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Paper

Automating yellow rust disease identification in wheat using artificial intelligence

by Independent / Community 0109a69ec6ad18375a90011ef6ef60c133ff85f5

Free2AITools Nexus Index

66.8

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 79

P: Popularity 54

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

Plant disease has long been one of the major threats to world food security due to reduction in the crop yield and quality. Accurate and precise diagnosis of plant diseases has been a significant challenge. Cost-effective automated computational systems for disease diagnosis would facilitate advancements in agriculture. The objective of this paper is to explore computer vision based Artificial Intelligence method for automating the identification of yellow rust disease and improve the accurac...

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Semantic Scholar 17 Citations

Paper Information Summary
Entity Passport
Registry ID	0109a69ec6ad18375a90011ef6ef60c133ff85f5
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{0109a69ec6ad18375a90011ef6ef60c133ff85f5,
  author = {Unknown},
  title = {Automating yellow rust disease identification in wheat using artificial intelligence Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/0109a69ec6ad18375a90011ef6ef60c133ff85f5}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). Automating yellow rust disease identification in wheat using artificial intelligence [Paper]. Free2AITools. https://api.semanticscholar.org/0109a69ec6ad18375a90011ef6ef60c133ff85f5

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 79

Popularity (P) 54

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Automating yellow rust disease identification in wheat using artificial intelligence: Authority (A:79), Popularity (P:54), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology

Open data Updated: Live data

📝 Executive Summary

"Plant disease has long been one of the major threats to world food security due to reduction in the crop yield and quality. Accurate and precise diagnosis of plant diseases has been a significant challenge. Cost-effective automated computational systems for disease diagnosis would facilitate advancements in agriculture. The objective of this paper is to explore computer vision based Artificial Intelligence method for automating the identification of yellow rust disease and improve the accurac..."

❝ Cite Node

@article{Unknown2026Automating,
  title={Automating yellow rust disease identification in wheat using artificial intelligence},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

🔗 Full Paper

Free2AITools indexes the abstract and factual metadata for this paper. Read the complete, authoritative paper on the official source.

Read the full paper on arXiv

📊 Research Signals

📈17CitationsSemantic Scholar

🏛️79AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

🗂️automation workflowField

🏷️ Research Topics

vision models

📦Data Source: semantic_scholar

🔄 Updated daily

Source summary: Based on semantic_scholar metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹ️ Verify with original source

🛡️ Paper Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag

📊 Engagement & Metrics

downloads: 0
stars: null
forks: null
citations: 17

Data indexed from public sources. Updated daily.