BHC-YOLOV8 : improved YOLOv8-based BHC target detection model for tea leaf disease and defect in real-world scenarios.

Introduction: The detection efficiency of tea diseases and defects ensures the quality and yield of tea. However, in actual production, on the one hand, the tea plantation has high mountains and long roads, and the safety of inspection personnel cannot be guaranteed; on the other hand, the inspection personnel have factors such as lack of experience and fatigue, resulting in incomplete and slow testing results. Introducing visual inspection technology can avoid the above problems.

Study on the Tea Pest Classification Model Using a Convolutional and Embedded Iterative Region of Interest Encoding Transformer.

Tea diseases are one of the main causes of tea yield reduction, and the use of computer vision for classification and diagnosis is an effective means of tea disease management. However, the random location of lesions, high symptom similarity, and complex background make the recognition and classification of tea images difficult. Therefore, this paper proposes a tea disease IterationVIT diagnosis model that integrates a convolution and iterative transformer.

Construction of Cobalt Molybdenum Diselenide Three-phase Heterojunctions for Electrocatalytic Hydrogen Evolution in Acid Medium.

Molybdenum diselenide and cobalt diselenide have been commonly implemented in electrocatalytic hydrogen evolution reaction (HER). However, there have been few research on the creation of their three-phase heterojunctions and the associated HER process. Herein, we constructed a three-phase heterostructure sample consisting of orthorhombic CoSe , cubic CoSe and MoSe and we investigated its HER performance.

Spectrum classification of citrus tissues infected by fungi and multispectral image identification of early rotten oranges.

Citrus fruit is susceptible to postharvest rot by fungal infection. The detection of early rot is difficult due to similar skin characteristics to sound area, which limits the ability of the grading system to evaluate the comprehensive quality of citrus. In this study, the visible and near infrared hyperspectral imaging system with the wavelength range of 325-1000 nm was used to collect hyperspectral images of oranges.

Quantitative visualization of photosynthetic pigments in tea leaves based on Raman spectroscopy and calibration model transfer.

Background: Photosynthetic pigments participating in the absorption, transformation and transfer of light energy play a very important role in plant growth. While, the spatial distribution of foliar pigments is an important indicator of environmental stress, such as pests, diseases and heavy metal stress.

Results: In this paper, in situ quantitative visualization of chlorophyll and carotenoid was realized by combining the Raman spectroscopy with calibration model transfer, and a laboratory Raman spectral model was successfully extended to a portable field spectral measurement.

[Hyperspectral technology combined with CARS algorithm to quantitatively determine the SSC in Korla fragrant pear].

Hyperspectral imaging has large data volume and high dimensionality, and original spectra data includes a lot of noises and severe scattering. And, quality of acquired hyperspectral data can be influenced by non-monochromatic light, external stray light and temperature, which resulted in having some non-linear relationship between the acquired hyperspectral data and the predicted quality index. Therefore, the present study proposed that competitive adaptive reweighted sampling (CARS) algorithm is used to select the key variables from visible and near infrared hyperspectral data.