ArticlePlant phenomics (Washington, D.C.)2023
Rice Plant Counting, Locating, and Sizing Method Based on High-Throughput UAV RGB Images.
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 20 papers.
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20 citing papers in PubMed.
- Article
- Physiology-informed high-throughput phenotyping of grain moisture dynamics provides enhanced insights into rice grain weight formation.Plant physiology · 2026Article
- Global rice multiclass segmentation dataset (RiceSEG): comprehensive and diverse high-resolution RGB-annotated images for the development and benchmarking of rice segmentation algorithms.Plant phenomics (Washington, D.C.) · 2025Article
- RGB imaging and computer vision-based approaches for identifying spike number loci for wheat.Plant phenomics (Washington, D.C.) · 2025Article
- Integrating UAV remote sensing and semi-supervised learning for early-stage maize seedling monitoring and geolocation.Plant phenomics (Washington, D.C.) · 2025Article
- Plant Detection in RGB Images from Unmanned Aerial Vehicles Using Segmentation by Deep Learning and an Impact of Model Accuracy on Downstream Analysis.Journal of imaging · 2025Article
- DUNet: a novel dehazing model based on outdoor images.Frontiers in plant science · 2025Article
- Article
- Field-deployable lightweight YOLOv8n for real-time detection and counting of Maize seedlings using UAV RGB imagery.Frontiers in plant science · 2025Article
- Automatic detection and counting of wheat seedling based on unmanned aerial vehicle images.Frontiers in plant science · 2025Article
- DSCONV-GAN: a UAV-BASED model for Verticillium Wilt disease detection in Chinese cabbage in complex growing environments.Plant methods · 2024Article
- RiGaD: An aerial dataset of rice seedlings for assessing germination rates and density.Data in brief · 2024Article
- OPIA: an open archive of plant images and related phenotypic traits.Nucleic acids research · 2024Article
- UAV-Assisted Dynamic Monitoring of Wheat Uniformity toward Yield and Biomass Estimation.Plant phenomics (Washington, D.C.) · 2024Article
- Counting Canola: Toward Generalizable Aerial Plant Detection Models.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
- Segmentation and Phenotype Calculation of Rapeseed Pods Based on YOLO v8 and Mask R-Convolution Neural Networks.Plants (Basel, Switzerland) · 2023Article
- An Ensemble Learning Model for Detecting Soybean Seedling Emergence in UAV Imagery.Sensors (Basel, Switzerland) · 2023Article
- Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals.Frontiers in plant science · 2023Review
- 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
9 authors.
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Abstract
Rice plant counting is crucial for many applications in rice production, such as yield estimation, growth diagnosis, disaster loss assessment, etc. Currently, rice counting still heavily relies on tedious and time-consuming manual operation. To alleviate the workload of rice counting, we employed an UAV (unmanned aerial vehicle) to collect the RGB images of the paddy field. Then, we proposed a new rice plant counting, locating, and sizing method (RiceNet), which consists of one feature extractor frontend and 3 feature decoder modules, namely, density map estimator, plant location detector, and plant size estimator. In RiceNet, rice plant attention mechanism and positive-negative loss are designed to improve the ability to distinguish plants from background and the quality of the estimated density maps. To verify the validity of our method, we propose a new UAV-based rice counting dataset, which contains 355 images and 257,793 manual labeled points. Experiment results show that the mean absolute error and root mean square error of the proposed RiceNet are 8.6 and 11.2, respectively. Moreover, we validated the performance of our method with two other popular crop datasets. On these three datasets, our method significantly outperforms state-of-the-art methods. Results suggest that RiceNet can accurately and efficiently estimate the number of rice plants and replace the traditional manual method.
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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.