Evidence map›Paper›PMID 40851828›Full record

ArticleFrontiers in plant science2025

CropPhenoX: high-throughput automatic extraction system for wheat seedling phenotypic traits based on software and hardware collaboration.

Jinxing Wang, Baohua Yang, Pengfei Wang, Runchao Chen, Hongbo Zhi, Zhiyuan Duan

Abstract read
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Article in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Jinxing WangSchool of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, China.
Baohua YangSchool of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, China.
Pengfei WangSchool of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, China.
Runchao ChenSchool of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, China.
Hongbo ZhiSchool of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, China.
Zhiyuan DuanSchool of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurately quantifying wheat seedling phenotypic traits is crucial for genetic breeding and the development of smart agriculture. However, existing phenotypic extraction methods are difficult to meet the needs of high-throughput and high-precision detection in complex scenarios. To this end, this paper proposes a high-throughput automated extraction system for wheat seedling phenotypic traits based on software and hardware collaboration, CropPhenoX. In terms of hardware, an architecture integrating Siemens programmable logic controller (PLC) modules is constructed to realize intelligent scheduling of crop transportation. The stability and efficiency of data acquisition are guaranteed by coordinating and controlling lighting equipment, cameras, and photoelectric switches. Modbus transmission control protocol (TCP) is used to achieve real-time data interaction and remote monitoring. In terms of software, the Wheat-RYNet model for wheat seedling detection is proposed, which combines the detection efficiency of YOLOv5, the lightweight architecture of MobileOne, and the efficient channel attention mechanism (ECA). By designing an adaptive rotation frame detection method, the challenges brought by leaf overlap and tilt are effectively overcome. In addition, a phenotypic trait extraction platform is developed to collect high-definition images in real time. The Wheat-RYNet model was used to extract wheat seedling phenotypic traits, such as leaf length, leaf width, leaf area, plant height, leaf inclination, etc. Compared with the actual measured values, the average fitting determination coefficient reached 0.9. The test results show that CropPhenoX provides an intelligent integrated solution for crop phenotyping research, breeding analysis and field management.

Indexed as

CropPhenoXhighthroughput detectionphenotypic traitsoftware-hardware collaborationwheat

Identifiers

PMID40851828
PMCPMC12367779

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.