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The image-based automatic plant disease identification has emerged as a prominent research area, driven by recent advancements in machine learning algorithms. Conventional disease detection methods rely on manual feature extraction from acquired images, involving extensive expertise from experts and pathologists. This labor-intensive practice is often time-consuming and impractical. With the recent advancements in digital technologies and computer vision, Deep neural networks address these challenges by learning the features automatically. This paper focuses on the EfficientNet B0 architecture-based deep neural network model integrated with a convolutional block attention mechanism module for identifying major wheat crop diseases. The attention module in the EfficientNet B0 model improves its performance by emphasizing the disease-infected regions in leaf images. To evaluate the proposed methodology, experiments were conducted on the WheatRust21 image dataset, comprising 6556 images of three major fungal diseases, namely, stripe rust, leaf rust, and stem rust, collected in natural field conditions from 2019 to 2021. Our developed EfficientNet B0-CBAM model performed well with a testing accuracy of 98.70% and an impressive F1 score of 98% on the dataset. The Grad-CAM visualization demonstrates that the attention module focuses more on the infected region and plays a crucial role in wheat disease identification. The results are quantitatively substantial, with the model achieving high accuracy and other evaluation metrics scores with an average of 98.5 %. These findings hold promise for practical applications in agriculture and sustainable crop management. [ABSTRACT FROM AUTHOR]

Date fruit (Phoenix dactylifera L.) is known for its diversity in terms of genotypes that differ in shape, color, texture, taste, and other physical and sensory attributes, in addition to biochemical compounds. This richness generates complex, long, and tedious work at the scale of farm as well as in handling, processing, and storage facilities to prepare standardized, and homogenous final products that deal with consumer preferences and needs. To alleviate this, an increased importance and particular trend were observed recently for the use of artificial intelligence (AI) techniques and computer vision technology since they are considered as accurate, objective, non-destructive, fast, and efficient emerging technologies. In this context, this manuscript provides comprehensive insights into key applications of AI techniques and computer vision technology, using different classification models and algorithms developed on the basis (for example) of neural network classifiers, such as convolutional and artificial neural network, on harvest and postharvest assessment of date fruit. Thus, the importance of date fruit dataset used in the AI domain was discussed with link to all accessible datasets in the field of date palm. In addition, available and relevant AI tools and approaches applied to the postharvest of date fruit from maturity distinguishing until storage through other key handling operations, sorting, grading, etc., were detailed in the five sections of this paper. In terms of conclusion, this review showed that these smart technologies can play a substantial role and offer new opportunities to improve the whole value chain of date palm sector. • Harvesting and post-harvest handling impact quality and storability of dates. • These operations are tedious and subjective leading to quantitative/qualitative losses. • Artificial intelligence (AI) techniques are emergent technologies that can reduce these losses. • AI techniques are more accurate, fast, non-destructive and objective. [ABSTRACT FROM AUTHOR]

Nutrition plays a vital role in maintaining human health. Traditional methods used for assessing food composition & nutritional content often require destructive sample preparation, which can be time-consuming and costly. Therefore, computer vision-based approaches have emerged as promising alternatives that enable rapid and non-destructive analysis of various nutritional parameters in foods. In this review, we summarized computer vision applications in meat processing, grains, fruits and vegetables, and seafood. We reviewed recent advancements in computer vision and deep learning-based algorithms employed for food recognition and nutrient estimation. Various existing food recognition and nutrient estimation datasets are also reviewed. Conventional methods offer some limitations, while vision-based technologies provide quick and non-destructive analysis of food composition & nutritional content. Computer vision and deep neural network architectures provide remarkable accuracy for food nutrient measurement. In conclusion, deep learning-based models are paving the way for a promising future in nutritional and health optimization research. In the future, vision-based technologies are expected to transform food classification and detection by enabling more rapid, affordable, and accurate nutritional analyses. Therefore, computer vision is developing into a useful tool for fast and precise evaluation of food nutrients without enabling samples to be damaged. [Display omitted] • Integrating nutrition analysis with computer vision for non-destructive food quality assessment are reviewed. • Transformer-based and CNN-based hybrid approaches are the accurate nutrient recognition methods in the food industry. • Vision-based technologies have revolutionized food analysis by facilitating faster, cost-effective, and precise methods. [ABSTRACT FROM AUTHOR]

