ArticleSensors (Basel, Switzerland)2019
Automated Counting of Rice Panicle by Applying Deep Learning Model to Images from Unmanned Aerial Vehicle Platform.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 62 citations in OpenAlex.
- High-Throughput Phenotyping: Status and Applications in Rice Breeding.Plants (Basel, Switzerland) · 2026Review
- Improved Multi-Stage Rice Above-Ground Biomass Estimation Using Wavelet-Texture-Fused Vegetation Indices from UAV Remote Sensing.Plants (Basel, Switzerland) · 2025Article
- OE-YOLO: An EfficientNet-Based YOLO Network for Rice Panicle Detection.Plants (Basel, Switzerland) · 2025Article
- High-throughput UAV-based rice panicle detection and genetic mapping of heading-date-related traits.Frontiers in plant science · 2024Article
- Research and Implementation of Millet Ear Detection Method Based on Lightweight YOLOv5.Sensors (Basel, Switzerland) · 2023Article
- Analyzing Nitrogen Effects on Rice Panicle Development by Panicle Detection and Time-Series Tracking.Plant phenomics (Washington, D.C.) · 2023Article
- Panicle-Cloud: An Open and AI-Powered Cloud Computing Platform for Quantifying Rice Panicles from Drone-Collected Imagery to Enable the Classification of Yield Production in Rice.Plant phenomics (Washington, D.C.) · 2023Article
- Image-Based Phenotyping for Non-Destructive In Situ Rice (Sensors (Basel, Switzerland) · 2022Article
- High-throughput field crop phenotyping: current status and challenges.Breeding science · 2022Article
- Universal detection of curved rice panicles in complex environments using aerial images and improved YOLOv4 model.Frontiers in plant science · 2022Article
- Field rice panicle detection and counting based on deep learning.Frontiers in plant science · 2022Article
- High-Precision Wheat Head Detection Model Based on One-Stage Network and GAN Model.Frontiers in plant science · 2022Article
- Automatic and Accurate Calculation of Rice Seed Setting Rate Based on Image Segmentation and Deep Learning.Frontiers in plant science · 2021Article
- Unsupervised Bayesian learning for rice panicle segmentation with UAV images.Plant methods · 2020Article
- Internet of Unmanned Aerial Vehicles-A Multilayer Low-Altitude Airspace Model for Distributed UAV Traffic Management.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
The number of panicles per unit area is a common indicator of rice yield and is of great significance to yield estimation, breeding, and phenotype analysis. Traditional counting methods have various drawbacks, such as long delay times and high subjectivity, and they are easily perturbed by noise. To improve the accuracy of rice detection and counting in the field, we developed and implemented a panicle detection and counting system that is based on improved region-based fully convolutional networks, and we use the system to automate rice-phenotype measurements. The field experiments were conducted in target areas to train and test the system and used a rotor light unmanned aerial vehicle equipped with a high-definition RGB camera to collect images. The trained model achieved a precision of 0.868 on a held-out test set, which demonstrates the feasibility of this approach. The algorithm can deal with the irregular edge of the rice panicle, the significantly different appearance between the different varieties and growing periods, the interference due to color overlapping between panicle and leaves, and the variations in illumination intensity and shading effects in the field. The result is more accurate and efficient recognition of rice-panicles, which facilitates rice breeding. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a global scale.
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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.