ArticlePlant phenomics (Washington, D.C.)2023
CountShoots: Automatic Detection and Counting of Slash Pine New Shoots Using UAV Imagery.
Article in Plant phenomics (Washington, D.C.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- High-resolution UAV imagery-based instance segmentation for automatic counting of rubber trees across phenological seasons.Plant phenomics (Washington, D.C.) · 2026Article
- Low-annotation apple flower counting: A color-SAM enhanced and uncertainty-guided semi-supervised framework.Plant phenomics (Washington, D.C.) · 2026Article
- Genetic constraints on in-situ reflectance spectral variation in bermudagrass populations across Hainan Island.Plant phenomics (Washington, D.C.) · 2026Article
- Development and Field Validation of a Smartphone-Based Web Application for Diagnosing Optimal Timing of Mid-Season Drainage in Rice Cultivation via Canopy Image-Derived Tiller Estimation.Sensors (Basel, Switzerland) · 2026Article
- Volumetric Deep Learning-Based Precision Phenotyping of Gene-Edited Tomato for Vertical Farming.Plant phenomics (Washington, D.C.) · 2025Article
- AgCNER, the First Large-Scale Chinese Named Entity Recognition Dataset for Agricultural Diseases and Pests.Scientific data · 2024Article
- MTSC-Net: A Semi-Supervised Counting Network for Estimating the Number of Slash pine New Shoots.Plant phenomics (Washington, D.C.) · 2024Article
- One to All: Toward a Unified Model for Counting Cereal Crop Heads Based on Few-Shot Learning.Plant phenomics (Washington, D.C.) · 2024Article
- Semi-supervised Counting of Grape Berries in the Field Based on Density Mutual Exclusion.Plant phenomics (Washington, D.C.) · 2023Article
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Authors and funding
8 authors.
Funding
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Abstract
The density of new shoots on pine trees is an important indicator of their growth and photosynthetic capacity. However, traditional methods to monitor new shoot density rely on manual and destructive measurements, which are labor-intensive and have led to fewer studies on new shoot density. Therefore, in this study, we present user-friendly software called CountShoots, which extracts new shoot density in an easy and convenient way using unmanned aerial vehicles based on the YOLOX and Slash Pine Shoot Counting Network (SPSC-net) models. This software mainly consists of 2 steps. Firstly, we deployed a modified YOLOX model to identify the tree species and location from complex RGB background images, which yielded a high recognition accuracy of 99.15% and 95.47%. These results showed that our model produced higher detection accuracy compared to YOLOv5, Efficientnet, and Faster-RCNN models. Secondly, we constructed an SPSC-net. This methodology is based on the CCTrans network, which outperformed DM-Count, CSR-net, and MCNN models, with the lowest mean squared error and mean absolute error results among other models (i.e., 2.18 and 1.47, respectively). To our best knowledge, our work is the first research contribution to identify tree crowns and count new shoots automatically in slash pine. Our research outcome provides a highly efficient and rapid user-interactive pine tree new shoot detection and counting system for tree breeding and genetic use purposes.
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