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In the Phalaenopsis industry, the growth vigor of seedlings when reaching their minimum growth time of vegetative cultivation plays an important role in the subsequent production chain and the final economic profits. The current manual assessment taking place in the commercial large-scale greenhouse is time-consuming and labor-intensive. Related studies based on RGB image for plant growth assessment relied on extracting hand-crafted features from images, affecting the effectiveness and generalization ability of machine learning models. In this study, the Convolutional Neural Network (CNN) was employed to explore its feasibility in assessing the growth vigor of Phalaenopsis aphrodite seedlings grown in the greenhouse in an end-to-end manner. Seedling images were collected in the greenhouse conditions with complex image background. Baseline models on the greenhouse dataset were established using different CNN architectures (VGG, ResNet, Inception-v3) coupled with various training mechanisms (training from scratch, fine-tuning, feature extraction), in which fine-tuning achieved the best classification results. Considering the target task of morphological classification for individual greenhouse seedlings with complex image background, to further boost model performance, additional seedlings images were acquired in controlled laboratory conditions. The segmented laboratory images were used to assist in model learning, namely building the augmented models. Two approaches were adopted, achieving an overall improvement in the testing F1-score of 0.03-0.05 compared with baseline models. The VGG model with augmentation method II achieved the highest performance in this study (F1-score of 0.997 on the test set), its feature maps were also visualized. In higher-level feature maps, regions of the target seedling were activated while filtering out most background including leaves from adjacent seedlings, proving the effective morphology characterizing using CNN for greenhouse seedlings. The overall results demonstrated the potential of deep learning models for image-based assessment of growth vigor for Phalaenopsis aphrodite seedlings and maybe other kinds of plants in greenhouse conditions. [ABSTRACT FROM AUTHOR]

Salmon has always been regarded as a popular gourmet fish that is consumed in large quantities due to its high nutritional value. This study proposes a new rapid and non-destructive method using visible and near infrared (Vis/NIR) hyperspectral imaging for the detection of freshness, storage time, and frozen-thawed times of fillets for turbot flesh. Hyperspectral imaging technology is a rapid, non-destructive, and non-contact technique that integrates spectroscopy and digital imaging to simultaneously obtain spectral and spatial information. Hyperspectral images are made up of hundreds of contiguous wavebands for each spatial position of a sample studied, and each pixel in an image contains the spectrum for that specific position. With hyperspectral imaging, a spectrum for each pixel can be obtained and a gray scale image for each narrow band can be acquired, thereby enabling this system to reflect componential and constructional characteristics, as well as the spatial distributions, of an object. In this study, a hyperspectral imaging system (380-1 023 nm) was developed to perform classification of freshness, storage time, and frozen-thawed times of fish fillets based on a gray level co-occurrence matrix (GLCM) and least squares support vector machines (LS-SVM). Altogether, 160 fish samples from two different storage days and two different frozen-thawed times were collected for hyperspectral image scanning, and mean spectra were extracted from the region of interest (ROI) inside each image. LS-SVM was applied as a calibration method to correlate the spectral and GLCM data for 110 samples in the calibration set. The LS-SVM model was then used to predict the freshness, storage time, and frozen-thawed times for the 50 prediction samples. Spectra of fish samples were extracted from the region of interest (ROI) and a competitive adaptive reweighted sampling (CARS) algorithm was used to select the key variables. Hyperspectral imaging data and principal component analysis (PCA) were performed with the goal of selecting the first principal component (PC) image that could potentially be used for the classification system. Then, 12 texture features (i.e., mean, standard deviation, smoothness, third moment, uniformity, and entropy) based on the statistical moment were extracted from the PC1 image. Finally, 12 gray level co-occurrence matrix (GLCM) variables, combined with 57 characteristic wavelengths for each fish sample, were extracted as the LS-SVM input. Experimental results show that the discriminating rate is 98% in the prediction set. The results indicate that hyperspectral imaging technology combined with chemometrics and image processing allows the classification of freshness, storage time and frozen-thawed times for fish fillets, which builds a foundation for the automatic processing of aquatic products. The fish industry can benefit from adopting hyperspectral imaging technology. [ABSTRACT FROM AUTHOR]

The rapid and non-destructive quantification of catechins in fermented black tea is crucial for evaluating the quality of black tea. The combination of hyperspectral imaging and chemometrics has been applied for quantitative detection, but its performance is usually constrained by the limited dataset size. Targeted at the challenge of insufficient samples in regression analysis of catechins, this study proposes an improved deep convolutional generative adversarial network with labeling module, named as DCGAN-L for hyperspectral data augmentation. The DCGAN-L consists of the spectral and label generating modules. First the synthetic spectra were generated, and an indicator was proposed to evaluate their quality. Then, the corresponding label values were generated, including epicatechin gallate (ECG), epicatechin (EGC), catechin (C), and total catechin (CC). For label generating, the Euclidean distances between the synthetic spectrum and all true spectra were measured, followed by allocating weights for calculating the label values based on these distances. Subsequently, the training dataset was augmented with the generated synthetic data. The effect of data augmentation was finally evaluated based on two regression models of random forest (RF) and broad learning system (BLS) for the quantification of catechins. Compared with the results before data augmentation, the average R 2 of RF and BLS models increased by 0.044 and 0.164, respectively. The proposed DCGAN-L model allows for the rapid, non-destructive quantification of catechins in black tea in the case of limited sample size., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)