Multispectral Cameras and Machine Learning Integrated into Portable Devices as Clay Prediction Technology

Helfer, Gilson Augusto; Barbosa, Jorge Luís Victória; Alves, Douglas; Costa, Adilson Ben da; Beko, Marko; Leithardt, Valderi Reis Quietinho

doi:10.3390/jsan10030040

Cited by 20 publications

(10 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of Context Histories is an emerging research theme considered strategic for recording data in a standardized structure that allows the application of advanced data analysis algorithms. In this sense, Context histories allow analyzes based on context prediction [97][98][99], pattern and similarity analysis [100,101], data privacy management [102] and profile management [103], finally, this literature review serves as a basis for future works aimed to implement intelligent services applied to the identification of cardiovascular diseases through heart sounds.…”

Section: Final Considerationsmentioning

confidence: 99%

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

et al. 2021

Self Cite

View full text Add to dashboard Cite

This article presents a systematic mapping study dedicated to conduct a literature review on machine learning and IoT applied in the identification of diseases through heart sounds. This research was conducted between January 2010 and July 2021, considering IEEE Xplore, PubMed Central, ACM Digital Library, JMIR—Journal of Medical Internet Research, Springer Library, and Science Direct. The initial search resulted in 4372 papers, and after applying the inclusion and exclusion criteria, 58 papers were selected for full reading to answer the research questions. The main results are: of the 58 articles selected, 46 (79.31%) mention heart rate observation methods with wearable sensors and digital stethoscopes, and 34 (58.62%) mention care with machine learning algorithms. The analysis of the studies based on the bibliometric network generated by the VOSviewer showed in 13 studies (22.41%) a trend related to the use of intelligent services in the prediction of diagnoses related to cardiovascular disorders.

show abstract

Section: Final Considerationsmentioning

confidence: 99%

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…The use of Context histories is an emerging research theme considered strategic for recording data in a standardized structure that allows the application of advanced data analysis algorithms. In this sense, Context histories allow analyzes based on context prediction [99][100][101], pattern and similarity analysis [102,103], data privacy management [104] and profile management [105], Finally, this literature review serves as a basis for future works aimed to implement intelligent services applied to the identification of cardiovascular diseases through heart sounds.…”

Section: Final Considerationsmentioning

confidence: 99%

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

Brites¹,

Silva²,

Barbosa³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

This article presents a systematic mapping study dedicated to conduct a literature review on machine learning and IoT applied in the identification of diseases through heart sounds. This research was conducted between January 2010 and July 2021, considering IEEE Xplore, PubMed Central, ACM Digital Library, JMIR- Journal of Medical Internet Research, Springer Library, and Science Direct. The initial search resulted in 4,372 papers, and after applying the inclusion and exclusion criteria, 58 papers were selected for full reading to answer the research questions. The main results are: of the 58 articles selected, 46 (79.31%) mention heart rate observation methods with wearable sensors and digital stethoscopes, and 34 (58.62%) mention care with machine learning algorithms. The analysis of the studies based on the bibliometric network generated by the VOSviewer showed in 13 studies (22.41%) a trend related to the use of intelligent services in the prediction of diagnoses related to cardiovascular disorders.

show abstract

“…Hyperspectral imaging (HSI) is a modern technology widely recognized as a nondestructive tool for many applications, such as in the early assessment of fruit ripeness to determine the optimal time of harvest and predict crop yield [19]. Moreover, the use of spectral images has been utilized in clay prediction necessary for assessing soil fertility [20], classification and safety inspection of fruits and vegetables [21,22], and prediction of quality of bananas based on internal factors, such as total soluble solids, moisture content, and firmness [5]. With the integration of artificial intelligence (AI) and a robotic system, HSI technology provides superior performance compared to humans and even more physiological information about fruit than what a human can perceive without cutting/tasting the fruit; thus, reducing waste [23].…”

Section: Introductionmentioning

confidence: 99%

Multi-Input Deep Learning Model with RGB and Hyperspectral Imaging for Banana Grading

Mesa

Chiang

2021

Agriculture

View full text Add to dashboard Cite

Grading is a vital process during the postharvest of horticultural products as it dramatically affects consumer preference and satisfaction when goods reach the market. Manual grading is time-consuming, uneconomical, and potentially destructive. A non-invasive automated system for export-quality banana tiers was developed, which utilized RGB, hyperspectral imaging, and deep learning techniques. A real dataset of pre-classified banana tiers based on quality and size (Class 1 for export quality bananas, Class 2 for the local market, and Class 3 for defective fruits) was utilized using international standards. The multi-input model achieved an excellent overall accuracy of 98.45% using only a minimal number of samples compared to other methods in the literature. The model was able to incorporate both external and internal properties of the fruit. The size of the banana was used as a feature for grade classification as well as other morphological features using RGB imaging, while reflectance values that offer valuable information and have shown a high correlation with the internal features of fruits were obtained through hyperspectral imaging. This study highlighted the combined strengths of RGB and hyperspectral imaging in grading bananas, and this can serve as a paradigm for grading other horticultural crops. The fast-processing time of the multi-input model developed can be advantageous when it comes to actual farm postharvest processes.

show abstract

Multispectral Cameras and Machine Learning Integrated into Portable Devices as Clay Prediction Technology

Cited by 20 publications

References 46 publications

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

Multi-Input Deep Learning Model with RGB and Hyperspectral Imaging for Banana Grading

Contact Info

Product

Resources

About