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Agriculture
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Single Leaf Area Models and Leaf Area Index in Agriculture...
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Single Leaf Area Models and Leaf Area Index in Agriculture and Its Related Ecosystems

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: 5 March 2025 | Viewed by 1376

Special Issue Editor

Dr. Kohei Koyama

E-Mail Website
Guest Editor
Hokkaido University of Education, Asahikawa Campus, Hokumoncho 9, Asahikawa, Japan
Interests: photosynthesis; transpiration; respiration; leaf area; stem physiology; ecophysiology

Special Issue Information

Dear Colleagues,

Crop production is determined by whole-plant leaf area, while an ecosystem’s total leaf area determines its carbon exchange rate. Determination of leaf area is, therefore, one of the most important topics in agronomy, plant ecology, and remote sensing.

This Special Issue focuses on developing and assessing methods for determining and modeling the leaf area at any scale, including single-leaf area, whole-plant leaf area, stand leaf area, and leaf area index (LAI). It will include field and laboratory measurements, mathematical modeling, image analysis, phenotyping, 3D/three-dimensional characterizations, and remote sensing of leaf area or LAI. Research articles will cover various plant species related to farmlands and their surrounding ecosystems (e.g., agroforestry, satoyama, etc.). Target plant species include crops, weeds, ornamental and medicinal plants, fruit trees, plants for natural materials (e.g., Gossypium, Salix), and bamboo. Original research articles, reviews, and opinion pieces, are welcome. We also welcome technical reports such as articles, communications (i.e., short communications), technical notes, or brief reports (depending on the length and type of the manuscript).

Dr. Kohei Koyama
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. Agriculture is an international peer-reviewed open access semimonthly 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

  • leaf area
  • leaf area index
  • image analysis
  • remote sensing
  • Montgomery equation
  • leaf length-times-width equation
  • foliage length-times-width equation
  • mathematical modeling
  • remote sensing
  • allometry

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Published Papers (2 papers)

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Research

17 pages, 2041 KiB  
Article
LEAF-Net: A Unified Framework for Leaf Extraction and Analysis in Multi-Crop Phenotyping Using YOLOv11
by Ameer Tamoor Khan and Signe Marie Jensen
Agriculture 2025, 15(2), 196; https://doi.org/10.3390/agriculture15020196 - 17 Jan 2025
Viewed by 437
Abstract
Accurate leaf segmentation and counting are critical for advancing crop phenotyping and improving breeding programs in agriculture. This study evaluates YOLOv11-based models for automated leaf detection and segmentation across spring barley, spring wheat, winter wheat, winter rye, and winter triticale. The key focus [...] Read more.
Accurate leaf segmentation and counting are critical for advancing crop phenotyping and improving breeding programs in agriculture. This study evaluates YOLOv11-based models for automated leaf detection and segmentation across spring barley, spring wheat, winter wheat, winter rye, and winter triticale. The key focus is assessing whether a unified model trained on a combined multi-crop dataset can outperform crop-specific models. Results show that the unified model achieves superior performance in bounding box tasks, with mAP@50 exceeding 0.85 for spring crops and 0.7 for winter crops. Segmentation tasks, however, reveal mixed results, with individual models occasionally excelling in recall for winter crops. These findings highlight the benefits of dataset diversity in improving generalization, while emphasizing the need for larger annotated datasets to address variability in real-world conditions. While the combined dataset improves generalization, the unique characteristics of individual crops may still benefit from specialized training. Full article
(This article belongs to the Special Issue Single Leaf Area Models and Leaf Area Index in Agriculture and Its Related Ecosystems)
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18 pages, 5691 KiB  
Article
The Inversion of Rice Leaf Pigment Content: Using the Absorption Spectrum to Optimize the Vegetation Index
by Longfei Ma, Yuanjin Li, Ningge Yuan, Xiaojuan Liu, Yuyan Yan, Chaoran Zhang, Shenghui Fang and Yan Gong
Agriculture 2024, 14(12), 2265; https://doi.org/10.3390/agriculture14122265 - 11 Dec 2024
Viewed by 683
Abstract
The pigment content of rice leaves plays an important role in the growth and development of rice. The accurate and rapid assessment of the pigment content of leaves is of great significance for monitoring the growth status of rice. This study used the [...] Read more.
The pigment content of rice leaves plays an important role in the growth and development of rice. The accurate and rapid assessment of the pigment content of leaves is of great significance for monitoring the growth status of rice. This study used the Analytical Spectra Device (ASD) FieldSpec 4 spectrometer to measure the leaf reflectance spectra of 4 rice varieties during the entire growth period under 4 nitrogen application rates and simultaneously measured the leaf pigment content. The leaf’s absorption spectra were calculated based on the physical process of spectral transmission. An examination was conducted on the variations in pigment composition among distinct rice cultivars, alongside a thorough dissection of the interrelations and distinctions between leaf reflectance spectra and absorption spectra. Based on the vegetation index proposed by previous researchers in order to invert pigment content, the absorption spectrum was used to replace the original reflectance data to optimize the vegetation index. The results showed that the chlorophyll and carotenoid contents of different rice varieties showed regular changes during the whole growth period, and that the leaf absorption spectra of different rice varieties showed more obvious differences than reflectance spectra. After replacing the reflectance of pigment absorptivity-sensitive bands (400 nm, 550 nm, 680 nm, and red-edge bands) with absorptivities that would optimize the vegetation index, the correlation between the vegetation index, which combines absorptivity and reflectivity, and the chlorophyll and carotenoid contents of 4 rice varieties during the whole growth period was significantly improved. The model’s validation results indicate that the pigment inversion model, based on the improved vegetation index using absorption spectra, outperforms the traditional vegetation index-based pigment inversion model. The results of this study demonstrate the potential application of absorption spectroscopy in the quantitative inversion of crop phenotypes. Full article
(This article belongs to the Special Issue Single Leaf Area Models and Leaf Area Index in Agriculture and Its Related Ecosystems)
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