Highlights • A deep-based model for solving facial expression recognition is proposed. • Propose a Salient Expression Region Descriptor (SERD) to locate expression-sensitive regions and refine deep features. • Construct an encoder-decoder framework to disentangle multiple variations (e.g. ages, races, poses, etc.) from features. • Experiments on six famous expression databases verify the effectiveness of the proposed model. Abstract Facial Expression Recognition (FER) has long been a challenging task in the field of computer vision. In this paper, we present a novel model, named Deep Attentive Multi-path Convolutional Neural Network (DAM-CNN), for FER. Different from most existing models, DAM-CNN can automatically locate expression-related regions in an expressional image and yield a robust image representation for FER. The proposed model contains two novel modules: an attention-based Salient Expressional Region Descriptor (SERD) and the Multi-Path Variation-Suppressing Network (MPVS-Net). SERD can adaptively estimate the importance of different image regions for FER task, while MPVS-Net disentangles expressional information from irrelevant variations. By jointly combining SERD and MPVS-Net, DAM-CNN is able to highlight expression-relevant features and generate a variation-robust representation for expression classification. Extensive experimental results on both constrained datasets (CK+, JAFFE, TFEID) and unconstrained datasets (SFEW, FER2013, BAUM-2i) demonstrate the effectiveness of our DAM-CNN model. [ABSTRACT FROM AUTHOR]

• Classification of roughness to unmeasured surfaces is firstly proposed. • No need to artificially design any indicators. • The method used in this article is robust to the light source environment. • This method is combined with machine vision system. At present, machine vision roughness detection mostly needs to design roughness related indexes based on images, and the index design has human intervention and is heavily dependent on the light source environment. To solve this problem, the paper classifies the surface roughness based on the deep convolutional neural network method, which can realize the roughness detection without index design. The most important thing is that the detection method has good light source robustness under different light source environments. The study adopts an end-to-end image analysis method, by means of image enhancement pre-processing of a small number of source images, after multi-layer convolution and pooling operations, as well as comprehensive processing of fully connected and classification layers, the convolutional kernel can automatically extract the features of the image, and finally the surface roughness obtained by vertical disc cutter milling can be classified and predicted. In addition, based on the experimental light source environment with two different luminance of night and day, by comparing with the technically mature ResNet50 and DenseNet121 convolutional neural network models, the deep convolutional neural network Xception model not only has high roughness classification accuracy, but also has more light source environment robustness. This method makes the online measurement of visual roughness possible. [ABSTRACT FROM AUTHOR]

[Display omitted] • A method combing NIRS and a computer vision system were proposed in this study. • The method (a sensor fusion strategy) is used for grading Keemun black tea. • A CNN model was constructed to extract NIRS features. • The accuracy of the proposed strategy was higher than that of a single-sensor strategy. • The sensor fusion strategy nearly attained 100% in grading black tea samples. Keemun black tea is classified into 7 grades according to the difference in its quality. The appearance and flavour are crucial indicators of its quality. This research demonstrates a rapid grading method of jointly using near-infrared reflectance spectroscopy (NIRS) and computer vision systems (CVS) to evaluate the flavour and appearance quality of tea. A Bruker MPA Fourier Transform near-infrared spectrometer was used to record the spectrum of samples. A computer vision system was used to capture the image of tea leaves in an unobstructed manner. 80 tea samples for each grade were analyzed. The performance of four NIRS feature extraction methods (principal component analysis, local linear embedding, isometric feature mapping, and convolutional neural network (CNN)) was compared in this study. Histograms of six geometric features (leaf width, leaf length, leaf area, leaf perimeter, aspect ratio, and rectangularity) of different tea samples were used to describe their appearance. A feature-level fusion strategy was used to combine softmax and artificial neural networks (ANN) to classify NIRS and CVS features. The results indicated that for an individual NIRS signal, CNN achieved the highest classification accuracy with the softmax classification model. The histograms of the combined shape features indicated that when the softmax classification model was used, the classification accuracy was also higher than ANN. The fusion of NIRS and CVS features proved to be the optimal combination; the accuracy of calibration, validation and testing sets increased from 99.29%, 96.67% and 98.57% (when the optimal features from a single-sensor were used) to 100.00%, 99.29% and 100.00% (when features from multiple-sensors were used). This study revealed that the combination of NIRS and CVS features can be a useful strategy for classifying black tea samples of different grades. [ABSTRACT FROM AUTHOR]

