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Lv Z, Cheng C, Lv H. Automatic identification of pavement cracks in public roads using an optimized deep convolutional neural network model. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2023; 381:20220169. [PMID: 37454685 DOI: 10.1098/rsta.2022.0169] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 08/19/2022] [Indexed: 07/18/2023]
Abstract
The current study aims to improve the efficiency of automatic identification of pavement distress and improve the status quo of difficult identification and detection of pavement distress. First, the identification method of pavement distress and the types of pavement distress are analysed. Then, the design concept of deep learning in pavement distress recognition is described. Finally, the mask region-based convolutional neural network (Mask R-CNN) model is designed and applied in the recognition of road crack distress. The results show that in the evaluation of the model's comprehensive recognition performance, the highest accuracy is 99%, and the lowest accuracy is 95% after the test and evaluation of the designed model in different datasets. In the evaluation of different crack identification and detection methods, the highest accuracy of transverse crack detection is 98% and the lowest accuracy is 95%. In longitudinal crack detection, the highest accuracy is 98% and the lowest accuracy is 92%. In mesh crack detection, the highest accuracy is 98% and the lowest accuracy is 92%. This work not only provides an in-depth reference for the application of deep CNNs in pavement distress recognition but also promotes the improvement of road traffic conditions, thus contributing to the progression of smart cities in the future. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.
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Affiliation(s)
- Zhihan Lv
- Department of Game design, Faculty of Arts, 752 36 Uppsala, Uppsala University, Sweden
| | - Chen Cheng
- The Second Monitoring and Application Center, CEA, Xìan, People's Republic of China
| | - Haibin Lv
- North China Sea Offshore Engineering Survey Institute, Ministry Of Natural Resources North Sea Bureau, People's Republic of China
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Orka NA, Toushique FM, Uddin MN, Bari ML. Application of computer vision in assessing crop abiotic stress: A systematic review. PLoS One 2023; 18:e0290383. [PMID: 37611022 PMCID: PMC10446212 DOI: 10.1371/journal.pone.0290383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 08/07/2023] [Indexed: 08/25/2023] Open
Abstract
BACKGROUND Abiotic stressors impair crop yields and growth potential. Despite recent developments, no comprehensive literature review on crop abiotic stress assessment employing deep learning exists. Unlike conventional approaches, deep learning-based computer vision techniques can be employed in farming to offer a non-evasive and practical alternative. METHODS We conducted a systematic review using the revised Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement to assemble the articles on the specified topic. We confined our scope to deep learning-related journal articles that focused on classifying crop abiotic stresses. To understand the current state, we evaluated articles published in the preceding ten years, beginning in 2012 and ending on December 18, 2022. RESULTS After the screening, risk of bias, and certainty assessment using the PRISMA checklist, our systematic search yielded 14 publications. We presented the selected papers through in-depth discussion and analysis, highlighting current trends. CONCLUSION Even though research on the domain is scarce, we encountered 11 abiotic stressors across 7 crops. Pre-trained networks dominate the field, yet many architectures remain unexplored. We found several research gaps that future efforts may fill.
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Affiliation(s)
- Nabil Anan Orka
- Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT), Gazipur, Bangladesh
| | - Fardeen Md. Toushique
- Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT), Gazipur, Bangladesh
| | - M. Nazim Uddin
- Horticultural Research Centre (HRC), Bangladesh Agricultural Research Institute (BARI), Gazipur, Bangladesh
| | - M. Latiful Bari
- Food, Nutrition, and Agriculture Research Laboratory, Centre for Advanced Research in Sciences, University of Dhaka (DU), Dhaka, Bangladesh
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Gouda M, Ghazzawy HS, Alqahtani N, Li X. The Recent Development of Acoustic Sensors as Effective Chemical Detecting Tools for Biological Cells and Their Bioactivities. Molecules 2023; 28:4855. [PMID: 37375410 DOI: 10.3390/molecules28124855] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 06/14/2023] [Accepted: 06/14/2023] [Indexed: 06/29/2023] Open
Abstract
One of the most significant developed technologies is the use of acoustic waves to determine the chemical structures of biological tissues and their bioactivities. In addition, the use of new acoustic techniques for in vivo visualizing and imaging of animal and plant cellular chemical compositions could significantly help pave the way toward advanced analytical technologies. For instance, acoustic wave sensors (AWSs) based on quartz crystal microbalance (QCM) were used to identify the aromas of fermenting tea such as linalool, geraniol, and trans-2-hexenal. Therefore, this review focuses on the use of advanced acoustic technologies for tracking the composition changes in plant and animal tissues. In addition, a few key configurations of the AWS sensors and their different wave pattern applications in biomedical and microfluidic media progress are discussed.
