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Surveillance of fish disease is essential to contain the spreading epidemics. Current surveillance mostly relies on molecular biology testing methods, which often requires complex procedures and trained operators. These techniques may hardly to be used in many small aquaculture farms. In this paper, a method based on machine vision combined with a target detection network is proposed. A convolutional neural network (CNN) was developed to detect fish infected with SVCV by analyzing the images. The datasets for the CNN that is implemented from a YOLO v7 deep learning algorithm are information extracted from images containing fish populations. An Auto-MSRCR algorithm was used to adaptively enhance the images to minimize manual intervention. We introduced a novel NAM Attention mechanism integrated with the ELAN module in the original YOLO v7 deep learning algorithm. Both channel attention and spatial attention modules were utilized to suppress the insignificant features in the datasets to achieve a more precise and efficient detection method. Also, a loss function MPDIoU was incorporated to avoid missing detection by this vision-based methodology. After training of the model, the detection testing results show that the NAM-YOLO v7 network achieves over 95% prediction accuracy and 93.8% recall, which is superior than other state-of-art YOLO series models. Also, the time for detection for each image only takes 0.18 s illustrating the integrated modules improved the computation efficiency. This novel technique is of great potential to be applied in small farms for rapid and early detection. It can be a vital supplementary tool in developing more effective surveillance strategy by combined with other established methods. • Deep learning algorithm is used in detecting of fish with SVC symptom • Image enhancement is adopted for the analysis of fish populations • MPDIoU is used to enhance the localization of the fish in the images • The detection performance is improved by using NAM attention mechanism combined with YOLOv7 [ABSTRACT FROM AUTHOR]

[Display omitted] • A Raman spectral classification model was created by combining a deep metric learning network with Gramian angular field images. • The trained model covers spectral classification tasks for up to 260 different classes of chemicals. • The proposed method has great potential to be applied to the fast modelling process of substance identification. Rapid identification of unknown material samples using portable or handheld Raman spectroscopy detection equipment is becoming a common analytical tool. However, the design and implementation of a set of Raman spectroscopy-based devices for substance identification must include spectral sampling of standard reference substance samples, resolution matching between different devices, and the training process of the corresponding classification models. The process of selecting a suitable classification model is frequently time-consuming, and when the number of classes of substances to be recognised increases dramatically, recognition accuracy decreases dramatically. In this paper, we propose a fast classification method for Raman spectra based on deep metric learning networks combined with the Gramian angular difference field (GADF) image generation approach. First, we uniformly convert Raman spectra acquired at different resolutions into GADF images of the same resolution, addressing spectral dimension disparities induced by resolution differences in different Raman spectroscopy detection devices. Second, a network capable of implementing nonlinear distance measurements between GADF images of different classes of substances is designed based on a deep metric learning approach. The Raman spectra of 450 different mineral classes obtained from the RRUFF database were converted into GADF images and used to train this deep metric learning network. Finally, the trained network can be installed on an embedded computing platform and used in conjunction with portable or handheld Raman spectroscopic detection sensors to perform material identification tasks at various scales. A series of experiments demonstrate that our trained deep metric learning network outperforms existing mainstream machine learning models on classification tasks of different sizes. For the two tasks of Raman spectral classification of natural minerals of 260 classes and Raman spectral classification of pathogenic bacteria of 8 classes with significant noise, our suggested model achieved 98.05% and 90.13% classification accuracy, respectively. Finally, we also deployed the model in a handheld Raman spectrometer and conducted identification experiments on 350 samples of chemical substances attributed to 32 classes, achieving a classification accuracy of 99.14%. These results demonstrate that our method can greatly improve the efficiency of developing Raman spectroscopy-based substance detection devices and can be widely used in tasks of unknown substance identification. [ABSTRACT FROM AUTHOR]

[Display omitted] • We designed an innovative Raman spectroscopy system that enables rapid nondestructive detection of food adulteration. • We constructed the first quantitative analysis model for rice flour adulteration with Rongalite. • We propose an innovative model SARNet based on residual structure combined with autoencoder for real-time detection of Rongalite content based on Raman spectroscopy. Rice flour is a raw material for various foods and is used as a substitute for wheat flour. However, some merchants adulterate rice flour with the illegal additive Rongalite to extend the shelf life and earn illegal profits. Rongalite is highly carcinogenic, and ingestion of more than 10 g can even cause death. high-performance liquid chromatography (HPLC) and mass spectrometry (MS) are currently the main methods for detecting food adulteration, however, the existing methods have many limitations, complex operation, expensive instrumentation, etc. Raman spectroscopy has the advantages of convenience and non-destructive samples, but Raman spectroscopy can be affected by interference such as fluorescence background that affects detection, in addition to the problem of difficult quantitative analysis due to nonlinear bias. In this article, we used the preprocessing method of Savitzky-Golay smoothing filtering and VTPspline to improve the quality of the spectra and proposed the SARNet, which combines autoencoder and residual network to achieve the quantitative analysis of Rongalite content in rice flour. The new model combines a linear model with a nonlinear model, which can solve the nonlinear problem effectively. Experiments showed that the new SARNet model achieved state-of-the-art results, achieving the best R2 of 0.9703 and RMSEP of 0.0075. The lowest Rongalite concentration detected by the portable Raman spectrometer was 0.49%. In summary, the proposed method using portable Raman spectroscopy combined with machine learning has low detection bias and high accuracy, which can realize quantitative analyses of adulterated Rongalite in rice flour quickly. The method provides an accurate and nondestructive analytical tool in the field of food detection. [ABSTRACT FROM AUTHOR]