• Recent studies show the potential of deep learning for binary classification of skin lesions. • This research proposes an advanced deep convolution neural network model for multi-class classification of skin lesions. • The proposed model is designed with several layers, and multiple filter sizes, but fewer filters and parameters. • It is evaluated by the experimental results on the ISIC-19 database in terms of sensitivity, specificity, and other metrics. • It outperforms state-of-the-art algorithms, exhibiting 0.991 AUROC. As an analytic tool in medicine, deep learning has gained great attention and opened new ways for disease diagnosis. Recent studies validate the effectiveness of deep learning algorithms for binary classification of skin lesions (i.e., melanomas and nevi classes) with dermoscopic images. Nonetheless, those binary classification methods cannot be applied to the general clinical situation of skin cancer screening in which multi-class classification must be taken into account. The main objective of this research is to develop, implement, and calibrate an advanced deep learning model in the context of automated multi-class classification of skin lesions. The proposed Deep Convolutional Neural Network (DCNN) model is carefully designed with several layers, and multiple filter sizes, but fewer filters and parameters to improve efficacy and performance. Dermoscopic images are acquired from the International Skin Imaging Collaboration databases (ISIC-17, ISIC-18, and ISIC-19) for experiments. The experimental results of the proposed DCNN approach are presented in terms of precision, sensitivity, specificity, and other metrics. Specifically, it attains 94 % precision, 93 % sensitivity, and 91 % specificity in ISIC-17. It is demonstrated by the experimental results that this proposed DCNN approach outperforms state-of-the-art algorithms, exhibiting 0.964 area under the receiver operating characteristics (AUROC) in ISIC-17 for the classification of skin lesions and can be used to assist dermatologists in classifying skin lesions. As a result, this proposed approach provides a novel and feasible way for automating and expediting the skin lesion classification task as well as saving effort, time, and human life. [ABSTRACT FROM AUTHOR]

• We propose a novel neural network architecture for unconstrained text recognition. • Our proposed architecture uses only the highly efficient convolutional primitives • We achieve state-of-the-art results on seven public benchmark datasets. • Our proposed model has won the ICFHR2018 Competition on Automated Text Recognition. Unconstrained text recognition is an important computer vision task, featuring a wide variety of different sub-tasks, each with its own set of challenges. One of the biggest promises of deep neural networks has been the convergence and automation of feature extractors from input raw signals, allowing for the highest possible performance with minimum required domain knowledge. To this end, we propose a data-efficient, end-to-end neural network model for generic, unconstrained text recognition. In our proposed architecture we strive for simplicity and efficiency without sacrificing recognition accuracy. Our proposed architecture is a fully convolutional network without any recurrent connections trained with the CTC loss function. Thus it operates on arbitrary input sizes and produces strings of arbitrary length in a very efficient and parallelizable manner. We show the generality and superiority of our proposed text recognition architecture by achieving state-of-the-art results on seven public benchmark datasets, covering a wide spectrum of text recognition tasks, namely: Handwriting Recognition, CAPTCHA recognition, OCR, License Plate Recognition, and Scene Text Recognition. Our proposed architecture has won the ICFHR2018 Competition on Automated Text Recognition on a READ Dataset. [ABSTRACT FROM AUTHOR]

