An integrated rice panicle phenotyping method based on X-ray and RGB scanning and deep learning

Abstract Rice panicle phenotyping is required in rice breeding for high yield and grain quality. To fully evaluate spikelet and kernel traits without threshing and hulling, using X-ray and RGB scanning, we developed an integrated rice panicle phenotyping system and a corresponding image analysis pipeline. We compared five methods of counting spikelets and found that Faster R-CNN achieved high accuracy (R2 of 0.99) and speed. Faster R-CNN was also applied to indica and japonica classification and achieved 91% accuracy. The proposed integrated panicle phenotyping method offers benefit for rice functional genetics and breeding.

• Publication year

  2020

• Venue

  Crop Journal

• Publication date

  2020-08-10

• Fields of study

  Agricultural and Food Sciences, Computer Science, Biology

• Identifiers
  DOI 10.1016/j.cj.2020.06.009
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• 11th International Conference on Information Technology in Medicine and Education

  2021cited by this paper
• Detection of Maize Tassels from UAV RGB Imagery with Faster R-CNN

  2020cited by this paper
• Detection and enumeration of wheat grains based on a deep learning method under various scenarios and scales

  2020cited by this paper
• Nondestructive 3D Image Analysis Pipeline to Extract Rice Grain Traits Using X-Ray Computed Tomography

  2020cited by this paper
• Mask R-CNN

  2020cited by this paper
• Maize seedling detection under different growth stages and complex field environments based on an improved Faster R–CNN

  2019cited by this paper
• Cascade R-CNN: High Quality Object Detection and Instance Segmentation

  2019cited by this paper
• MMDetection: Open MMLab Detection Toolbox and Benchmark

  2019cited by this paper
• Tomato Diseases Recognition Based on Faster RCNN

  2019cited by this paper
• Use of X-ray micro computed tomography imaging to analyze the morphology of wheat grain through its development

  2019cited by this paper
• Semiautomated Feature Extraction from RGB Images for Sorghum Panicle Architecture GWAS1[OPEN]

  2018influential reference
• Seed-per-pod estimation for plant breeding using deep learning

  2018cited by this paper
• Deep Learning: Individual Maize Segmentation From Terrestrial Lidar Data Using Faster R-CNN and Regional Growth Algorithms

  2018cited by this paper
• Deep learning in agriculture: A survey

  2018cited by this paper
• Combining high-throughput micro-CT-RGB phenotyping and genome-wide association study to dissect the genetic architecture of tiller growth in rice

  2018cited by this paper
• Benefit of using biodegradable film on rice grain yield and N use efficiency in ground cover rice production system

  2017cited by this paper
• Shallow and Deep Model Investigation for Distinguishing Corn and Weeds

  2017cited by this paper
• Deep Count: Fruit Counting Based on Deep Simulated Learning

  2017cited by this paper
• Feature Pyramid Networks for Object Detection

  2016cited by this paper
• Deep Residual Learning for Image Recognition

  2015influential reference
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

  2015cited by this paper
• SSD: Single Shot MultiBox Detector

  2015cited by this paper
• Breeding signatures of rice improvement revealed by a genomic variation map from a large germplasm collection

  2015cited by this paper
• GrainScan: a low cost, fast method for grain size and colour measurements

  2014cited by this paper
• High-Resolution Inflorescence Phenotyping Using a Novel Image-Analysis Pipeline, PANorama1[W][OPEN]

  2014cited by this paper
• Very Deep Convolutional Networks for Large-Scale Image Recognition

  2014cited by this paper
• First Report of Bacterial Panicle Blight of Rice Caused by Burkholderia glumae in South Africa.

  2014cited by this paper
• P-TRAP: a Panicle Trait Phenotyping tool

  2013influential reference
• Variation of Panicle Differentiation Stage by Leaf Growth According to Rice Cultivars and Transplanting Time

  2013cited by this paper
• Rice panicle length measuring system based on dual-camera imaging

  2013influential reference
• SmartGrain: High-Throughput Phenotyping Software for Measuring Seed Shape through Image Analysis1[C][W][OA]

  2012cited by this paper
• Scikit-learn: Machine Learning in Python

  2011cited by this paper
• A novel machine-vision-based facility for the automatic evaluation of yield-related traits in rice

  2011cited by this paper
• Original paper: Fast discrimination and counting of filled/unfilled rice spikelets based on bi-modal imaging

  2011cited by this paper
• Genetic and molecular bases of rice yield.

