کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
84987 | 158917 | 2009 | 5 صفحه PDF | دانلود رایگان |

This study reports a machine vision system for the identification of the visual symptoms of plant diseases, from coloured images. Diseased regions shown in digital pictures of cotton crops were enhanced, segmented, and a set of features were extracted from each of them. Features were then used as inputs to a Support Vector Machine (SVM) classifier and tests were performed to identify the best classification model. We hypothesised that given the characteristics of the images, there should be a subset of features more informative of the image domain. To test this hypothesis, several classification models were assessed via cross-validation. The results of this study suggested that: texture-related features might be used as discriminators when the target images do not follow a well defined colour or shape domain pattern; and that machine vision systems might lead to the successful discrimination of targets when fed with appropriate information.
Journal: Computers and Electronics in Agriculture - Volume 66, Issue 2, May 2009, Pages 121–125