Kidney bean detection in complex agricultural scenarios based on the kidneyB-YOLO model

The enhancements improved the accuracy and robustness of bean pod detection. The model demonstrated strong robustness and generalization capability in the fruit detection task for kidney bean harvesting under open-air trellises.

Authors

Hyosub Chu (ORCID: https://orcid.org/0000-0002-8536-7500)
Jiaguo Qi (ORCID: https://orcid.org/0000-0002-8183-0297)
Chunxiang Liu

Institutions

Heilongjiang Institute of Technology (CN)

Publication Details

Journal: Frontiers in Plant Science
Published: 2025-12-12
DOI: https://doi.org/10.3389/fpls.2025.1672179
Primary Topic: Smart Agriculture and AI
Type: article
Field-Weighted Citation Impact: 0.00

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article

Kidney bean detection in complex agricultural scenarios based on the kidneyB-YOLO model

Hyosub Chu, Jiaguo Qi, Chunxiang Liu

Dec 12, 2025

Frontiers in Plant Science

Smart Agriculture and AI

article

Kidney bean detection in complex agricultural scenarios based on the kidneyB-YOLO model

Hyosub Chu, Jiaguo Qi, Chunxiang Liu

December 12, 2025article en

Abstract

Frontiers in Plant ScienceVol. 16

Heilongjiang Institute of Technology (CN)

Openalex Percentile: Top 71%

Smart Agriculture and AI

DOI Open Access PDF OpenAlex

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Kidney bean detection in complex agricultural scenarios based on the kidneyB-YOLO model — Hyosub Chu, Jiaguo Qi, et al. · Frontiers in Plant Science (2025) | TGRS Research Map | TGRS