This study was conducted using a drone with advanced mobility to develop a unified sensor and communication system as a new platform for in situ atmospheric measurements. As a major cause of air pollution, particulate matter (PM) has been attracting attention globally. We developed a small, lightweight, simple, and cost-effective multi-sensor system for multiple measurements of atmospheric phenomena and related environmental information. For in situ local area measurements, we used a long-range wireless communication module with real-time monitoring and visualizing software applications. Moreover, we developed four prototype brackets with optimal assignment of sensors, devices, and a camera for mounting on a drone as a unified system platform. Results of calibration experiments, when compared to data from two upper-grade PM${}_{2.5}$ sensors, demonstrated that our sensor system followed the overall tendencies and changes. We obtained original datasets after conducting flight measurement experiments at three sites with differing surrounding environments. The experimentally obtained prediction results matched regional PM${}_{2.5}$ trends obtained using long short-term memory (LSTM) networks trained using the respective datasets.

This study was conducted to develop robot prototypes of three models that navigate mallards to achieve high-efficiency rice-duck farming. We examined two robotics navigation approaches based on imprinting and feeding. As the first approach, we used imprinting applied to baby mallards. They exhibited follow behavior to our first prototype after imprinting. Experimentally obtained observation results revealed the importance of providing imprinting immediately up to one week after hatching. As another approach, we used feed placed on the top of our second prototype. Experimentally obtained results showed that adult mallards exhibited wariness not only against the robot, but also against the feeder. After relieving wariness with provision of more than one week time to become accustomed, adult mallards ate feed in the box on the robot. However, they ran away immediately at a slight movement. Based on this confirmation, we developed the third prototype as an autonomous mobile robot aimed for mallard navigation in a paddy field. The body width is less than the length between rice stalks. After checking the waterproof capability of a body waterproof box, we conducted an indoor driving test for manual operation. Moreover, we conducted outdoor evaluation tests to assess running on an actual paddy field. We developed indoor and outdoor image datasets using an onboard monocular camera. For the outdoor image datasets, our segmentation method based on SegNet achieved semantic segmentation for three semantic categories. For the indoor image datasets, our prediction method based on CNN and LSTM achieved visual prediction for three motion categories.

Numerous methods and applications have been proposed in human activity recognition (HAR). This paper presents a mini-survey of recent HAR studies and our originally developed benchmark datasets of two types using environmental sensors. For the first dataset, we specifically examine human pose estimation and slight motion recognition related to activities of daily living (ADL). Our proposed method employs OpenPose. It describes feature vectors without effects of objects or scene features, but with a convolutional neural network (CNN) with the VGG-16 backbone, which recognizes behavior patterns after classifying the obtained images into learning and verification subsets. The first dataset comprises time-series panoramic images obtained using a fisheye lens monocular camera with a wide field of view. We attempted to recognize five behavior patterns: eating, reading, operating a smartphone, operating a laptop computer, and sitting. Even when using panoramic images including distortions, results demonstrate the capability of recognizing properties and characteristics of slight motions and pose-based behavioral patterns. The second dataset was obtained using five environmental sensors: a thermopile sensor, a CO${}_2$ sensor, and air pressure, humidity, and temperature sensors. Our proposed sensor system obviates the need for constraint; it also preserves each subject’s privacy. Using a long short-term memory (LSTM) network combined with CNN, which is a deep-learning model dealing with time-series features, we recognized eight behavior patterns: eating, operating a laptop computer, operating a smartphone, playing a game, reading, exiting, taking a nap, and sitting. The recognition accuracy for the second dataset was lower than for the first dataset consisting of images, but we demonstrated recognition of behavior patterns from time-series of weak sensor signals. The recognition results for the first dataset, after accuracy evaluation, can be reused for automatically annotated labels applied to the second dataset. Our proposed method actualizes semi-automatic annotation, false recognized category detection, and sensor calibration. Feasibility study results show the new possibility of HAR used for ADL based on unique sensors of two types.

Classification, segmentation, and recognition techniques based on deep-learning algorithms are used for smart farming. It is an important and challenging task to reduce the time, burden, and cost of annotation procedures for collected datasets from fields and crops that are changing in a wide variety of ways according to growing, weather patterns, and seasons. This study was conducted to generate crop image datasets for semantic segmentation based on an image style transfer using generative adversarial networks (GANs). To assess data-augmented performance and calculation burdens, our proposed framework comprises contrastive unpaired translation (CUT) for a conditional GAN, pix2pixHD for an unconditional GAN, and DeepLabV3+ for semantic segmentation. Using these networks, the proposed framework provides not only image generation for data augmentation, but also automatic labeling based on distinctive feature learning among domains. The Fréchet inception distance (FID) and mean intersection over union (mIoU) were used, respectively, as evaluation metrics for GANs and semantic segmentation. We used a public benchmark dataset and two original benchmark datasets to evaluate our framework of four image-augmentation types compared with the baseline without using GANs. The experimentally obtained results showed the efficacy of using augmented images, which we evaluated using FID and mIoU. The mIoU scores for the public benchmark dataset improved by 0.03 for the training subset, while remaining similar on the test subset. For the first original benchmark dataset, the mIoU scores improved by 0.01 for the test subset, while they dropped by 0.03 for the training subset. Finally, the mIoU scores for the second original benchmark dataset improved by 0.18 for the training subset and 0.03 for the test subset.

Drones equipped with a global navigation satellite system (GNSS) receiver for absolute localization provide high-precision autonomous flight and hovering. However, the GNSS signal reception sensitivity is considerably lower in areas such as those between high-rise buildings, under bridges, and in tunnels. This paper presents a drone localization method based on acoustic information using a microphone array in GNSS-denied areas. Our originally developed microphone array system comprised 32 microphones installed in a cross-shaped configuration. Using drones of two different sizes and weights, we obtained an original acoustic outdoor benchmark dataset at 24 points. The experimentally obtained results revealed that the localization error values were lower for $0^{\circ }$ and $\pm {45}^{\circ }$ than for $\pm {90}^{\circ }$. Moreover, we demonstrated the relative accuracy for acceptable ranges of tolerance for the obtained localization error values.

This paper presents a novel approach for predicting Particulate Matter (PM) concentrations using mobile camera devices. In response to persistent air pollution challenges across Japan, we developed a system that utilizes cutting-edge transformer-based deep learning architectures to estimate PM values from imagery captured by smartphone cameras. Our approach employs Contrastive Language–Image Pre-Training (CLIP) as a multimodal framework to extract visual features associated with PM concentration from environmental scenes. We first developed a baseline through comparative analysis of time-series models for 1D PM signal prediction, finding that linear models, particularly NLinear, outperformed complex transformer architectures for short-term forecasting tasks. Building on these insights, we implemented a CLIP-based system for 2D image analysis that achieved a Top-1 accuracy of 0.24 and a Top-5 accuracy of 0.52 when tested on diverse smartphone-captured images. The performance evaluations on Graphics Processing Unit (GPU) and Single-Board Computer (SBC) platforms highlight a viable path toward edge deployment. Processing times of 0.29 s per image on the GPU versus 2.68 s on the SBC demonstrate the potential for scalable, real-time environmental monitoring. We consider that this research connects high-performance computing with energy-efficient hardware solutions, creating a practical framework for distributed environmental monitoring that reduces reliance on costly centralized monitoring systems. Our findings indicate that transformer-based multimodal models present a promising approach for mobile sensing applications, with opportunities for further improvement through seasonal data expansion and architectural refinements.