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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]

• Conventional quality analysis of emulsions proved subjective and time-consuming. • Machine learning outperformed deep learning predicting in-process emulsion quality. • Droplet characteristics are better predictors compared to pixel intensity. • Random Forest model presented 99.8% accuracy. • The automated approach demonstrated promising potential in reducing over processing. Determining the uniformity and consistency of droplet size in the dispersed phase is key to emulsion stability. Conventional methods, which include manual microscopic evaluation and laser diffraction, have presented many challenges to achieve an accurate evaluation of droplet dispersion in pharmaceutical emulsions. Artificial intelligence techniques have demonstrated potential in overcoming the subjectivity and time-consumption associated with the manual approaches in industry. A new automated machine learning approach is presented in this study to predict in-process emulsion quality from micrographs. Droplet characteristics are extracted from emulsion micrographs using a histogram-based image segmentation technique. Machine learning classification models are developed, with a selected set of droplet characteristics as predictors, via Random Forest, Multinomial Logistic Regression and Vanilla Neural Network to classify the micrographs into four categories. The hyper-parameters of the models are tuned using 10-fold cross validation. A pixel-based Convolutional Neural Network model is also investigated. Random Forest presented the best accuracy of 99.78% compared to the deep learning models, which presented a bias towards the high frequency classes. The automated machine learning approach demonstrated promising potential for inline emulsion quality evaluation. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes an object detection model named Feature Enriching Object Detection Framework with Weak Segmentation Loss (FEOD) based on convolutional neural networks, which is an improvement of You Only Look Once (YOLO). To overcome the shortcoming of insensitivity to small objects, a novel feature enriching module is proposed to augment the semantic information of the feature in the shallow layer within a typical deep detector. Meanwhile, Focal Loss is also introduced to our model to further improve the algorithm performance. To obtain features more suitable for object detection, a more powerful feature extractor – DetNet is used as the backbone. An mAP of 83.8 in VOC2007 test and an mAP of 82.1 in VOC2012 test are achieved with a FPS of 11.3 with a NVIDIA GTX1080Ti GPU. For a lower resolution version, we achieve an mAP of 81.9 and 80.9 respectively in VOC2007 and VOC2012 tests with FPS of 17. Comparisons with other object detection algorithms have shown that our method works well and achieves a trade-off of accuracy and speed. [ABSTRACT FROM AUTHOR]

Depth information has been discovered to improve the performance of computer vision tasks, such as semantic segmentation and object recognition. However, careful acquisition of depth data needs highly developed depth sensors which are expensive. As a classic computer vision task, depth estimation from a single image has obtained promising results based on supervised learning methods. In this paper, we investigate the extension of color images with corresponding deep-regressed depth images in boosting the performance of semantic segmentation. Furthermore, the usage of combining color channels with the estimated depth or the ground truth depth channel is compared. Specifically, there are two stages in our work. Firstly, we adopt the framework of convolutional neural networks (CNN) for the depth estimation by combing the global depth network and the depth gradient network. After refining based on these two networks, the depth image map can be estimated in a deep-regressed manner. Secondly, after augmenting the color images with the predicted depth images, fully convolutional networks (FCN) are further used to implement the pixel-level semantic labeling. In the experiments, we employ two popular RGBD datasets, i.e., SUNRGBD and NYUDv2, for 37 and 40-class semantic segmentation, respectively. By comparing with the ground truth depth images, experimental results demonstrate that the networks trained on the estimated depth images can achieve comparable performance on facilitating the accuracy of semantic segmentation task. [ABSTRACT FROM AUTHOR]

Regularization is an essential technique discussed in an attempt to solve the overfitting problem in deep convolutional neural networks (CNNs). In this paper, we proposed a novel companion objective function as a regularization strategy to boost the classification performance in deep CNNs. Three aspects of this companion objective function are studied. Firstly, we proposed two kinds of auxiliary supervision for convolutional filters and non-linear activations respectively in the companion objective function. Both of them enhanced the performance by aleviating the overfitting problem and auxiliary supervision for non-linear activations provided more efficiency. Secondly, regularization of auxiliary supervision in the pre-training phrase is discussed. With the assistance of auxiliary supervision, CNNs could obtain a more favorable initialization for end-to-end supervised fine-tuning. Finally, this companion objective function is verified to be compatible with other regularization strategies such as dropout and data augmentation. Experimental results on benchmark datasets (CIFAR-10 and CIFAR-100) demonstrated advantages of our proposed companion objective function as a regularization approach. [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]