The detection and identification of tea leaf diseases and pests play a crucial role in determining the yield and quality of tea. However, the high similarity between different tea leaf diseases and the difficulty of balancing model accuracy and complexity pose significant challenges during the detection process. This study proposes an enhanced Tea Leaf Disease Detection Model (TLDDM), an improved model based on YOLOv8 to tackle the challenges. Initially, the C2f-Faster-EMA module is employed to reduce the number of parameters and model complexity while enhancing image feature extraction capabilities. Furthermore, the Deformable Attention mechanism is integrated to improve the model’s adaptability to spatial transformations and irregular data structures. Moreover, the Slimneck structure is incorporated to reduce the model scale. Finally, a novel detection head structure, termed EfficientPHead, is proposed to maintain detection performance while improving computational efficiency and reducing parameters which leads to inference speed acceleration. Experimental results demonstrate that the TLDDM model achieves an AP of 98.0%, which demonstrates a significant performance enhancement compared to the SSD and Faster R-CNN algorithm. Furthermore, the proposed model is not only of great significance in improving the performance in accuracy, but also can provide remarkable advantages in real-time detection applications with an FPS (frames per second) of 98.2.

We present a visible-infrared imaging study of young planetary nebula (PN) Hubble 12 (Hb 12; PN G111.8-02.8) obtained with Hubble Space Telescope (HST) archival data and our own Canada-France-Hawaii Telescope (CFHT) measurements. Deep HST and CFHT observations of this nebula reveal three pairs of bipolar structures and an arc-shaped filament near the western waist of Hb 12. The existence of nested bipolar lobes together with the presence of H₂ knots suggests that these structures originated from several mass-ejection events during the pre-PN phase. To understand the intrinsic structures of Hb 12, a three-dimensional model enabling the visualisation of this PN at various orientations was constructed. The modelling results show that Hb 12 may resemble other nested hourglass nebulae, such as Hen 2-320 and M 2-9, suggesting that this type of PN may be common and the morphologies of PNs are not so diverse as is shown by their visual appearances. The infrared spectra show that this PN has a mixed chemistry. We discuss the possible material that may cause the unidentified infrared emissions. The analyses of the infrared spectra and the spectral energy distribution suggest the existence of a cool companion in the nucleus of this object.

The detection and identification of tea leaf diseases and pests play a crucial role in determining the yield and quality of tea. However, the high similarity between different tea leaf diseases and the difficulty of balancing model accuracy and complexity pose significant challenges during the detection process. This study proposes an enhanced Tea Leaf Disease Detection Model (TLDDM), an improved model based on YOLOv8 to tackle the challenges. Initially, the C2f-Faster-EMA module is employed to reduce the number of parameters and model complexity while enhancing image feature extraction capabilities. Furthermore, the Deformable Attention mechanism is integrated to improve the model's adaptability to spatial transformations and irregular data structures. Moreover, the slim neck structure is incorporated to reduce the model scale. Finally, a novel detection head structure, termed EfficientPHead, is proposed to maintain detection performance while improving computational efficiency and reducing parameters which leads to inference speed acceleration. Experimental results demonstrate that the TLDDM model achieves an AP of 98.0%, which demonstrates a significant performance enhancement compared to the SSD and Faster R-CNN algorithm. Furthermore, the proposed model is not only of great significance in improving the performance in accuracy, but also can provide remarkable advantages in real-time detection applications with an FPS (frames per second) of 58.0.

The National Space Science and Technology Centre (NSSTC) in the United Arab Emirates (UAE) is leading the development of the Arab Satellite 813, an Earth observation (EO) platform equipped with hyperspectral imaging (HSI) capabilities. Funded by the UAE Space Agency (UAESA), this satellite aims to provide comprehensive data for environmental assessment and sustainable resource management. Arab Satellite 813 is designed to collect high spatial and spectral resolution data across the blue to Short-Wave- Infrared (SWIR-1) regions of the electromagnetic spectrum. Its primary payload, the HSI, supports applications such as inland water and coastal monitoring, soil characterization, land use mapping, agriculture and vegetation health assessment, and marine pollution monitoring. The satellite also includes a panchromatic (PAN) payload, providing high spatial resolution for detailed observations in specific regions. Additionally, the Atmospheric Polarimeter (AP) payload facilitates polarization data collection, aiding in understanding atmospheric dynamics and correcting HSI data, improving accuracy. The development of Arab Satellite 813 marks a significant advancement in Earth observation, particularly in the Middle East. Through cooperation agreements, data from the satellite can be accessed for research, positioning NSSTC as a key player in hyperspectral imaging. With excellent spectral resolution and high Signal to Noise Ratio (SNR), Arab Satellite 813 will contribute valuable insights into environmental processes and support sustainable resource management and environmental assessment.

To address the technical bottlenecks of strong background radiation interference and weak target signals in mid-wave infrared (MWIR) hyperspectral mineral detection over complex terrain, this paper proposes a “full-link background radiation suppression” methodological framework. A coupled illumination-terrain-atmosphere-sensor radiative transfer model is constructed to systematically quantify how multidimensional parameters—such as observation geometry, surface temperature, elevation, aerosol optical depth, and water vapor content—influence the target background radiation contrast. The findings reveal that daytime observation, lower surface temperature, higher altitude, dry atmosphere, and moderate solar and observation zenith angles are key factors for maximizing the signal-to-noise ratio. Comprehensive optimization analysis demonstrates that observations during midday in autumn and winter achieve optimal performance, with the target background relative contrast potentially enhanced by up to 6.29 times compared to unfavorable conditions such as summer nights. This work elucidates the physical mechanisms governing MWIR hyperspectral detection efficacy in complex scenarios, provides direct parameter-optimization strategies for intelligent mission planning of spaceborne imaging systems, and holds significant value for advancing mineral remote sensing from “passive acquisition” to “cognitive detection”.