For the stable supply of oil and gas resources, industry is pushing for various attempts and technology development to produce not only existing land fields but also deep-sea, where production is difficult. The development of flow assurance technology is necessary because hydrate is aggregated in the pipeline and prevent stable production. This study established a system that enables hydrate diagnosis in the gas pipeline from a flow assurance perspective. Learning data were generated using an OLGA simulator, and temperature, pressure, and hydrate volume at each time step were generated. Stacked auto-encoder (SAE) was used as the AI model after analyzing training loss. Hyper-parameter matching and structure optimization were carried out using the greedy layer-wise technique. Through time-series forecast, we determined that AI diagnostic model enables depiction of the growth of hydrate volume. In addition, the average R-square for the maximum hydrate volume was 97%, and that for the formation location was calculated as 99%. This study confirmed that machine learning could be applied to the flow assurance area of gas pipelines and it can predict hydrate formation in real time.

This paper proposes a system for the forecasting and automated inspection of rice Bakanae disease (RBD) infection rates via drone imagery. The proposed system synthesizes camera calibrations and area calculations in the optimal data domain to detect infected bunches and classify infected rice culm numbers. Optimal heights and angles for identification were examined via linear discriminant analysis and gradient magnitude by targeting the morphological features of RBD in drone imagery. Camera calibration and area calculation enabled distortion correction and simultaneous calculation of image area using a perspective transform matrix. For infection detection, a two-step configuration was used to recognize the infected culms through deep learning classifiers. The YOLOv3 and RestNETV2 101 models were used for detection of infected bunches and classification of the infected culm numbers, respectively. Accordingly, 3 m drone height and 0° angle to the ground were found to be optimal, yielding an infected bunches detection rate with a mean average precision of 90.49. The classification of number of infected culms in the infected bunch matched with an 80.36% accuracy. The RBD detection system that we propose can be used to minimize confusion and inefficiency during rice field inspection.

Hyperspectral imaging (HSI) is a powerful tool for precision imaging tasks such as vegetation analysis, but its widespread use remains limited due to the high cost of equipment and challenges in data acquisition. To explore a more accessible alternative, we propose a Green Normalized Difference Vegetation Index (GNDVI)-guided green channel adjustment method, termed G-RGB, which enables the estimation of near-infrared (NIR) reflectance from standard RGB image inputs. The G-RGB method enhances the green channel to encode NIR-like information, generating a spectrally enriched representation. Building on this, we introduce FUSE-Net, a novel deep learning model that combines multi-scale convolutional layers and MLP-Mixer-based channel learning to effectively model spatial and spectral dependencies. For evaluation, we constructed a high-resolution RGB-HSI paired dataset by capturing basil leaves under controlled conditions. Through ablation studies and band combination analysis, we assessed the model’s ability to recover spectral information. The experimental results showed that the G-RGB input consistently outperformed unmodified RGB across multiple metrics, including mean squared error (MSE), peak signal-to-noise ratio (PSNR), spectral correlation coefficient (SCC), and structural similarity (SSIM), with the best performance observed when paired with FUSE-Net. While our method does not replace true NIR data, it offers a viable approximation during inference when only RGB images are available, supporting cost-effective analysis in scenarios where HSI systems are inaccessible.

Generating early and reliable fire/smoke alarms from real-world video surveillance remains challenging because visually ambiguous patterns such as sunlight, reflections, clouds, mist, steam, and illumination changes often trigger unstable frame-level false alarms. This paper presents the Spatio-Temporal Verification Network for Fire and Smoke Alarm Validation (STV-FSANet), a detect–track–verify framework that first localizes candidate fire/smoke regions, associates them into temporal tracks, and then verifies the observed sequence of each active track online as fire, smoke, or false fire/smoke. The verifier combines a primary appearance stream from cropped candidate regions with lightweight geometric cues derived from bounding-box position, scale, motion, and short-term fluctuation. A dual-branch GRU models long-term track history, while recent temporal pooling emphasizes newly observed evidence for streaming decisions. To support temporal learning, we construct the Fire–Smoke Alarm Verification (FSAV) Tracklet Dataset from 1347 source videos, yielding 58,733 parent tracks and 2.06 million annotated track frames. The best matched-context STV-FSANet achieves 97.09% test accuracy and 95.81% macro-F1, and both shorter-context models are within 0.5 percentage points of their final prefix metrics by 1.0 s. The results indicate that compact temporal evidence is more useful than simply accumulating longer 96-frame histories. The proposed model also rejects false fire/smoke tracklets with 90.2% recall, demonstrating the value of explicit hard-negative modeling, while the decoupled design allows the verifier to be reused with future detector backbones. TensorRT FP16 deployment reaches 109.55 frames/s on an RTX 4060 Laptop GPU and 39.51 frames/s on a Jetson AGX Orin DevKit.

Strawberry ripeness estimation is essential for determining optimal harvest timing and supporting quality management in smart greenhouse environments. However, since strawberry ripening is a continuous process rather than a transition between discrete visual boundaries, standard RGB-based methods often struggle to distinguish subtle color transitions during intermediate ripeness levels, and misclassifications among adjacent ripeness stages can substantially affect harvest decisions. This makes it important to not only achieve high classification accuracy but also to minimize the ordinal deviation between predicted and actual ripeness stages. To address these limitations, this study presents a two-stage vision-based framework for five-class strawberry ripeness grading. In the first stage, a YOLO11m-based detector localizes strawberry regions within greenhouse images and generates regions of interest (ROIs). In the second stage, each extracted ROI is classified using a five-channel input representation that combines RGB, Excess Green (ExG), and Hue information to enhance color-sensitive feature extraction. In addition, a specialized backbone, termed CP2-A2C2F, is designed to preserve high-resolution P2 features and integrate them with deeper P4 semantic representations through a dual-scale fusion strategy. This structure enables the model to capture localized chromatic variations and contextual maturity information simultaneously. Experimental results show that the CP2-A2C2F model achieved the best overall classification accuracy of 87.7% and a weighted F1 score of 0.877, together with the lowest mean absolute error (MAE) of 0.162 among models equipped with an auxiliary regression head, indicating the smallest average ordinal deviation between predicted and ground-truth ripeness stages. The ablation study further confirmed that adding ExG and Hue channels moderately reduced ordinal prediction error compared with the RGB-only configuration, and that the proposed P2 + P4 fusion achieved the best accuracy, weighted F1 score, and MAE among the evaluated fusion configurations, indicating that direct fusion of high-resolution P2 features with deeper P4 semantic features provides a more effective balance than including the intermediate P3 level. These results indicate that the combination of explicit color representation and dual-scale P2 + P4 feature fusion can improve five-class strawberry ripeness grading and help reduce ordinal prediction errors under the tested greenhouse conditions.