Authors:
Najmah Alharbi
1
;
Ji Zhou
2
and
Wenija Wang
3
Affiliations:
1
Taibah University, Saudi Arabia
;
2
Erlham Institute, United Kingdom
;
3
Universtiy of East Anglia and Nanjing Agricultural University, United Kingdom
Keyword(s):
Wheat Spikes, Counting, Gabor Filter, K-means, Segmentation, Clustering, Regression.
Related
Ontology
Subjects/Areas/Topics:
Clustering
;
Feature Selection and Extraction
;
Pattern Recognition
;
Regression
;
Theory and Methods
Abstract:
This study aims to develop an automated screening system that can estimate the number of wheat spikes (i.e.
ears) from a given wheat plant image acquired after the flowering stage. The platform can be used to assist
the dynamic estimation of wheat yield potential as well as grain yield based on wheat images captured by
the CropQuant platform. Our proposed system framework comprises three main stages. Firstly, it transforms
the wheat plant raw image data using colour index of vegetation extraction (CIVE) and then segments wheat
ear regions from the image to reduce the influence of the background signals. Secondly, it detects wheat ears
using Gabor filter banks and K-means clustering algorithm. Finally, it estimates the number of wheat spikes
within extracted wheat spike region through a regression method. The framework is tested with a real-world
dataset of wheat growth images equally distributed from flowering to ripening stages. The estimations of the
wheat ears were benchm
arked against the ground truth produced in this study by human manual counting.
Our automatic counting system achieved an average accuracy of 90.7% with a standard deviation of 0.055, at
a much faster speed than human experts and hence the system has a potential to be improved for agricultural
applications on wheat growth studies in the future.
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