employed for this series of processes. Since color is strongly affected by sunlight, we propose a multitask model that considers sunlight exposure to achieve a robust color estimation model that exhibits decreased sensitivity to sunlight. Our results show that the color estimation accuracy of the model is 76% when sunlight exposure is not considered and 81% when sunlight exposure is considered. In addition, we performed a practical field test of the developed harvest support system in an actual grape field. The results show that our support system can determine the appropriateness of grape harvest with an accuracy of 90%, demonstrating the effectiveness of the system.

Inflorescence trimming is a crucial process to produce high-quality table grapes. It can eliminate nutrient competition in a bunch and makes it less vulnerable to disease development. After trimming, the remaining part of the inflorescence should have a target length decided by the grape variety. This is challenging for novice farmers because of the time constraint. The farmer needs to finish trimming the inflorescence before the berries develop. This paper proposes a novel end-to-end inflorescence length measurement method for supporting a trimming process with augmented reality technology. The proposed technique makes use of the state-of-the-art deep neural network model for detecting the inflorescence area, as well as the scissors from the images captured with a camera installed on an optical see-through head-mounted display. A new algorithm is designed to estimate the length of the remaining inflorescence with the screw of the scissors loop as the calibrator. The estimated length is then visualized on the head-mounted display to support the farmer in performing the trimming correctly and efficiently. The experiment, conducted with real inflorescence trimming tasks, shows that the mean absolute error of the length estimation is only 0.19 cm, which is small enough for use in real applications.

Augmented reality (AR) is a technology that expands information by superimposing digital information, such as virtual objects, on the real world using smartphones, smart glasses, and head-mounted displays. It is used in a variety of situations. In this paper, we propose a system that allows vine farmers to investigate effectively the vegetation condition of trellising-style vineyards using a head mounted display and AR technology. The experiment results show that by using a hybrid navigation approach include showing the whole vineyard in a small window and showing the details only, when necessary, the proposed system enable the farmers to move to a location with concern accurately.