Trunk diameter is related to the overall health and level of carbon sequestration in a tree. Trunk diameter measurement, therefore, is a key task in both forest plot and urban settings. Unlike the traditional approach of manual measurement with a measuring tape or calipers, several recent approaches rely on sophisticated technologies such as Terrestrial Laser Scanning (TLS), LiDAR, and time-of-flight sensors that provide fine-grain depth maps, which are used for depth-assisted image segmentation in downstream processing. These technologies are supported only on specialized devices or high-end smartphones. We present a mobile application that uses coarse-grain depth maps derived from an optical sensor, and so can be run on most common Android devices. Moreover, we use a state-of-the-art deep neural network to estimate trunk diameter from an image and its corresponding coarse depth map (RGB-D). We tested our app using a dataset collected from four countries and under challenging conditions including occlusion, leaning trees, and irregular shapes and found that our algorithm has a MAE of 2.58 cm and an RMSE of 3.57 cm, which is comparable to accuracy from fine-grain depth maps. Moreover, diameter measurement using our app is more than 5 times faster than traditional manual surveying.
Why it matches plant phenotyping methodsRGB-D画像と粗い深度マップから樹幹直径を推定するモバイル手法を開発し、複数国のデータセットと困難条件で精度検証しており、植物形質取得が中心である。
abstractWe present a mobile application that uses coarse-grain depth maps derived from an optical sensor
Reproduction assets foundThe paper's DBH estimation evaluation dataset (154 RGB + depth tree images with metadata and ground-truth DBH) is publicly deposited on Zenodo, and the app/algorithm source code is publicly available on GitHub. Both are paper-specific, public, and actionable.Dataset · publicing the quality of the DBH estimate, includ-
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ing non-cylindrical trunks, burl presence, degrees of leaning and occlusion, and poor lighting, as illustrated in Fig. 4. Sample images
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from the dataset are available in Supplement 10.9, and the complete set of RGB and depth images, along with metadata, is acces-
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sible at https://zenodo.org/records/10199711.200
In Thailand, we collected data in Bangkok’s Lumphini Park and Chulalongkorn Centenary Park. As a tropical location, Bangkok is
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home to many tropical trees, such as rain trees (Samanea saman), banyan trees, palm trees, and coconut trees52. At Lumphini Park,
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where most of our data came from, small forests grow next to watOpen asset ↗zenodo.org · 10199711pdf-raw-page:8 lines:1-31Code · public; Z.F. analyzed the data and led the writing of the manuscript.
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A.H. and S.K. reviewed the manuscript and provided constructive suggestions. All authors contributed critically to the drafts and
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gave final approval for publication.
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8 DATA AVAILABILITY
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The algorithm and app code are publicly available on GitHub at https://github.com/MingyueX/GreenLens, with APK available from
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APKPure at https://apkpure.com/p/com.cleeg.greenlens. All the data for the app evaluation can be accessed at https://zenodo.org/357
records/10199711.
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9 AUTHOR COMPETING INTERESTS
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The authors declare no conflict of interest.
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REFERENCES
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[1] Kenneth G MacDicken. Global forest resourOpen asset ↗github.com/MingyueX/GreenLenspdf-raw-page:15 lines:1-92Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Measuring lycopene in tomatoes is fundamental to the agrifood industry because of its health benefits. It is one of the leading quality criteria for consuming this fruit. Traditionally, the amount determination of this carotenoid is performed using the high-performance liquid chromatography (HPLC) technique. This is a very reliable and accurate method, but it has several disadvantages, such as long analysis time, high cost, and destruction of the sample. In this sense, this work proposes a low-cost sensor that correlates the lycopene content in tomato with the color present in its epicarp. A Raspberry Pi 4 programmed with Python language was used to develop the lycopene prediction model. Various regression models were evaluated using neural networks, fuzzy logic, and linear regression. The best model was the fuzzy nonlinear regression as the RGB input, with a correlation of R 2 = 0.99 and a mean error of 1.9 × 10 -5 . This work was able to demonstrate that it is possible to determine the lycopene content using a digital camera and a low-cost integrated system in a non-invasive way.
Why it matches plant phenotyping methodsトマト果皮画像の色からリコペン含量を非破壊推定する低コストセンサーと予測モデルの開発が中心であり、植物器官の形質測定法に該当する。
abstractthis work proposes a low-cost sensor that correlates the lycopene content in tomato with the color present in its epicarp.
