Correction: Lin et al. Research on Machine Learning Models for Maize Hardness Prediction Based on Indentation Test. Agriculture 2024, 14, 224

Lin, Haipeng; Song, Xuefeng; Dai, Fei; Zhang, Fengwei; Xie, Qiang; Chen, Huhu

doi:10.3390/agriculture15151659

Open AccessCorrection

Correction: Lin et al. Research on Machine Learning Models for Maize Hardness Prediction Based on Indentation Test. Agriculture 2024, 14, 224

by

Haipeng Lin

,

Xuefeng Song

^*

,

Fei Dai

,

Fengwei Zhang

,

Qiang Xie

and

Huhu Chen

College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China

^*

Author to whom correspondence should be addressed.

Agriculture 2025, 15(15), 1659; https://doi.org/10.3390/agriculture15151659

Submission received: 20 June 2025 / Accepted: 24 June 2025 / Published: 1 August 2025

(This article belongs to the Special Issue Agricultural Products Processing and Quality Detection)

Download Versions Notes

Error in Figure

In the original publication [1], there was a mistake in Figure 16 caption. Endosperm microstructure of maize grains before and after loading with different types of indenters. The shape of the hole in Figure 16d is the same as in Figure 16e; the difference lies in the magnification factor. The former is magnified 150 times, while the latter is magnified 85 times. Additionally, the shape of the hole in Figure 16f was generated using a quadrangular indenter, not a spherical steel needle indenter. The shape of the hole in Figure 16d was generated using a spherical indenter. The corrected Figure 16 caption appears below. The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.

Figure 16. Endosperm microstructure of maize grains before and after loading with different types of indenters. (a) Untreated seeds, magnified 1500 times; (b) A 30° conical indenter, magnified 320 times; (c) A 30° triangular indenter, magnified 130 times; (d) A 30° spherical steel needle indenter, magnified 150 times; (e) A 30° spherical steel needle indenter, magnified 85 times; and (f) A 30° quadrangular indenter, magnified 81 times. Note: Figure (d,e) are the same image. In order to showcase the changes in surface structure, the magnification of the two images is different.

Reference

Lin, H.; Song, X.; Dai, F.; Zhang, F.; Xie, Q.; Chen, H. Research on Machine Learning Models for Maize Hardness Prediction Based on Indentation Test. Agriculture 2024, 14, 224. [Google Scholar] [CrossRef]

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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

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MDPI and ACS Style

Lin, H.; Song, X.; Dai, F.; Zhang, F.; Xie, Q.; Chen, H. Correction: Lin et al. Research on Machine Learning Models for Maize Hardness Prediction Based on Indentation Test. Agriculture 2024, 14, 224. Agriculture 2025, 15, 1659. https://doi.org/10.3390/agriculture15151659

AMA Style

Lin H, Song X, Dai F, Zhang F, Xie Q, Chen H. Correction: Lin et al. Research on Machine Learning Models for Maize Hardness Prediction Based on Indentation Test. Agriculture 2024, 14, 224. Agriculture. 2025; 15(15):1659. https://doi.org/10.3390/agriculture15151659

Chicago/Turabian Style

Lin, Haipeng, Xuefeng Song, Fei Dai, Fengwei Zhang, Qiang Xie, and Huhu Chen. 2025. "Correction: Lin et al. Research on Machine Learning Models for Maize Hardness Prediction Based on Indentation Test. Agriculture 2024, 14, 224" Agriculture 15, no. 15: 1659. https://doi.org/10.3390/agriculture15151659

APA Style

Lin, H., Song, X., Dai, F., Zhang, F., Xie, Q., & Chen, H. (2025). Correction: Lin et al. Research on Machine Learning Models for Maize Hardness Prediction Based on Indentation Test. Agriculture 2024, 14, 224. Agriculture, 15(15), 1659. https://doi.org/10.3390/agriculture15151659

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Correction: Lin et al. Research on Machine Learning Models for Maize Hardness Prediction Based on Indentation Test. Agriculture 2024, 14, 224

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