A state-of-the-art review of image motion deblurring techniques in precision agriculture

Yu, Hongbo; Li, Daoliang; Chen, Yingyi

doi:10.1016/j.heliyon.2023.e17332

Cited by 9 publications

(4 citation statements)

References 92 publications

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“…A need for deconvolution arises in various areas-for example, in the compensation of blur caused by the movement of a camera or object at shooting [43], or the correction of optical artifacts in microscopy [44] and astronomy [45]. The case in which the distorting function (convolution operator) is known in advance is "non-blind" deconvolution.…”

Section: Deep Learning-based Refinement Of Inverse Modeling Resultsmentioning

confidence: 99%

Hybrid Deep Learning and Sensitivity Operator-Based Algorithm for Identification of Localized Emission Sources

Penenko,

Emelyanov,

Rusin

et al. 2023

Mathematics

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Hybrid approaches combining machine learning with traditional inverse problem solution methods represent a promising direction for the further development of inverse modeling algorithms. The paper proposes an approach to emission source identification from measurement data for advection–diffusion–reaction models. The approach combines general-type source identification and post-processing refinement: first, emission source identification by measurement data is carried out by a sensitivity operator-based algorithm, and then refinement is done by incorporating a priori information about unknown sources. A general-type distributed emission source identified at the first stage is transformed into a localized source consisting of multiple point-wise sources. The second, refinement stage consists of two steps: point-wise source localization and emission rate estimation. Emission source localization is carried out using deep learning with convolutional neural networks. Training samples are generated using a sensitivity operator obtained at the source identification stage. The algorithm was tested in regional remote sensing emission source identification scenarios for the Lake Baikal region and was able to refine the emission source reconstruction results. Hence, the aggregates used in traditional inverse problem solution algorithms can be successfully applied within machine learning frameworks to produce hybrid algorithms.

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Section: Deep Learning-based Refinement Of Inverse Modeling Resultsmentioning

confidence: 99%

Hybrid Deep Learning and Sensitivity Operator-Based Algorithm for Identification of Localized Emission Sources

Penenko,

Emelyanov,

Rusin

et al. 2023

Mathematics

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“…Video enhancement [53] Image/video deblurring [54] Education Cheating prevention (S) [55] Engineering Importing real life objects into modeling software [56] Food service Reduce food waste in restaurants (M) [57] Robotic food delivery https://starship.xyz/, accessed on 15 July 2023 Gaming Xbox Kinect [58] Gesture based gaming [59] Government Control traffic lights [60] Detecting natural disasters (M) [61] Insurance Insurance appraisals [10,62,63] Law enforcement Forensic analysis [64] Facial recognition in large crowds (S) [65] Identity verification (S) [66] Detect dangerous situations (S) [67] Speeding enforcement [14] Manufacturing Defective products on an assembly line [68] Manufacturing Workplace inspection [69] Medical See Table 3 Military Terrain Reconnaissance (S) [70] Automate military drones [71] Movie Film movie restoration […”

Section: Digital Designmentioning

confidence: 99%

Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings

Lindroth,

Nalaie,

Raghu

et al. 2024

J. Imaging

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Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or a sequence of images to recognize content, has been used extensively across industries in recent years. However, in the healthcare industry, its applications are limited by factors like privacy, safety, and ethical concerns. Despite this, CV has the potential to improve patient monitoring, and system efficiencies, while reducing workload. In contrast to previous reviews, we focus on the end-user applications of CV. First, we briefly review and categorize CV applications in other industries (job enhancement, surveillance and monitoring, automation, and augmented reality). We then review the developments of CV in the hospital setting, outpatient, and community settings. The recent advances in monitoring delirium, pain and sedation, patient deterioration, mechanical ventilation, mobility, patient safety, surgical applications, quantification of workload in the hospital, and monitoring for patient events outside the hospital are highlighted. To identify opportunities for future applications, we also completed journey mapping at different system levels. Lastly, we discuss the privacy, safety, and ethical considerations associated with CV and outline processes in algorithm development and testing that limit CV expansion in healthcare. This comprehensive review highlights CV applications and ideas for its expanded use in healthcare.

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“…Artificial intelligence and computer techniques enable the use of precision agriculture technologies in fertilisation, the application of plant protection products, and precise agricultural and technical operations such as sowing, planting potatoes or vegetable seedlings, etc. [ 44 , 45 , 46 , 47 , 48 , 49 ].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network (CNN) Model for the Classification of Varieties of Date Palm Fruits (Phoenix dactylifera L.)

Rybacki,

Niemann,

Derouiche

et al. 2024

Sensors

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The popularity and demand for high-quality date palm fruits (Phoenix dactylifera L.) have been growing, and their quality largely depends on the type of handling, storage, and processing methods. The current methods of geometric evaluation and classification of date palm fruits are characterised by high labour intensity and are usually performed mechanically, which may cause additional damage and reduce the quality and value of the product. Therefore, non-contact methods are being sought based on image analysis, with digital solutions controlling the evaluation and classification processes. The main objective of this paper is to develop an automatic classification model for varieties of date palm fruits using a convolutional neural network (CNN) based on two fundamental criteria, i.e., colour difference and evaluation of geometric parameters of dates. A CNN with a fixed architecture was built, marked as DateNET, consisting of a system of five alternating Conv2D, MaxPooling2D, and Dropout classes. The validation accuracy of the model presented in this study depended on the selection of classification criteria. It was 85.24% for fruit colour-based classification and 87.62% for the geometric parameters only; however, it increased considerably to 93.41% when both the colour and geometry of dates were considered.

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A state-of-the-art review of image motion deblurring techniques in precision agriculture

Cited by 9 publications

References 92 publications

Hybrid Deep Learning and Sensitivity Operator-Based Algorithm for Identification of Localized Emission Sources

Hybrid Deep Learning and Sensitivity Operator-Based Algorithm for Identification of Localized Emission Sources

Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings

Convolutional Neural Network (CNN) Model for the Classification of Varieties of Date Palm Fruits (Phoenix dactylifera L.)

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