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Performance Analysis of YOLO and Detectron2 Models for Detecting Corn and Soybean Pests Employing Customized Dataset paper by Independent / Community
⭐ 65.1
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Paper

Performance Analysis of YOLO and Detectron2 Models for Detecting Corn and Soybean Pests Employing Customized Dataset

by Independent / Community 00d83c7c41dc6c40ff6cd444dd4621fdbf4379a7
Free2AITools Nexus Index
65.1
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 75
P: Popularity 50
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

One of the most challenging aspects of agricultural pest control is accurate detection of insects in crops. Inadequate control measures for insect pests can seriously impact the production of corn and soybean plantations. In recent years, artificial intelligence (AI) algorithms have been extensively used for detecting insect pests in the field. In this line of research, this paper introduces a method to detect four key insect species that are predominant in Brazilian agriculture. Our model re...

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Semantic Scholar 8 Citations
Paper Information Summary
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Registry ID 00d83c7c41dc6c40ff6cd444dd4621fdbf4379a7
License ArXiv
Provider semantic_scholar
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Academic & Research Attribution

BibTeX 
@misc{00d83c7c41dc6c40ff6cd444dd4621fdbf4379a7,
  author = {Unknown},
  title = {Performance Analysis of YOLO and Detectron2 Models for Detecting Corn and Soybean Pests Employing Customized Dataset Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/00d83c7c41dc6c40ff6cd444dd4621fdbf4379a7}},
  note = {Accessed via Free2AITools.}
}
APA Style
Unknown. (2026). Performance Analysis of YOLO and Detectron2 Models for Detecting Corn and Soybean Pests Employing Customized Dataset [Paper]. Free2AITools. https://api.semanticscholar.org/00d83c7c41dc6c40ff6cd444dd4621fdbf4379a7

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βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 75
Popularity (P) 50
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Performance Analysis of YOLO and Detectron2 Models for Detecting Corn and Soybean Pests Employing Customized Dataset: Authority (A:75), Popularity (P:50), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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πŸ“ Executive Summary

"One of the most challenging aspects of agricultural pest control is accurate detection of insects in crops. Inadequate control measures for insect pests can seriously impact the production of corn and soybean plantations. In recent years, artificial intelligence (AI) algorithms have been extensively used for detecting insect pests in the field. In this line of research, this paper introduces a method to detect four key insect species that are predominant in Brazilian agriculture. Our model re..."

❝ Cite Node 

@article{Unknown2026Performance,
  title={Performance Analysis of YOLO and Detectron2 Models for Detecting Corn and Soybean Pests Employing Customized Dataset},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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πŸ“Š Research Signals

πŸ“ˆ8CitationsSemantic Scholar
πŸ›οΈ75AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
πŸ—‚οΈvision multimediaField
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source
semantic_scholar
author
Unknown
license
ArXiv
tags
paper, research, academic

βš™οΈ Technical Specs

architecture
null
params billions
null
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null
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