Real Time Classification of Fruits and Vegetables Deployed on Low Power Embedded Devices Using Tiny ML
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Authors in [120] developed a TinyML CNN model that aims to classify between infected and healthy coffee plant.
“The authors in [120] developed a TinyML CNN model aims to classify between infected and healthy coffee plant.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [120]
The model in [120] aims to monitor the health of coffee plants and prevent propagation of the epidemic to others.
“This model aims to monitor the health of the coffee plants and prevents the propagation of the epidemic to the others.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [120]
The citation lists haven’t been fetched yet
References 14 · Cited by 14
- Authors
- Gutti, V., & Karthi, R.
- Affiliations
- Amrita Vishwa Vidyapeetham (IN)
- Corresponding author
- Karthi, R.
- ORCID
- Karthi, R.
- Venue
- Lecture Notes in Networks and Systems
- Publisher
- Springer International Publishing
- Published
- 2022
- Pages
- 347-359
- ISSN
- 2367-3370 (ISSN-L)
- 2367-3389
- OpenAlex
- W4288097308
- Cited by
- 14
- References
- 14
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 5 · 2023: 3 · 2022: 1
- FWCI
- 1.18
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in networks and systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- fruit and vegetable classification, TinyML, low-power embedded devices, ESP32 microcontroller, MobileNetV1, convolutional neural networks, real-time inference, agricultural product sorting, edge deployment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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