ArticleScientific reports2024
A CNN-based model to count the leaves of rosette plants (LC-Net).
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Functional division of the prefrontal lobe of the brain: coordinate-based neuroimaging meta-analysis.Physical and engineering sciences in medicine · 2025Pooled it
- Band level similarity reduction and deep dual signal fusion for reliable diagnosis of power insulator defects.Scientific reports · 2025Article
- A lightweight large receptive field network LrfSR for image super-resolution.Scientific reports · 2025Article
- A high-throughput ResNet CNN approach for automated grapevine leaf hair quantification.Scientific reports · 2025Article
- A User-Friendly Machine Learning Pipeline for Automated Leaf Segmentation inBioinformatics and biology insights · 2025Article
- A novel groundnut leaf dataset for detection and classification of groundnut leaf diseases.Data in brief · 2024Article
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plant image analysis is a significant tool for plant phenotyping. Image analysis has been used to assess plant trails, forecast plant growth, and offer geographical information about images. The area segmentation and counting of the leaf is a major component of plant phenotyping, which can be used to measure the growth of the plant. Therefore, this paper developed a convolutional neural network-based leaf counting model called LC-Net. The original plant image and segmented leaf parts are fed as input because the segmented leaf part provides additional information to the proposed LC-Net. The well-known SegNet model has been utilised to obtain segmented leaf parts because it outperforms four other popular Convolutional Neural Network (CNN) models, namely DeepLab V3+, Fast FCN with Pyramid Scene Parsing (PSP), U-Net, and Refine Net. The proposed LC-Net is compared to the other recent CNN-based leaf counting models over the combined Computer Vision Problems in Plant Phenotyping (CVPPP) and KOMATSUNA datasets. The subjective and numerical evaluations of the experimental results demonstrate the superiority of the LC-Net to other tested models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.