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您的检索式：作者名="David RJordan"
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| 1 | A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting显示文摘The yield of cereal crops such as sorghum(Sorghum bicolor L.Moench)depends on the distribution of crop-heads in varying branching arrangements.Therefore,counting the head number per unit area is critical for plant breeders to correlate with the genotypic variation in a specific breeding field.However,measuring such phenotypic traitsmanually is an extremely labor-intensive process and suffers from low efficiency and human errors.Moreover,the process is almost infeasible for large-scale breeding plantations or experiments.Machine learning-based approaches like deep convolutional neural network(CNN)based object detectors are promising tools for efficient object detection and counting.However,a significant limitation of such deep learningbased approaches is that they typically require a massive amount of hand-labeled images for training,which is still a tedious process.Here,we propose an active learning inspired weakly supervised deep learning framework for sorghum head detection and counting from UAV-based images.We demonstrate that it is possible to significantly reduce human labeling effort without compromising final model performance(��2 between human count and machine count is 0.88)by using a semitrained CNN model(i.e.,trained with limited labeled data)to perform synthetic annotation.In addition,we also visualize key features that the network learns.This improves trustworthiness by enabling users to better understand and trust the decisions that the trained deep learning model makes. | Sambuddha Ghosal Bangyou Zheng Scott CChapman Andries BPotgieter David RJordan Xuemin Wang Asheesh KSingh Arti Singh Masayuki Hirafuji Seishi Ninomiya Baskar Ganapathysubramanian Soumik Sarkar Wei Guo | 2019 | Plant Phenomics2019，1，1： | 16 |
| 2 | Detecting Sorghum Plant and Head Features from Multispectral UAV Imagery显示文摘In plant breeding,unmanned aerial vehicles(UAVs)carrying multispectral cameras have demonstrated increasing utility for high-throughput phenotyping(HTP)to aid the interpretation of genotype and environment effects on morphological,biochemical,and physiological traits.A key constraint remains the reduced resolution and quality extracted from“stitched”mosaics generated from UAV missions across large areas.This can be addressed by generating high-quality reflectance data from a single nadir image per plot.In this study,a pipeline was developed to derive reflectance data from raw multispectral UAV images that preserve the original high spatial and spectral resolutions and to use these for phenotyping applications.Sequential steps involved(i)imagery calibration,(ii)spectral band alignment,(iii)backward calculation,(iv)plot segmentation,and(v)application.Each step was designed and optimised to estimate the number of plants and count sorghum heads within each breeding plot.Using a derived nadir image of each plot,the coefficients of determination were 0.90 and 0.86 for estimates of the number of sorghum plants and heads,respectively.Furthermore,the reflectance information acquired from the different spectral bands showed appreciably high discriminative ability for sorghum head colours(i.e.,red and white).Deployment of this pipeline allowed accurate segmentation of crop organs at the canopy level across many diverse field plots with minimal training needed from machine learning approaches. | Yan Zhao Bangyou Zheng Scott CChapman Kenneth Laws Barbara George-Jaeggli Graeme LHammer David RJordan Andries B.Potgieter | 2021 | Plant Phenomics2021，3，1： | 0 |
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