Método para classificação de padrões da Lagarta do cartucho (Spodoptera frugiperda) na cultura do milho baseado em processamento de imagens digitais e aprendizado de máquina
Resumo
The detection, identification, and control of the Fall Armyworm (Spodoptera frugperda) pest
into the maize culture (Zea mays) are greatly dependent on the human factor. Currently, such
control occurs mainly through the use of capture traps. This makes the diagnosis of infestations
of this pest inefficient and can cause significant damage to production, as well as in general
some additional use of pesticides. The objective of this research is to use image and signal
processing techniques to establish a method for recognizing the Fall Armyworm (Spodoptera
frugperda) pattern in maize culture, allowing its early, reliable and supervised recognition, which
improves the state of art of controller procedments in order to obtain an automatized process.
Image aquisition, image enhancement, segmentation, features extraction, the use of Principal
Components Analisys (PCA) and superviosioned classification techniques were considered
for the method development. For image aquisition, it has been used an online image data
base. For image enhancement, Gaussiam and Non-Local Means filters were experimented
for noise reduction. Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) were
measured for filters avaliation. For semgmentation process, thresholding with seed pixels were
experimented on HSV and CIE L*a*b* color spaces. In order to automatize, Otsu technique was
applied in the process of image segmentation. For feature extraction, Histogram of Orientation
Gradient (HOG) and invariants moments of Hu were experimentd, in order to obtain texture
and geometric information, respectively, as well as, for feature vector dimensionality reduction
Princial Components Analisys (PCA) were experimented. For pattern classification of Fall
Armyworm (Spodoptera frugperda) a set of classifiers based on Support Verctor Machine (SVM)
was established. The developed method has shown to be suitable for Fall Armyworm (Spodoptera
frugperda) pattern classification, wich has contributed to the porpouse of decision making for
pest identification and control on maize culture. The method has also contributed to the evolution
of digital image processing techniques and analisys tools.
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