Crop Phenotyping Based on Artificial Intelligence Methods

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Plant Science".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 1179

Special Issue Editor


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Guest Editor
College of Arts and Sciences, Northeast Agricultural University, Harbin 150030, China
Interests: crop phenotyping; bioinformatics; deep learning; image processing

Special Issue Information

Dear Colleagues,

Crop phenomics, as one of the core key technologies in the development process from traditional agriculture to smart agriculture, has been increasingly emphasized by the majority of agricultural scientists. With the rapid development of artificial intelligence technology represented by deep learning, the development of crop phenomics technology has been greatly promoted. This Special Issue, titled "Crop Phenotyping Based on Artificial Intelligence Methods", will focus on the solution of new problems in crop phenomics, the proposal of new methods and technologies for phenotyping, and the release of new platforms for phenotyping and analyzing driven by AI technology. This Special Issue will focus on the above topics, but its content is not limited to these topics, as the study of the pattern of change of crop phenotypes, the genetic analysis of phenotypic changes, and the study of functional phenotypes of crops, etc., will also be covered.

Dr. Rongsheng Zhu
Guest Editor

Manuscript Submission Information

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Keywords

  • crop phenomics
  • image processing
  • deep learning
  • machine learning
  • agronomy traits
  • UAV
  • growth and development period
  • biotic and abiotic stresses
  • IoT
  • physiological phenotype

Published Papers (1 paper)

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Research

12 pages, 2035 KiB  
Article
Rapid Detection of Tannin Content in Wine Grapes Using Hyperspectral Technology
by Peng Zhang, Qiang Wu, Yanhan Wang, Yun Huang, Min Xie and Li Fan
Life 2024, 14(3), 416; https://doi.org/10.3390/life14030416 - 21 Mar 2024
Viewed by 677
Abstract
Wine grape quality is influenced by the variety and growing environment, and the quality of the grapes has a significant impact on the quality of the wine. Tannins are a crucial indicator of wine grape quality, and, therefore, rapid and non-destructive methods for [...] Read more.
Wine grape quality is influenced by the variety and growing environment, and the quality of the grapes has a significant impact on the quality of the wine. Tannins are a crucial indicator of wine grape quality, and, therefore, rapid and non-destructive methods for detecting tannin content are necessary. This study collected spectral data of Pinot Noir and Chardonnay using a geophysical spectrometer, with a focus on the 500–1800 nm spectrum. The spectra were preprocessed using Savitzky–Golay (SG), first-order differential (1D), standard normal transform (SNV), and their respective combinations. Characteristic bands were extracted through correlation analysis (PCC). Models such as partial least squares (PLS), support vector machine (SVM), random forest (RF), and one-dimensional neural network (1DCNN) were used to model tannin content. The study found that preprocessing the raw spectra improved the models’ predictive capacity. The SVM–RF model was the most effective in predicting grape tannin content, with a test set R2 of 0.78, an RMSE of 0.31, and an RE of 10.71%. These results provide a theoretical basis for non-destructive testing of wine grape tannin content. Full article
(This article belongs to the Special Issue Crop Phenotyping Based on Artificial Intelligence Methods)
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