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Affiliation(s)
- Mostafa Gouda
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
- Department of Nutrition & Food Science, National Research Centre, Dokki, Giza 12622, Egypt
| | - Hesham S Ghazzawy
- Date Palm Research Center of Excellence, King Faisal University, Al Ahsa 31982, Saudi Arabia
- Central Laboratory for Date Palm Research and Development, Agriculture Research Center, Giza 12511, Egypt
| | - Nashi Alqahtani
- Date Palm Research Center of Excellence, King Faisal University, Al Ahsa 31982, Saudi Arabia
| | - Xiaoli Li
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
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Gul Z, Bora S. Exploiting Pre-Trained Convolutional Neural Networks for the Detection of Nutrient Deficiencies in Hydroponic Basil. SENSORS (BASEL, SWITZERLAND) 2023; 23:5407. [PMID: 37420572 DOI: 10.3390/s23125407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 05/31/2023] [Accepted: 06/04/2023] [Indexed: 07/09/2023]
Abstract
Due to the integration of artificial intelligence with sensors and devices utilized by Internet of Things technology, the interest in automation systems has increased. One of the common features of both agriculture and artificial intelligence is recommendation systems that increase yield by identifying nutrient deficiencies in plants, consuming resources correctly, reducing damage to the environment and preventing economic losses. The biggest shortcomings in these studies are the scarcity of data and the lack of diversity. This experiment aimed to identify nutrient deficiencies in basil plants cultivated in a hydroponic system. Basil plants were grown by applying a complete nutrient solution as control and non-added nitrogen (N), phosphorous (P) and potassium (K). Then, photos were taken to determine N, P and K deficiencies in basil and control plants. After a new dataset was created for the basil plant, pretrained convolutional neural network (CNN) models were used for the classification problem. DenseNet201, ResNet101V2, MobileNet and VGG16 pretrained models were used to classify N, P and K deficiencies; then, accuracy values were examined. Additionally, heat maps of images that were obtained using the Grad-CAM were analyzed in the study. The highest accuracy was achieved with the VGG16 model, and it was observed in the heat map that VGG16 focuses on the symptoms.
