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FruitProM-V2: Robust Probabilistic Maturity Estimation and Detection of Fruits and Vegetables paper by Rahul Harsha Cheppally
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Paper

FruitProM-V2: Robust Probabilistic Maturity Estimation and Detection of Fruits and Vegetables

by Rahul Harsha Cheppally arxiv/2604.26084
Free2AITools Nexus Index
38.5 Top 6%
S: Semantic 50

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A: Authority 0
P: Popularity 0
R: Recency 82
Q: Quality 60
Tech Context
Vital Performance —

Accurate fruit maturity identification is essential for determining harvest timing, as incorrect assessment directly affects yield and post-harvest quality. Although ripening is a continuous biological process, vision-based maturity estimation is typically formulated as a multi-class classification task, which imposes sharp boundaries between visually similar stages. To examine this limitation, we perform an annotation reliability study with two independent annotators on a held-out tomato dat...

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Paper Information Summary
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Registry ID 2604.26084
License arXiv
Provider arxiv
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Cite this paper

Academic & Research Attribution

BibTeX 
@misc{arxiv_2604_26084,
  author = {Rahul Harsha Cheppally},
  title = {FruitProM-V2: Robust Probabilistic Maturity Estimation and Detection of Fruits and Vegetables Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2604.26084}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
Rahul Harsha Cheppally. (2026). FruitProM-V2: Robust Probabilistic Maturity Estimation and Detection of Fruits and Vegetables [Paper]. Free2AITools. https://arxiv.org/abs/2604.26084

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⚖ī¸ Free2AITools Nexus Index V2.0

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Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 0
Popularity (P) 0
Recency (R) 82
Quality (Q) 60

đŸ’Ŧ Index Insight

FNI V2.0 for FruitProM-V2: Robust Probabilistic Maturity Estimation and Detection of Fruits and Vegetables: Authority (A:0), Popularity (P:0), Recency (R:82), Quality (Q:60). Semantic (S) is a query-time baseline scored live at search.

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📝 Executive Summary

"Accurate fruit maturity identification is essential for determining harvest timing, as incorrect assessment directly affects yield and post-harvest quality. Although ripening is a continuous biological process, vision-based maturity estimation is typically formulated as a multi-class classification task, which imposes sharp boundaries between visually similar stages. To examine this limitation, we perform an annotation reliability study with two independent annotators on a held-out tomato dat..."

❝ Cite Node 

@article{Cheppally2026FruitProM-V2:,
  title={FruitProM-V2: Robust Probabilistic Maturity Estimation and Detection of Fruits and Vegetables},
  author={Rahul Harsha Cheppally},
  journal={arXiv preprint arXiv:2604.26084},
  year={2026}
}

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Rahul Harsha Cheppally

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📊 Research Signals

📅1970Published
⏱ī¸82RecencyFNI pillar
✅60QualityFNI pillar
🗂ī¸cs.CVField

🏷ī¸ Research Topics

vision models
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🆔 Identity & Source

id
2604.26084
slug
2604.26084
source
arxiv
author
Rahul Harsha Cheppally
license
arXiv
tags
arxiv:cs.CV, arxiv:cs.AI, arxiv:cs.RO

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params billions
null
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