Abstract
The manifestation of pathogens in plantations is the most important cause of losses in several crops. These usually represent less income to the farmers due to the lower product quality as well as higher prices to the consumer due to the smaller offering of goods. The sooner the disease is identified the sooner one can control it through the use of agrochemicals, avoiding great damages to the plantation. This chapter introduces a method for the automatic classification of cotton diseases based on the feature extraction of foliar symptoms from digital images. The method uses the energy of the wavelet transform for feature extraction and a Support Vector Machine for the actual classification. Five possible diagnostics are provided: (1) healthy (SA), (2) injured with Ramularia disease (RA), (3) infected with Bacterial Blight (BA), (4) infected with Ascochyta Blight (AS), or (5) possibly infected with an unknown disease.
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Bernardes, A.A. et al. (2013). Identification of Foliar Diseases in Cotton Crop. In: Tavares, J., Natal Jorge, R. (eds) Topics in Medical Image Processing and Computational Vision. Lecture Notes in Computational Vision and Biomechanics, vol 8. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0726-9_4
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DOI: https://doi.org/10.1007/978-94-007-0726-9_4
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