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Affiliation(s)
- Zeki Gul
- Department of Computer Engineering, Ege University, 35100 Izmir, Turkey
| | - Sebnem Bora
- Department of Computer Engineering, Ege University, 35100 Izmir, Turkey
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Das Choudhury S, Saha S, Samal A, Mazis A, Awada T. Drought stress prediction and propagation using time series modeling on multimodal plant image sequences. FRONTIERS IN PLANT SCIENCE 2023; 14:1003150. [PMID: 36844082 PMCID: PMC9947149 DOI: 10.3389/fpls.2023.1003150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 01/09/2023] [Indexed: 06/18/2023]
Abstract
The paper introduces two novel algorithms for predicting and propagating drought stress in plants using image sequences captured by cameras in two modalities, i.e., visible light and hyperspectral. The first algorithm, VisStressPredict, computes a time series of holistic phenotypes, e.g., height, biomass, and size, by analyzing image sequences captured by a visible light camera at discrete time intervals and then adapts dynamic time warping (DTW), a technique for measuring similarity between temporal sequences for dynamic phenotypic analysis, to predict the onset of drought stress. The second algorithm, HyperStressPropagateNet, leverages a deep neural network for temporal stress propagation using hyperspectral imagery. It uses a convolutional neural network to classify the reflectance spectra at individual pixels as either stressed or unstressed to determine the temporal propagation of stress in the plant. A very high correlation between the soil water content, and the percentage of the plant under stress as computed by HyperStressPropagateNet on a given day demonstrates its efficacy. Although VisStressPredict and HyperStressPropagateNet fundamentally differ in their goals and hence in the input image sequences and underlying approaches, the onset of stress as predicted by stress factor curves computed by VisStressPredict correlates extremely well with the day of appearance of stress pixels in the plants as computed by HyperStressPropagateNet. The two algorithms are evaluated on a dataset of image sequences of cotton plants captured in a high throughput plant phenotyping platform. The algorithms may be generalized to any plant species to study the effect of abiotic stresses on sustainable agriculture practices.
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Affiliation(s)
- Sruti Das Choudhury
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, United States
- School of Computing, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Sinjoy Saha
- Institute of Radio Physics and Electronics, University of Calcutta, Kolkata, West Bengal, India
| | - Ashok Samal
- Institute of Radio Physics and Electronics, University of Calcutta, Kolkata, West Bengal, India
| | - Anastasios Mazis
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, United States
- Department of Civil and Environmental Engineering, University of California, Merced, Merced, CA, United States
| | - Tala Awada
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, United States
- Agricultural Research Division, University of Nebraska-Lincoln, Lincoln, NE, United States
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Recent Advances of Smart Systems and Internet of Things (IoT) for Aquaponics Automation: A Comprehensive Overview. CHEMOSENSORS 2022. [DOI: 10.3390/chemosensors10080303] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Aquaponics is an innovative, smart, and sustainable agricultural technology that integrates aquaculture (farming of fish) with hydroponics in growing vegetable crops symbiotically. The correct implementation of aquaponics helps in providing healthy organic foods with low consumption of water and chemical fertilizers. Numerous research attempts have been directed toward real implementations of this technology feasibly and reliably at large commercial scales and adopting it as a new precision technology. For better management of such technology, there is an urgent need to use the Internet of things (IoT) and smart sensing systems for monitoring and controlling all operations involved in the aquaponic systems. Thence, the objective of this article is to comprehensively highlight research endeavors devoted to the utilization of automated, fully operated aquaponic systems, by discussing all related aquaponic parameters aligned with smart automation scenarios and IoT supported by some examples and research results. Furthermore, an attempt to find potential gaps in the literature and future contributions related to automated aquaponics was highlighted. In the scope of the reviewed research works in this article, it is expected that the aquaponics system supported with smart control units will become more profitable, intelligent, accurate, and effective.
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Gouda M, He Y, Bekhit AED, Li X. Emerging Technologies for Detecting the Chemical Composition of Plant and Animal Tissues and Their Bioactivities: An Editorial. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27092620. [PMID: 35565969 PMCID: PMC9105901 DOI: 10.3390/molecules27092620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 04/18/2022] [Indexed: 11/16/2022]
Abstract
Integrating physical and chemical technologies for the characterization and modification of plants and animal tissues has been used for several decades to improve their detection potency and quality [...].
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Affiliation(s)
- Mostafa Gouda
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
- Department of Nutrition & Food Science, National Research Centre, Dokki, Giza 12422, Egypt
- Correspondence: or (M.G.); (Y.H.); (X.L.)
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
- Correspondence: or (M.G.); (Y.H.); (X.L.)
| | - Alaa El-Din Bekhit
- Department of Food Sciences, University of Otago, Dunedin 9054, New Zealand;
| | - Xiaoli Li
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
- Correspondence: or (M.G.); (Y.H.); (X.L.)
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