[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. The performance of CARS was compared with full spectra, successive projections algorithm (SPA), Monte Carlo-uninformative variable elimination (MC-UVE), genetic algorithm (GA) and GA-SPA (genetic algorithm-successive projections algorithm). Two hundred Korla fragrant pears were used as research object. SPXY algorithm was used to divided sample set to correction set with 150 samples and prediction set with 50 samples, respectively. Based on variables selected by different methods, linear PLS and nonlinear LS-SVM models were developed, respectively, and the performance of models was assessed using parameters r2, RMSEP and RPD. A comprehensive comparison found that GA, GA-SPA and CARS can effectively select the variables with strong and useful information. These methods can be used for selection of Vis-NIR hyperspectral data variables, particularly for CARS. LS-SVM model can obtain the best results for SSC prediction of Korla fragrant pear based on variables obtained from CARS method. r2, RMSEP and RPD were 0.851 2, 0.291 3 and 2.592 4, respectively. The study showed that CARS is an effectively hyperspectral variable selection method, and nonlinear LS-SVM model is more suitable than linear PLS model for quantitatively determining the quality of fra- grant pear based on hyperspectral information.

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. The convolution consists of a superimposed bottleneck layer for extracting the local features of tea leaves. The iterative algorithm incorporates the attention mechanism and bilinear interpolation operation to obtain disease location information by continuously updating the region of interest in location information. The transformer module uses a multi-head attention mechanism for global feature extraction. A total of 3544 images of red leaf spot, algal leaf spot, bird's eye disease, gray wilt, white spot, anthracnose, brown wilt, and healthy tea leaves collected under natural light were used as samples and input into the IterationVIT model for training. The results show that when the patch size is 16, the model performed better with an IterationVIT classification accuracy of 98% and F1 measure of 96.5%, which is superior to mainstream methods such as VIT, Efficient, Shuffle, Mobile, Vgg, etc. In order to verify the robustness of the model, the original images of the test set were blurred, noise- was added and highlighted, and then the images were input into the IterationVIT model. The classification accuracy still reached over 80%. When 60% of the training set was randomly selected, the classification accuracy of the IterationVIT model test set was 8% higher than that of mainstream models, with the ability to analyze fewer samples. Model generalizability was performed using three sets of plant leaf public datasets, and the experimental results were all able to achieve comparable levels of generalizability to the data in this paper. Finally, this paper visualized and interpreted the model using the CAM method to obtain the pixel-level thermal map of tea diseases, and the results show that the established IterationVIT model can accurately capture the location of diseases, which further verifies the effectiveness of the model.

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 CoSe2 , cubic CoSe2 and MoSe2 and we investigated its HER performance. The sample shows microsphere morphology composed of nanosheets with interfacial interactions between the components. It possesses an overpotential of -136 mV at -10 mA cm-2 in acid medium, which is superior to that of single component and most two-phase heterostructures. Especially, the overpotential at -200 mA cm-2 is smaller than that of Pt/C. The excellent performance can be attributed to the d-orbital upshift of the Co active sites due to charge redistribution between the three-phase heterojunction and the optimization of the hydrogen free energy. This work provides inspiration for exploring the application of other multi-component heterojunctions in electrocatalytic hydrogen evolution.

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. Hyperspectral data of three types of tissues including sound tissue from 80 samples, rotten tissue infected by Penicillium digitatum from 100 samples and rotten tissue infected by Penicillium italicum from 100 samples were extracted. The bootstrapping soft shrinkage (BOSS) and BOSS-SPA (BOSS-Successive Projections Algorithm) combination algorithm were separately used to optimize spectrum variables. The partial least squares discriminant analysis (PLS-DA) model for classifying three types of tissues and PLS-DA model for classifying two types of tissues (sound tissue and rotten tissue) were constructed based on full-spectrum and the selected informative variables. Model comparisonshowed that the BOSS-PLS-DA model can effectively identify three types of tissues with the classification accuracy of 97.1%, while the BOSS-SPA-PLS-DA model was more effective for the binary classification of sound and rotten citrus tissues with the accuracy of 100%. Furthermore, the wavelength images corresponding to the nine informative variables extracted by BOSS-SPA were performed the principal component analysis (PCA), and four feature wavelength images (508, 568, 578 and 614 nm) were obtained by analyzing the weighting coefficients of each single-wavelength images constituting the optimal principal component (PC) image. Finally, a fast multispectral image processing algorithm combined with the global threshold theory was proposed for the rotten orange detection based on the extracted four wavelength images. A total of 280 samples including 80 sound and 200 rotten samples were used to evaluate the classification ability, which showed the proposed multispectral image detection algorithm can successfully differentiate between sound and rotten oranges with an overall classification accuracy of 98.6%.

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. Firstly, a nondestructive and fast model for determination of chlorophyll and carotenoid in tea leaf was established based on confocal micro-Raman spectrometer in the laboratory. Then the spectral model was extended to a real-time foliar map scanning spectra of a field portable Raman spectrometer through calibration model transfer, and the spectral variation between the confocal micro-Raman spectrometer in the laboratory and the portable Raman spectrometer were effectively corrected by the direct standardization (DS) algorithm. The portable map scanning Raman spectra of the tea leaves after the model transfer were got into the established quantitative determination model to predict the concentration of photosynthetic pigments at each pixel of the tea leaves. The predicted photosynthetic pigments concentration of each pixel was imaged to illustrate the distribution map of foliar pigments. Statistical analysis showed that the predicted pigment contents were highly correlated with the real contents. CONCLUSIONS: It can be concluded that the Raman spectroscopy was applicable for in situ, non-destructive and rapid quantitative detecting and imaging of photosynthetic pigment concentration in tea leaves, and the spectral detection model established based on the laboratory Raman spectrometer can be applied to a portable field spectrometer for quantitatively imaging of the foliar pigments.