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.
La erosión hídrica de origen antrópico es una de las formas de degradación del suelo y del agua más importantes en México y en el mundo, causante de la desertificación en las zonas semiáridas y subhúmedas de nuestro país, afectando 32 millones de ha de suelos de ladera con vocación forestal (SEMARNAP, 1998).La zona eminentemente forestal del municipio de Texcoco, está sujeta al impacto causado por talas clandestinas, el sobre pastoreo, los incendios, así como al cambio de uso del suelo de forestal a agricultura de ladera, minería y urbano; lo que incrementa la compactación del suelo, disminución de las tasas de infiltración y percolación, incrementando el número de escurrimientos superficiales erosivos durante el año que disminuyen la fertilidad del suelo y su capacidad de retención de humedad lo que conduce a mediano plazo a la disminución de la disponibilidad de agua de calidad y la desertificación del paisaje.
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