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Sensors 2011, 11(6), 6015-6036; doi:10.3390/s110606015
Article

Digital Image Sensor-Based Assessment of the Status of Oat (Avena sativa L.) Crops after Frost Damage

1,* , 2
, 2
 and 3
1 Colegio de Postgraduados, Campus Montecillo, km. 36.5 carretera México-Texcoco, cp 56230, Montecillo, Texcoco, Estado de México, C.P. 56230, México 2 Facultad de Informática, Universidad Complutense de Madrid, 28040-Madrid, Spain 3 Universidad Autónoma Chapingo, km 38.5 carretera México-Texcoco, cp 56230, Chapingo, Texcoco, Estado de México, C.P. 56230, México
* Author to whom correspondence should be addressed.
Received: 11 April 2011 / Revised: 18 May 2011 / Accepted: 30 May 2011 / Published: 3 June 2011
(This article belongs to the Special Issue Sensors in Agriculture and Forestry)
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Abstract

The aim of this paper is to classify the land covered with oat crops, and the quantification of frost damage on oats, while plants are still in the flowering stage. The images are taken by a digital colour camera CCD-based sensor. Unsupervised classification methods are applied because the plants present different spectral signatures, depending on two main factors: illumination and the affected state. The colour space used in this application is CIELab, based on the decomposition of the colour in three channels, because it is the closest to human colour perception. The histogram of each channel is successively split into regions by thresholding. The best threshold to be applied is automatically obtained as a combination of three thresholding strategies: (a) Otsu’s method, (b) Isodata algorithm, and (c) Fuzzy thresholding. The fusion of these automatic thresholding techniques and the design of the classification strategy are some of the main findings of the paper, which allows an estimation of the damages and a prediction of the oat production.
Keywords: digital image sensor; agricultural images; unsupervised classification; automatic thresholding; CIELab colour space; fuzzy error matrix; oat frost damage digital image sensor; agricultural images; unsupervised classification; automatic thresholding; CIELab colour space; fuzzy error matrix; oat frost damage
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Macedo-Cruz, A.; Pajares, G.; Santos, M.; Villegas-Romero, I. Digital Image Sensor-Based Assessment of the Status of Oat (Avena sativa L.) Crops after Frost Damage. Sensors 2011, 11, 6015-6036.

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