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Life
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Crop Phenotyping Based on Artificial Intelligence Methods
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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 3285

Special Issue Editor

Dr. Rongsheng Zhu

E-Mail Website
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Life is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

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Published Papers (2 papers)

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Research

14 pages, 4284 KiB  
Article
Leaf Segmentation Using Modified YOLOv8-Seg Models
by Peng Wang, Hong Deng, Jiaxu Guo, Siqi Ji, Dan Meng, Jun Bao and Peng Zuo
Life 2024, 14(6), 780; https://doi.org/10.3390/life14060780 - 20 Jun 2024
Viewed by 1238
Abstract
Computer-vision-based plant leaf segmentation technology is of great significance for plant classification, monitoring of plant growth, precision agriculture, and other scientific research. In this paper, the YOLOv8-seg model was used for the automated segmentation of individual leaves in images. In order to improve [...] Read more.
Computer-vision-based plant leaf segmentation technology is of great significance for plant classification, monitoring of plant growth, precision agriculture, and other scientific research. In this paper, the YOLOv8-seg model was used for the automated segmentation of individual leaves in images. In order to improve the segmentation performance, we further introduced a Ghost module and a Bidirectional Feature Pyramid Network (BiFPN) module into the standard Yolov8 model and proposed two modified versions. The Ghost module can generate several intrinsic feature maps with cheap transformation operations, and the BiFPN module can fuse multi-scale features to improve the segmentation performance of small leaves. The experiment results show that Yolov8 performs well in the leaf segmentation task, and the Ghost module and BiFPN module can further improve the performance. Our proposed approach achieves a 86.4% leaf segmentation score (best Dice) over all five test datasets of the Computer Vision Problems in Plant Phenotyping (CVPPP) Leaf Segmentation Challenge, outperforming other reported approaches. Full article
(This article belongs to the Special Issue Crop Phenotyping Based on Artificial Intelligence Methods)
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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
Cited by 1 | Viewed by 1343
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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