Identification of Ornamental Plants Via Google Lens Based on Intersemiotic

Nuraini, Nuraini; Faridy, Najihatul; Nursamsu, Nursamsu

doi:10.29303/jppipa.v8i3.1627

Cited by 3 publications

(3 citation statements)

References 42 publications

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“…This screening application would capture retinal images using Google Lens or automatically become linked to Google Lens. Google Lens [28,36], being a vision-assisted AI tool, would then search the web or dedicate DR image libraries to find an appropriate match. This match would then inform the patient about the grade of DR, including no DR, mild DR, moderate DR, severe DR, and presence/absence of DME [30].…”

Section: Future and Implications Of Google Lens-based Dr Screeningmentioning

confidence: 99%

Google Lens: A potential cost-effective screening tool for diabetic retinopathy

Venkatesh¹

2022

Med Hypothesis Discov Innov Optom

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Background: Diabetic retinopathy (DR) is a major, sight-threatening complication of diabetes mellitus. Blindness from DR can be prevented by successful and proactive screening. However, DR is screened in less than half of the patients because of barriers in availability, affordability, accessibility, and awareness. Although artificial intelligence (AI)-based algorithms are being evaluated for DR screening, they have limitations of infrastructure, accessibility, training, and manpower cost. Therefore, simpler and more practical DR screening tools should be explored. Hypothesis: Google Lens, an easily available, vision- and AI-based application in most smartphones, is a potential tool for cost-effective DR screening. It recognises images through a visual analysis based on neural networking. Thus, it can recognize retinal disorders, such as DR, in images. The development and adoption of Google Lens-based DR screening would have several advantages over the conventional hospital/specialist/healthcare facility-based approach, including widespread accessibility, acceptable accuracy, reduction in the direct cost of healthcare for patients with diabetes mellitus, and active patient participation in self-care. Conclusions: DR screening, detection, and grading using Google Lens is a feasible and effective option. Despite current limitations, it could transform DR screening from a costly, hospital- and expert-based method to a cost-effective, self-applicable, and home-based one. However, diagnostic accuracy studies comparing the index test with Google Lens-based screening are required to determine the usability and validity of this proposed screening tool for DR.

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Section: Future and Implications Of Google Lens-based Dr Screeningmentioning

confidence: 99%

Google Lens: A potential cost-effective screening tool for diabetic retinopathy

Venkatesh¹

2022

Med Hypothesis Discov Innov Optom

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“…Masropah et al (2022) concluded that learning biology on plant concepts using Google Lens really helps students in constructing their own knowledge, apart from being able to analyze digital literacy skills, it can also improve student activities and learning outcomes (Sofian et al, 2022). Google Lens is the most recommended application for the process of plant identification in biology learning (Nuraini et al, 2022;Shapovalov et al, 2019). Not only Google Lens, other similar applications are used by Sugandi et al (2020) namely the Pl@ntNet application, Kumar et al (2012)) with Leaf Snap application, and Hannah et al (2020) with the Picture This application which showed that the application used in the study had a good performance in identifying plants.…”

Section: Introductionmentioning

confidence: 99%

Comparison of plant identifier application ability as a recommendation for biology learning

Wilsa

Nugroho

Prasetyo

2023

Biosfer

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This study aims to test the ability of the Plant Identifier application to identify plants as a recommendation for biology learning and practicum on plant identification and classification materials. The applications used in this research are Google Lens, Plant Net, Picture This, and Leaf Snap. Object samples include moss plants, ferns, dicot plants, and monocot plants. This study uses the VES (Visual Encounter Survey) method with an exploratory-descriptive approach. Application testing includes the speed of object detection, the accuracy of detecting objects based on shape and color, as well as the completeness of plant taxonomy information which includes division, class, order, family, genus, and species. The results of application testing show that: (1) the Google Lens and Picture This applications are superior in terms of speed in detecting objects, (2) Google Lens and Picture This are also superior in terms of accuracy in detecting objects based on shape and color, (3) the Picture This application is an application superior in completeness of taxonomic information. Based on these findings it can be concluded that the Picture This application is the most superior application and can be a recommendation for biology learning and practicum on plant identification and classification materials

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“…The role of the agricultural sector in the economy of a country or a region can be seen from several aspects. The contribution of the agricultural sector to the Gross Domestic Product (GDP) or to the Gross Regional Domestic Product (GRDP) (Nuraini et al, 2022;Muskitta et al, 2023). The contribution of the agricultural sector to employment opportunities.…”

Section: Introductionmentioning

confidence: 99%

Implementation of C4.5 and K-Nearest Neighbor to Predict Palm Oil Fruit Production on Local Plantations

Rahmahwati,

Kirana

2023

jppipa, pendidikan ipa, fisika, biologi, kimia

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The agricultural sector, especially oil palm plantations, has an important role in the Indonesian economy. Oil palm farming has increased the welfare of farmers and provided employment. In this study, researchers used the C4.5 and K-Nearest Neighbor algorithms in the RapidMiner application to analyze oil palm fruit production data after going through the data preprocessing process. The C4.5 algorithm produces a decision tree with an accuracy of 94.12%, while the KNN algorithm achieves an accuracy of 82.53%. Based on these results, it can be concluded that the C4.5 algorithm has a higher accuracy in classifying oil palm fruit production based on existing attributes.

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Identification of Ornamental Plants Via Google Lens Based on Intersemiotic

Cited by 3 publications

References 42 publications

Google Lens: A potential cost-effective screening tool for diabetic retinopathy

Google Lens: A potential cost-effective screening tool for diabetic retinopathy

Comparison of plant identifier application ability as a recommendation for biology learning

Implementation of C4.5 and K-Nearest Neighbor to Predict Palm Oil Fruit Production on Local Plantations

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