  2010cited by this paper
• Food Security: The Challenge of Feeding 9 Billion People

  2010cited by this paper
• Detection of sprouted wheat kernels using soft X-ray image analysis

  2007cited by this paper
• Strategies for developing Green Super Rice

  2007cited by this paper
• Dual energy X-ray image analysis for classifying vitreousness in durum wheat

  2007cited by this paper
• Support-vector networks

  2004cited by this paper
• Improving quality inspection of food products by computer vision: a review

  2004cited by this paper
• Machine vision technology for agricultural applications

  2002cited by this paper
• Selection Responses for the Characters Related to Yield Sink Capacity of Rice

  1997cited by this paper

• Application of RGB-Imaging techniques for high-throughput plant phenotyping- A Review

  2025cites this paper
• “Image-Spectral” fusion monitoring of peanut leaf spot disease level based on deep learning

  2025cites this paper
• Panicle-Driven Rice Yield Estimation Using YOLO: A Data-Centric Deep Learning Approach

  2025cites this paper
• Rapid detection of corn seed turtle cracks based on transmitted light image and deep learning method

  2025cites this paper
• PanicleNeRF: Low-Cost, High-Precision In-Field Phenotyping of Rice Panicles with Smartphone

  2024cites this paper
• RICE GRAIN TRAIT ESTIMATION USING COLOR SPACE CONVERSION AND DEEP LEARNING-BASED IMAGE SEGMENTATION

  2024cites this paper
• A High-Throughput Method for Accurate Extraction of Intact Rice Panicle Traits

  2024cites this paper
• Unveiling the Potential of Artificial Intelligence in Plant Phenotyping and Disease Detection: A Survey

  2024cites this paper
• Rice spikelet’s disease detection using hybrid optimization model and optimized CNN

  2024cites this paper
• Accurate rice grain counting in natural morphology: A method based on image classification and object detection

  2024cites this paper
• Size measurement and filled/unfilled detection of rice grains using backlight image processing

  2023cites this paper
• AIseed: An automated image analysis software for high-throughput phenotyping and quality non-destructive testing of individual plant seeds

  2023cites this paper
• Research on Asphalt Pavement Disease Detection Based on Improved YOLOv5s

  2023influential citation
• High-throughput and separating-free phenotyping method for on-panicle rice grains based on deep learning

  2023cites this paper
• Seedscreener: A novel integrated wheat germplasm phenotyping platform based on NIR-feature detection and 3D-reconstruction

  2023cites this paper
• A Pathway to Assess Genetic Variation of Wheat Germplasm by Multidimensional Traits with Digital Images

  2023cites this paper
• Fast and robust monitoring of broken rice kernels in the course of milling

  2023cites this paper
• Quick and accurate monitoring peanut seedlings emergence rate through UAV video and deep learning

  2022cites this paper
• An Intelligent Rice Yield Trait Evaluation System Based on Threshed Panicle Compensation

  2022cites this paper
• Contribution of spike type and characteristics to the seed yield of Elymus sibiricus L. on Qinghai–Tibetan Plateau

  2022cites this paper
• A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning

  2022cites this paper
• Automatic Detection and Counting of Wheat Spikelet Using Semi-Automatic Labeling and Deep Learning

  2022cites this paper
• Crop breeding for a changing climate: integrating phenomics and genomics with bioinformatics

  2021cites this paper
• Ir-UNet: Irregular Segmentation U-Shape Network for Wheat Yellow Rust Detection by UAV Multispectral Imagery

  2021cites this paper
• REPORT ON ESTIMATING RICE FIELD QUALITY USING ARTIFICIAL INTELLIGENCE

  2021cites this paper
• LabVIEW Based Real-time Color Measurement System

  2021cites this paper
• Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature

  2021cites this paper
• Detection and Dynamic Variation Characteristics of Rice Nitrogen Status after Anthesis Based on the RGB Color Index

  2021cites this paper