Online citations, reference lists, and bibliographies.

Fine-grained Maize Tassel Trait Characterization With Multi-view Representations

H. Lu, Z. Cao, Yang Xiao, Zhiwen Fang, Y. Zhu, Ke Xian
Published 2015 · Engineering, Computer Science

Cite This
Download PDF
Analyze on Scholarcy
Display Omitted A novel pipeline is proposed for efficient tassel potential region extraction.We proposed to characterize the maize tassel with a multi-view mechanism.Effective tassel detection is performed for fine-grained trait characterization.Time-series monitoring is executed to acquire tassel trait growing parameters.We have established a relatively large-scale maize tassel dataset. The characteristics of maize tassel trait are important cues to improve the farming operation for production enhancement. Currently, the information obtained from the maize tassel mainly depends on human labor, which is subjective and labor-intensive. Recent researches have introduced several image-based approaches to overcome the shortage with a modest degree of success. However, due to the variation of cultivar, pose and illumination, and the clustered background, characterizing the maize tassel trait with computer vision remains a challenging problem. To this end, an automatic fine-grained machine vision system termed mTASSEL is developed in this paper. We proposed to characterize the maize tassel with multi-view representations that combine multiple feature views and different channel views, which can alleviate the influence of environmental variations. In addition to the total tassel number trait, some fine-grained tassel traits, including the tassel color, branch number, length, width, perimeter and diameter, are further characterized to execute the time-series monitoring. To boost the related research, a relatively large-scale maize tassel dataset (10 sequences with 16,031 samples) is first constructed by our team. The experimental results demonstrate that both system modules significantly outperform other state-of-the-art approaches by large margins (26.0% for the detection and 7.8% for the segmentation). Results of this research can serve the automatic growth stage detection, accurate yield estimation and machine detasseling, as well as the field-based phenotyping research. The dataset and source code of the system are available online.
This paper references
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
T. Ojala (2002)
of selection during plant domestication
N. L. Roux (2012)
Estimates of genetic parameters for tosses characters in maize ( zea mays l ) and breeding respectives
Isaias Olívio Geraldi (1985)
Root growth, available soil water, and water-use efficiency of winter wheat under different irrigation regimes applied at different growth stages in North China
Q. Li (2010)
Illumination invariant segmentation of spatio-temporal images by spatio-temporal Markov random field model
S. Kamijo (2002)
Improving the Fisher Kernel for Large-Scale Image Classification
F. Perronnin (2010)
An image-based approach for automatic detecting five true-leaves stage of cotton
Yanan Li (2013)
The devil is in the details: an evaluation of recent feature encoding methods
K. Chatfield (2011)
A k-means clustering algorithm
J. Hartigan (1979)
The Pascal Visual Object Classes (VOC) Challenge
M. Everingham (2009)
A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets
N. Roux (2012)
mCENTRIST: A Multi-Channel Feature Generation Mechanism for Scene Categorization
Y. Xiao (2014)
Phenotypic diversity for morphological and agronomic characteristics in chickpea core collection
H. D. Upadhyaya (2004)
Definition of Linear Color Models in the RGB Vector Color Space to Detect Red Peaches in Orchard Images Taken under Natural Illumination
Mercè Teixidó (2012)
British Machine Vision Conference (BMVC)
D. Damen (2007)
The nature of selection during plant domestication
M. Purugganan (2009)
Determining the Most Important Physiological and Agronomic Traits Contributing to Maize Grain Yield through Machine Learning Algorithms: A New Avenue in Intelligent Agriculture
Avat Shekoofa (2014)
Visualization of the growth process of pruned and trained citrus trees for appropriate cultivation
Miyuki Hayashi (2014)
Corn tassel detection based on image processing
Wenbing Tang (2011)
Liblinear: A library
K. W. Chang (2008)
Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice
Wanneng Yang (2014)
Estimates of genetic-parameters for tassel
I. Geraldi (1985)
Semantic Segmentation with Second-Order Pooling
J. Carreira (2012)
Detecting corn tassels using computer vision and support vector machines
Ferhat Kurtulmuş (2014)
Green citrus detection using 'eigenfruit', color and circular Gabor texture features under natural outdoor conditions
F. Kurtulmuş (2011)
Histograms of oriented gradients for human detection
N. Dalal (2005)
Object Detection with Discriminatively Trained Part Based Models
Pedro F. Felzenszwalb (2009)
Illumination invariant segmentation of vegetation for time series wheat images based on decision tree model
W. Guo (2013)
Automated Visual Yield Estimation in Vineyards
Stephen Nuske (2014)
Genetic diversity in cultivated common bean: II. Marker-based analysis of morphological and agronomic traits
S. Singh (1991)
Machine Vision Conference (BMVC)
A. Coates (2011)
Estimates of genetic - parameters for tassel characters in maize ( zea - mays - l ) and breeding perspectives
I. Geraldi (1985)
Selective search for
K. van de Sande (2013)
Type-2 fuzzy thresholding using glsc histogram
Z. Cao (2010)
Large Margin Dimensionality Reduction for Time Series
Xiao Yu (2010)
Phenotypic diversity
R. Ortiz (2002)
Leaf Angle, Tassel Morphology, and the Performance of Maize Hybrids 1
R. J. Lambert (1978)
M. D. Purugganan
Automatic detection of crop rows in maize fields with high weeds pressure
M. Montalvo (2012)
Type-2 fuzzy thresholding using GLSC histogram of human visual nonlinearity characteristics.
Yang Xiao (2011)
Automated characterization of flowering dynamics in rice using field-acquired time-series RGB images
W. Guo (2015)
Critical period for weed control: the concept and data analysis
Stevan Z. Knezevic (2002)
An image-based approach for automatic detecting tasseling stage of maize using spatio-temporal saliency
M. Ye (2013)
Extracting and composing robust features with denoising autoencoders
Pascal Vincent (2008)
Ensemble Methods: Foundations and Algorithms
Z. Zhou (2012)
Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach
F. Liebisch (2015)
Ensemble Methods: Foundations and Algorithms [Book Review]
F. Schwenker (2013)
Vlfeat: an open and portable library of computer vision algorithms
A. Vedaldi (2010)
Automatic image-based detection technology for two critical growth stages of maize: Emergence and three-leaf stage
Zhenghong Yu (2013)
Selective Search for Object Recognition
J. Uijlings (2013)
An Analysis of Single-Layer Networks in Unsupervised Feature Learning
A. Coates (2011)
Statistical algorithms: algorithm AS 136: a K-means clustering algorithm
J. Hartigan (1979)
Recognition of shapes by attributed skeletal graphs
C. D. Ruberto (2004)
What is Genetic Diversity
John Woolliams (2007)
Multiresolution grayscale and rotation invariant texture classification with local binary patterns
T. Ojala (2002)
CENTRIST: A Visual Descriptor for Scene Categorization
Jianxin Wu (2011)
Large Margin Dimensionality Reduction for Action Similarity Labeling
Xiaojiang Peng (2014)
Fisher Vector Faces in the Wild
K. Simonyan (2013)
LIBLINEAR: A Library for Large Linear Classification
Rong-En Fan (2008)
Green citrus detection using ‘eigenfruit
W. S. Lee (2011)
What is an object?
B. Alexe (2010)
maize (zea-mays-l) and breeding perspectives
T. Fukatsu (2015)
Precision Agriculture and Food Security
R. Gebbers (2010)

