Qing Yao

The widespread adoption of sensor devices and Internet of Things (IoT) technologies has generated vast amounts of time series data, making time series data mining a key research focus for informed decision-making in IoT. This article focuses on time series clustering, a key task that has driven significant research and algorithm development. Despite progress, current methods still offer room for improvement in accuracy and efficiency. To this end, this article presents RACER, a time series clustering method that achieves high accuracy and efficiency by bridging the gap between random convolutional kernels and ensemble clustering. Specifically, RACER utilizes random convolutional kernels to extract features from raw data and obtains six base clustering results by combining various dimensionality reduction (DR) techniques with clustering methods tailored for the extracted features. The final clustering results are derived through ensemble clustering using nonnegative matrix factorization (NMF). We evaluate RACER on the UCR Archive using the adjusted rand index (ARI). Experimental results reveal that: 1) RACER surpasses recently proposed state-of-the-art algorithms, including R-Clustering and RandomNet, as well as classical methods such as $k$ -Shape and $k$ -means with DTW and 2) RACER completes clustering on 128 UCR datasets (excluding 11 datasets with unequal lengths) in just 18 min, significantly outperforming the vast majority of existing approaches in terms of efficiency. The experimental results and the code for RACER are available at the GitHub link: https://github.com/pcjdhhhh/RACER

The whitefly (Bemisia tabaci) is a globally distributed agricultural pest. While accurate monitoring of this species is crucial for early warning systems and efficient pest control, traditional manual monitoring methods suffer from subjectivity, low accuracy with large populations, and arduous data traceability. To surmount these challenges, this paper proposes an automatic counting method for whitefly adults and late-instar nymphs, based on whitefly images acquired using augmented reality (AR) glasses and a segmentation-then-detection approach. Acquired by the surveyors wearing AR glasses, the images of whiteflies on the undersides of crop leaves are transmitted to a server via Wi-Fi/5G. The system enables the automatic whitefly counting model to enumerate the adult and late-instar nymph populations, and the results can be viewed on both the AR glasses and mobile devices. The study utilizes Mask2Former-Leaf to segment the foreground primary leaf in pursuit of the minimal influence of non-primary leaf areas and background noise in the images, and detects tiny whitefly adults and late-instar nymphs in high-resolution images by involving the YOLOv11-Whitefly detection model. This model integrates Slicing Aided Hyper Inference (SAHI) capability, and can enormously amplify the feature representation of tiny objects by slicing large images through overlapping windows for both training and inference. Furthermore, DyCM-C3K2 is introduced into the YOLOv11 backbone network, which enhances the detection capability for small whitefly targets by dynamically generating input-dependent convolutional kernels, and injecting global contextual information into local convolution operations. Also, a Multi-Branch Channel Re-Weighted Feature Pyramid Network (MCRFPN) is designed to replace the original neck network, optimizing the fusion between superficial and deep features. In contrast to mainstream detection models such as YOLO, RTMDet, Cascade-CNN, DETR, and DINO, YOLOv11-Whitefly demonstrates superior performance—attaining an average recall rate of 86.20%, an average precision of 84.25%, and an mAP₅₀ of 91.60% for whitefly adults and late-instar nymphs. With the purpose of visualizing the whitefly infestation data, this paper developed an intelligent whitefly survey system that provides on-site visualization of whitefly images integrated with their adult and late-instar nymph counting results. This facilitates surveyors in understanding pest populations and formulating scientific control decisions.

Summary: With the expansion of the global wood trade, customs inspectors increasingly face the challenge of verifying wood species on site. To equip inspectors with a rapid and accurate identification tool, an intelligent wood identification system, MultiWoodID, was developed to capture wood cross-section images and determine wood species. The system comprises a wood polisher, a portable image acquisition device, a wood identification model, a mobile application, and a web platform. The wood polisher is designed to reveal both the macroscopic and microscopic structure of wood. A macro lens attached to a smartphone magnifies the wood cross-section image to expose finer details. Owing to the self-similarity inherent in wood cross-section images, a slicing strategy was applied to partition the original image into multiple sub-images. To address the issue of a clear central region accompanied by blurred surroundings caused by the macro lens, the softmax layer was modified by integrating weighted contributions from 25 sub-images during the identification task. To accurately identify 145 wood species and reduce errors associated with varying image resolutions, a TEFPN architecture based on ResNeSt was introduced. This modification enhances the extraction of both macroscopic and local features from wood cross-section images, thereby facilitating the discrimination of similar wood species. The model achieved a precision of 99.12%, a recall of 98.58%, and an score of 98.84%. The complete identification process, from wood polishing to result display, can now be accomplished in approximately 10 minutes, representing a significant reduction in time compared to conventional wood identification methods.

