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High-Throughput Spike Detection in Greenhouse Cultivated Grain Crops with Attention Mechanisms-Based Deep Learning Models被引量：1
《Plant Phenomics》2024年第1期195-205,共11页Sajid Ullah Klára Panzarová Martin Trtílek Matej Lexa Vojtěch Máčala Kerstin Neumann Thomas Altmann Jan Hejátko Markéta Pernisová Evgeny Gladilin 
supported by European Regional Development Fund-Project SINGING PLANT(No.CZ.02.1.01/0.0/0.0/16_026/0008446),which received a financial contri bution from the Ministry of Education,Youths and Sports of the CR in the form of special support through the National Program for Sustainability Ⅱ funds;supported by the project of specific research provided by Masaryk University.
Detection of spikes is the first important step toward image-based quantitative assessment of crop yield.However,spikes of grain plants occupy only a tiny fraction of the image area and often emerge in the middle of t...
关键词：CULTIVAR ARCHITECTURAL RENDER 
SegVeg: Segmenting RGB Images into Green and Senescent Vegetation by Combining Deep and Shallow Methods被引量：3
《Plant Phenomics》2022年第1期26-42,共17页Mario Serouart Simon Madec Etienne David Kaaviya Velumani Raul LopezLozano Marie Weiss Frederic Baret 
The study was partly supported by several projects including ANR PHENOME(Programme d’investissement d’avenir),Digitag(PIA Institut Convergences Agriculture Numérique ANR-16-CONV-0004),CASDAR LITERAL,and P2S2 funded by CNES.
Pixel segmentation of high-resolution RGB images into chlorophyll-active or nonactive vegetation classes is a first step often required before estimating key traits of interest.We have developed the SegVeg approach fo...
关键词：DEEP offering RENDER 
GANana:Unsupervised Domain Adaptation for Volumetric Regression of Fruit被引量：4
《Plant Phenomics》2021年第1期326-336,共11页Zane K.J.Hartley Aaron S.Jackson Michael Pound Andrew P.French 
the Engineering and Physical Sciences Research Council[EP/R513283/1]awarded to Zane K.J.Hartley。
3D reconstruction of fruit is important as a key component of fruit grading and an important part of many size estimation pipelines.Like many computer vision challenges,the 3D reconstruction task suffers from a lack o...
关键词：IMAGE RENDER adapted 
High-Throughput Rice Density Estimation from Transplantation to Tillering Stages Using Deep Networks被引量：4
《Plant Phenomics》2020年第1期255-268,共14页Liang Liu Hao Lu Yanan Li Zhiguo Cao 
Part of LL’s contribution was made when visiting the University of Adelaide.This work was supported in part by the Natural Science Foundation of China under Grant Nos.61876211 and 61906139 and in part by the Hubei Provincial Natural Science Foundation of China under Grant 2019CFB173.
Rice density is closely related to yield estimation,growth diagnosis,cultivated area statistics,and management and damage evaluation.Currently,rice density estimation heavily relies on manual sampling and counting,whi...
关键词：ILLUMINATION ESTIMATION RENDER 
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