Piping and Instrumentation Diagrams (P&IDs) are the most commonly used engineering drawings to describe components and their relationships, and they are one of the most important inputs for data analysis in Nuclear Power Plants (NPP). In traditional analysis, the information related to the components is extracted manually from the P&IDs. This usually takes large amounts of effort and is error-prone. With the rapid development in the area of computer vision and deep learning, automatically detecting components and their relationships becomes possible. In this paper, we aim to use the latest neural network models to automatically extract information on components and their identifications, from the P&IDs in NPPs. We use a Faster Regional Convolutional Neural Networks (Faster RCNN) architecture called ResNet-50, to detect the components in the P&IDs. Compared to common object detection, object detection for P&IDs poses unique challenges to these methods. For example, the P&IDs symbols are much smaller than the background, and detecting such small objects remains a challenging task for modern neural networks. To address these challenges, we 1) propose several techniques for data augmentation that effectively solve the problem of training data shortage, and 2) propose a feature grouping strategy for detecting components with distinct features. Besides, we introduce a SegLink model for text detection, which can automatically extract components' identifications from P&IDs. We also develop a method for building a data structure to reflect the relationships between components (e.g., to which pipe a component is connected, or what are the downstream or upstream components of one specific component) based on the extracted information. This data structure can be further used for plant safety analysis, and operation and maintenance cost optimization. Sensitivity analysis and comparison with other Convolutional Neural Networks (CNNs) are performed. The results of these analyses are also discussed in this paper. This analysis framework has been tested on the P&IDs from a commercial NPP. The Average Precision for components, which is used to measure the performance of the proposed method, is about 98%. The success rates of component-text mapping and component-pipe mapping are 270/275 and 319/319, respectively. It is worth noting that this framework is generic and can also be applied to P&IDs of non-nuclear industries. [ABSTRACT FROM AUTHOR]

• We propose a unified model for recognizing human actions in static images. It explicitly investigates the body structure information as well as integrates the body structure exploration and action classification tasks into a unified model. Moreover, we design a twostep learning technique, where keypoint estimation provides intermediate supervision for learning human action representations. • We design two body structure cues, SBPs and LAD, to fully explore the structure information of human bodies from the local and global perspectives. • In order to construct body parts with different scales in unconstrained images, we propose a technique to use human keypoint heatmaps to generate scale adaptive SBPs, which extract fine-grained local human features. Moreover, we propose a technique to automatically determine the most discriminative body part of each action category for identifying the ongoing action. In order to extract global hightlevel body structure features, we propose the LAD to model the spatial angle relationship of pairs of human limbs. The LAD is more robust and achieves better performance compared with the distance based skeleton descriptor. • We evaluate our model on two challenging image-based action datasets, and the results show that our method achieves the state-of-the-art performance. Although Convolutional Neural Networks (CNNs) have made substantial improvements in many computer vision tasks, there remains room for improvements in image-based action recognition due to the limited capability to exploit the body structure information.In this work, we propose a unified deep model to explicitly explore body structure information and fuse multiple body structure cues for robust action recognition in images.In order to fully explore the body structure information, we design the Body Structure Exploration sub-network.It generates two novel body structure cues, Structural Body Parts and Limb Angle Descriptor, which capture structure information of human bodies from the global and local perspectives respectively. And then, we design the Action Classification sub-network to fuse the predictions from multiple body structure cues to obtain precise results. Moreover, we integrate the two sub-networks into a unified model by sharing the bottom convolutional layers, which improves the computational efficiency in both training and testing stages. We comprehensively evaluate our network on the challenging image-based human action datasets, Pascal VOC 2012 Action and Stanford40. Our approach achieves 93.5% and 93.8% mAP respectively, which outperforms all recent approaches in this field. [ABSTRACT FROM AUTHOR]