The proliferation of IoT devices generates vast amounts of sensor data, making sensor data mining an increasingly important research topic in the IoT domain. The Top-k similarity search plays a crucial role in sensor data mining, forming the foundation for advanced techniques such as sensor data classification, clustering, and anomaly detection. The need for efficient algorithms to enhance search speed drives our research. This paper introduces an efficient method named PIPDTW for Top-k similarity search of sensor data using Dynamic Time Warping (DTW) distance, a prevalent but computationally intensive similarity measure in time-series data mining. To address the high computational complexity, PIPDTW first employs perceptually important points (PIPs) to transform lengthy sensor data into concise vector representations. We then introduce a novel distance measure, DTWIV, based on PIP representations to quickly estimate DTW distances between the query q and all sensor data. Finally, during the online search, we apply two levels of filtering based on approximate ranking and the lower-bound technique to efficiently prune dissimilar data, reducing the need for exact DTW distance computations and thereby achieving efficient querying. Experiments conducted on synthetic and real-world publicly available datasets, compared with relevant competitors, demonstrate that PIPDTW slightly sacrifices retrieval accuracy but significantly reduces runtime.

Crop plant height is a critical parameter for assessing crop physiological properties, such as above-ground biomass and grain yield and crop health. Current dominant plant height estimation methods are based on digital surface model (DSM) and vegetation indexes (VIs). However, DSM-based methods usually estimate plant height by growth stages, which would result in some discontinuity between growth stages due to different fitting curves. Additionally, there has been limited research on the application of VI-based plant height estimation for multiple crop species. Thus, this study investigated the validity and challenges associated with these methods for estimating canopy heights of multi-variety rice throughout the entire growing season. A total of 474 rice varieties were cultivated in a single season, and RGB images including red, green, and blue bands, DSMs, multispectral images including near infrared and red edge bands, and manually measured plant heights were collected in 2022. DSMs and 26 commonly used VIs were employed to estimate rice canopy heights during the growing season. The plant height estimation using DSMs was performed using different quantiles (50th, 75th, and 95th), while two-stage linear regression (TLR) models based on each VI were developed. The DSM-based method at the 95th quantile showed high accuracy, with an R² value of 0.94 and an RMSE value of 0.06 m. However, the plant height estimation at the early growth stage showed lower effectiveness, with an R² < 0. For the VIs, height estimation with MTCI yielded the best results, with an R² of 0.704. The first stage of the TLR model (maximum R² = 0.664) was significantly better than the second stage (maximum R² = 0.133), which indicated that the VIs were more suitable for estimating canopy height at the early growth stage. By grouping the 474 varieties into 15 clusters, the R² values of the VI-based TLR models exhibited inconsistencies across clusters (maximum R² = 0.984; minimum R² = 0.042), which meant that the VIs were suitable for estimating canopy height in the cultivation of similar or specific rice varieties. However, the DSM-based method showed little difference in performance among the varieties, which meant that the DSM-based method was suitable for multi-variety rice breeding. But for specific clusters, the VI-based methods were better than the DSM-based methods for plant height estimation. In conclusion, the DSM-based method at the 95th quantile was suitable for plant height estimation in the multi-variety rice breeding process, and we recommend using DSMs for plant height estimation after 26 DAT. Furthermore, the MTCI-based TLR model was suitable for plant height estimation in monoculture planting or as a correction for DSM-based plant height estimation in the pre-growth period of rice.

Spodoptera frugiperda (fall armyworm, FAW) is a global agriculture pest. Adults have a strong migratory ability and larvae feed on the host stalks, which pose a serious threat for maize and other crops. Identification and counting of different instar larvae in the fields is important for effective pest management and forecasting emergence and migration time of adults. Usually, the technicians identify the larval instars according to the larva morphological features with the naked eye or stereoscope in the lab. The manual identification method is complex, professional and inefficient. In order to intelligently, quickly and accurately identify the larval instar, we design a portable image acquisition device using a mobile phone with a macro lens and collect 1st-6th instar larval images. The YOLOv4 detection method and improved MRES-UNet++ segmentation methods are used to locate the larvae and segment the background. The larval length and head capsule width are automatically measured by some graphics algorithms, and the larval image features are extracted by SIFT descriptors. The random forest model improved by Boruta feature selection and grid search method is used to identify the larval instars of FAWs. The test results show that high-definition images can be easily collected by using the portable device (Shenzhen, China). The MRES-UNet++ segmentation method can accurately segment the larvae from the background. The average measurement error of the head capsule width and body length of moth larvae is less than 5%, and the overall identification accuracy of 1st–6th instar larvae reached 92.22%. Our method provides a convenient, intelligent and accurate tool for technicians to identify the larval instars of FAWs.

The aim of the study was to investigate the body distribution of nanoparticle-containing adriamycin (NADR) injected into the hepatic artery of hepatoma-bearing rats. Thirty Walker-256...