Next Article in Journal
DRIFTS Sensor: Soil Carbon Validation at Large Scale (Pantelleria, Italy)
Next Article in Special Issue
Design of a Soil Cutting Resistance Sensor for Application in Site-Specific Tillage
Previous Article in Journal
Nanostructured Surfaces and Detection Instrumentation for Photonic Crystal Enhanced Fluorescence
Previous Article in Special Issue
Camera Sensor Arrangement for Crop/Weed Detection Accuracy in Agronomic Images
Open AccessArticle

Seedling Discrimination with Shape Features Derived from a Distance Transform

Institute of Chemical Engineering, Biotechnology and Environmental Technology,University of Southern Denmark, Campusvej 55, DK-5230 Odense, Denmark
Author to whom correspondence should be addressed.
Sensors 2013, 13(5), 5585-5602;
Received: 6 March 2013 / Revised: 4 April 2013 / Accepted: 8 April 2013 / Published: 26 April 2013
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
The aim of this research is an improvement of plant seedling recognition by two new approaches of shape feature generation based on plant silhouettes. Experiments show that the proposed feature sets possess value in plant recognition when compared with other feature sets. Both methods approximate a distance distribution of an object, either by resampling or by approximation of the distribution with a high degree Legendre polynomial. In the latter case, the polynomial coefficients constitute a feature set. The methods have been tested through a discrimination process where two similar plant species are to be distinguished into their respective classes. The used performance assessment is based on the classification accuracy of 4 different classifiers (a k-Nearest Neighbor, Naive-Bayes, Linear Support Vector Machine, Nonlinear Support Vector Machine). Another set of 21 well-known shape features described in the literature is used for comparison. The used data consisted of 139 samples of cornflower (Centaura cyanus L.) and 63 samples of nightshade (Solanum nigrum L.). The highest discrimination accuracy was achieved with the Legendre Polynomial feature set and amounted to 97.5%. This feature set consisted of 10 numerical values. Another feature set consisting of 21 common features achieved an accuracy of 92.5%. The results suggest that the Legendre Polynomial feature set can compete with or outperform the commonly used feature sets. View Full-Text
Keywords: object recognition; machine vision; shape characterization object recognition; machine vision; shape characterization
MDPI and ACS Style

Giselsson, T.M.; Midtiby, H.S.; Jørgensen, R.N. Seedling Discrimination with Shape Features Derived from a Distance Transform. Sensors 2013, 13, 5585-5602.

Show more citation formats Show less citations formats

Article Access Map by Country/Region

Only visits after 24 November 2015 are recorded.
Search more from Scilit
Back to TopTop