Reproduction assets foundThe paper's Data Availability Statement links to a public Google Drive folder containing the data supporting the reported lycopene measurement results (tomato RGB/L*a*b* image-derived measurements and HPLC-calibrated model data). No separate code deposit is described; the models were built in MATLAB toolboxes without aDataset · publicl analysis, M.-G.B.-S.; investigation, J.-A.P.-M.; writing—original draft preparation, J.P.-O. and M.-J.V.-A.; writing—review and editing, A.-I.B.-G.; supervision, A.-I.B.-G. All authors have read and agreed to the published version of the manuscript.
Data Availability Statement
Data supporting reported results can be found at: https://drive.google.com/drive/folders/1d1Q_RtEWmo2lbpipMCNG4x53s09-pB-C?usp=sharing .
Conflicts of Interest
The authors declare no conflict of interest.
Appendix A
Listed below are the 18 inference rules and weights for each of the two fuzzy systems red, green, and blue:
If (L is Low_L) and (a is Low_a) and (b is Low_b) then (Lycopene is Lycopenemf1)
If (L is Low_L) Open asset ↗lines:75-128Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methods植物葉の画像を対象に、適応的セグメンテーションとResNet/LSTM-DNNによる多病害分類モデルを開発する研究であり、葉の病害状態を画像から推定する手法が中心と判断できる。
titleAdaptive Segmentation with Intelligent ResNet and LSTM–DNN for Plant Leaf Multi-disease Classification Model
Reproduction assets foundThe paper's leaf-disease classification experiments use the publicly available PlantifyDR Kaggle dataset (~87k RGB leaf images, 37 classes) as its input image data. No author code, models, or supplementary deposits are mentioned.Dataset · publicbability of
beggars and producers in terms of searching for food. The pseudo-code of the sug-
gested HBM-BSO is given here, Algorithm 1.
4.2 Description of Datasets
The developed multi-disease plant leaf classification model gathered the images
from standard online sources. The selected input images are obtained from the
link “https://www.kaggle.com/datasets/lavaman151/plantifydr-dataset: access date:
2022-05-02”. Here, sample images are collected from the dataset kaggle, whereas
the original dataset is collected from the GitHub repo. This dataset holds nearly 87 k
rgb healthy and non-healthy images of plant leaves, and it is classified into 37 varie-
ties of classes. The whole dataset is sOpen asset ↗kaggle · lavaman151/plantifydr-datasetpdf-raw-page:21 lines:1-29Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Multicellular organisms result from complex developmental processes largely orchestrated through the quantitative spatiotemporal regulation of gene expression. Yet, obtaining absolute counts of messenger RNAs at a three-dimensional resolution remains challenging, especially in plants, owing to high levels of tissue autofluorescence that prevent the detection of diffraction-limited fluorescent spots. In situ hybridization methods based on amplification cycles have recently emerged, but they are laborious and often lead to quantification biases. In this article, we present a simple method based on single-molecule RNA fluorescence in situ hybridization to visualize and count the number of mRNA molecules in several intact plant tissues. In addition, with the use of fluorescent protein reporters, our method also enables simultaneous detection of mRNA and protein quantity, as well as subcellular distribution, in single cells. With this method, research in plants can now fully explore the benefits of the quantitative analysis of transcription and protein levels at cellular and subcellular resolution in plant tissues.
Why it matches plant phenotyping methods植物組織内のmRNA・タンパク質量を細胞および細胞内解像度で可視化・定量するsmFISH法の開発であり、植物の状態を測定する方法が中心的です。
abstractIn this article, we present a simple method based on single-molecule RNA fluorescence in situ hybridization to visualize and count the number of mRNA molecules in several intact plant tissues.
Reproduction assets foundThe authors openly deposited all raw microscopy images (WM-smFISH mRNA/protein imaging of Arabidopsis and barley tissues) used for their quantification pipeline on Figshare. No separate author analysis code repository with explicit availability language is stated in the supplied text.Dataset · publicAll the raw microscopy images used in this manuscript are openly available in Figshare at https://doi.org/10.6084/m9.figshare.22699132 .Open asset ↗Figshare · 10.6084/m9.figshare.22699132lines:110-216