Hyperspectral images, with their many spectral bands, provide a rich source of material information about an object that can be effectively used for object tracking. However, many trackers in this domain rely on detection-based techniques, which often perform suboptimally in challenging scenarios such as managing occlusions and distinguishing objects in cluttered backgrounds. This underperformance is primarily due to the presence of multiple spectral bands and the inability to leverage this abundance of data for effective tracking. Additionally, the scarcity of annotated hyperspectral videos and the absence of comprehensive temporal information exacerbate these difficulties, further limiting the effectiveness of current tracking methods. To address these challenges, this paper introduces the novel Hy-Tracker framework, designed to bridge the gap between hyperspectral data and state-of-the-art object detection methods. Our approach leverages the strengths of YOLOv7 for object tracking in hyperspectral videos, enhancing both accuracy and robustness in complex scenarios. The Hy-Tracker framework comprises two key components. We introduce a hierarchical attention band selection (HAS-BS) that selectively processes and groups the most informative spectral bands, thereby significantly improving detection accuracy. Additionally, we have developed a refined tracker that refines the initial detections by incorporating a classifier and a temporal network using gated recurrent units (GRU). The classifier distinguishes similar objects, while the temporal network models temporal dependencies across frames for robust performance despite occlusions and scale variations. Experimental results on hyperspectral benchmark datasets demonstrate the effectiveness of Hy-Tracker in accurately tracking objects across frames and overcoming the challenges inherent in detection-based hyperspectral object tracking.

Hyperspectral images contain many bands that can be used to obtain object material information for object tracking and remote sensing. Nevertheless, neighboring bands of hyperspectral images are often highly correlated, and a large number of bands increase the complexity of model learning. This issue is worsened by the shortage of labeled hyperspectral videos for fine-tuning pre-trained deep neural networks. To tackle these challenges, this paper introduces a novel background-aware band selection method to model spatial changes of an object and its corresponding local region, which is capable of selecting discriminative bands for object representation while reducing computational complexity. Specifically, the object and local region of each band is compared with other bands to obtain their dissimilarity scores. Guided by these scores, the top three bands are selected and form a three-channel image. This image is then fed into an object tracker. Experimental results demonstrate the efficiency and effectiveness of the proposed method on a benchmark hyperspectral object tracking dataset.

Dragon fruit (Hylocereus spp.), renowned for its aesthetic appeal and rich antioxidant content, has gained global popularity due to its numerous health benefits. In Australia, despite growing commercial interest in cultivating dragon fruit, there is uncertainty for local growers stemming from competition with imported varieties. Notably, there is a lack of comparative research on the shelf-life, antioxidant activity, and phytochemical contents of Australian-grown versus imported dragon fruit, which is crucial for enhancing market competitiveness and consumer perception. This study compares the shelf-life, antioxidant activity, and phytochemical content of Australian-grown and imported dragon fruits under ambient conditions, addressing the competitive challenges faced by local growers. Freshly harvested white-flesh (Hylocereus undatus) and red-flesh (H. polyrhizus) dragon fruit were sourced from Queensland and the Northern Territory and imported fruit were sourced from an importer in Queensland. All fruit were assessed for key quality parameters including peel color, firmness, weight loss, total soluble solids (TSS), pH, titratable acidity (TA), total phenolic content (TPC), total flavonoid content (TFC), ferric reducing antioxidant power (FRAP), cupric reducing antioxidant capacity (CUPRAC), total betalain content (TBC), and total anthocyanin content (TAC). The results indicate that Australian-grown white dragon fruits exhibited average one day longer shelf-life with less color degradation, better firmness retention, and less decline in weight loss, TSS, and acidity compared to imported fruits. Australian-grown red dragon fruits showed similar shelf-life compared to fruits from overseas. Antioxidant activities and phytochemicals were consistently higher in Australian-grown fruits throughout their shelf-life. These findings indicate that Australian-grown dragon fruits offer better physical quality and retain more nutritional value, which could enhance their marketability.

Hyperspectral object tracking is notably challenging due to the high-dimensional nature of the data and the necessity of seamlessly integrating spectral, spatial and temporal information. Traditional methods often emphasize detection-based or tracking-based networks, each leveraging their inherent strengths but overlooking the potential advantages of a combined approach, leading to suboptimal performance in complex, real-world scenarios. Furthermore, this challenge is amplified by the variability of spectral bands across datasets, making the maintenance of consistent tracking performance complicated. To address these issues, we propose a novel, unified approach that merges adaptive band selection with a multi-model ensemble strategy. We introduce a local and global attention-based unified band selection (UBS) technique that identifies the most informative three bands from any dataset, significantly reducing data complexity while preserving critical spectral and spatial information. This UBS method employs spectral independence, allowing it to process hyperspectral video frames with any number of bands as input, ultimately generating a three-band pseudocolor image. This is coupled with a multi-model ensemble framework, utilizing a local and global attention-based appearance module. The module selects the optimal candidate by computing the similarity between the proposals generated by the base models and historical frames. Experimental results show that our approach, UBSTrack, achieves state-of-the-art performance, delivering robust and accurate tracking under different real-world challenging scenarios. The code of UBSTrack is available at the following link: source code.

The seed is an inevitable element for agricultural and industrial production. The non-destructive paddy seed variety identification is essential to assure paddy purity and quality. This research is aimed at developing a computer vision-based system to identify paddy varieties using multiple heterogeneous features, exploiting textural, external, and physical properties. We captured the paddy seed images without any fixed setup to make the system user friendly at both industry and farmer levels, which can lead to illumination problems in the images. To overcome this problem, we introduced a modified histogram oriented gradient (T20-HOG) feature that can describe the illumination, scale, and rotational variations of a paddy image. We also utilized the existing Haralick and traditional features and the dimensionality of the features is reduced by the Lasso feature selection technique. The selected features are used to train the feed-forward neural network (FNN) to predict the paddy variety. The experiments conducted on two different datasets: BDRICE, and VNRICE. Results of our method are shown in terms of four standard evaluation metrics, namely, accuracy, precision, recall, and F_1 score, and achieved 99.28%, 98.64%, 98.48%, and 98.56% score, respectively. We also compared our system efficiency with existing studies. The experimental results demonstrate that our proposed features are effective to identify paddy variety and achieved a new state-of-the-art performance. And we also observed that our newly proposed T20-HOG features have a major impact on overall system performance.