This paper is referenced by
Physical, chemical and biological properties of maize variety Fr -28
Iván González Góngora (2018)
Automatic crop detection under field conditions using the HSV colour space and morphological operations
Esmael Hamuda (2017)
An maize leaf segmentation algorithm based on image repairing technology
P. Wang (2020)
Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review
Diego Inácio Patrício (2018)
Panicle-SEG: a robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization
X. Xiong (2017)
Fusion of superpixel, expectation maximization and PHOG for recognizing cucumber diseases
S. Zhang (2017)
Automatic Counting of in situ Rice Seedlings from UAV Images Based on a Deep Fully Convolutional Neural Network
Jintao Wu (2019)
Drought Stress Detection in the Middle Growth Stage Of Maize Based On Gabor Filter and Deep Learning
Boran Jiang (2019)
High-Throughput Rice Density Estimation from Transplantation to Tillering Stages Using Deep Networks
L. Liu (2020)
Detecting tomatoes in greenhouse scenes by combining AdaBoost classifier and colour analysis
Yuanshen Zhao (2016)
In-field automatic detection of maize tassels using computer vision
Mingqiang Ji (2020)
Crowdsourcing Image Analysis for Plant Phenomics to Generate Ground Truth Data for Machine Learning
Zachary D. Siegel (2018)
Detection of maize drought based on texture and morphological features
Boran Jiang (2018)
Fine-grained maize cultivar identification using filter-specific convolutional activations
Hao Lu (2016)
Toward Good Practices for Fine-Grained Maize Cultivar Identification With Filter-Specific Convolutional Activations
H. Lu (2018)
Towards fine-grained maize tassel flowering status recognition: Dataset, theory and practice
Hao Lu (2017)
TasselNet: counting maize tassels in the wild via local counts regression network
H. Lu (2017)
Faster R-CNN-based deep learning for locating corn tassels in UAV imagery
Aziza Najib AL-Zadjali (2020)
Detection of Maize Tassels from UAV RGB Imagery with Faster R-CNN
Yunling Liu (2020)
Two-dimensional subspace alignment for convolutional activations adaptation
H. Lu (2017)
Region-based colour modelling for joint crop and maize tassel segmentation
Hao Lu (2016)
Crowdsourcing image analysis for plant phenomics to generate ground truth data for machine learning
Naihui Zhou (2018)
Automatic Radish Wilt Detection Using Image Processing Based Techniques and Machine Learning Algorithm
Asif Ashraf Patankar (2020)
TasselNetv2: in-field counting of wheat spikes with context-augmented local regression networks
Haipeng Xiong (2019)
Maize tassels detection: a benchmark of the state of the art
Hongwei Zou (2020)
Semantic Scholar Logo Some data provided by SemanticScholar