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            <title xml:lang="en">A classic approach to detect potato diseases</title>
            <title xml:lang="es">Un enfoque clásico para detectar plagas en la papa.</title>
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                <title xml:lang="en">A classic approach to detect potato diseases</title>
                <title xml:lang="es">Un enfoque clásico para detectar plagas en la papa.</title>
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              <p>Nowadays, agriculture is mainly influenced by technology. Hence, computer vision is a crucial component to improve crops harvests. In this work, we propose an automatic solution to detect potato pests diseases. Our solution combines image processing and machine learning and finds that potatoes disease can be detected with high accuracy. We compare traditional classifiers and find that SVM is the best performer. We also analyze our results with PCA and we observe that many classes are linearly separable. This is an ongoing effort to improve crop harvest</p>
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              <p>Hoy en día, la agricultura está influenciada principalmente por la tecnología. Por lo tanto, la visión por computadora es un componente crucial para mejorar las cosechas de cultivos. En este trabajo, proponemos una solución automática para detectar enfermedades de plagas de papa. Nuestra solución combina el procesamiento de imágenes y el aprendizaje automático y descubre que la enfermedad de la papa se puede detectar con gran precisión. Comparamos clasificadores tradicionales y descubrimos que SVM es el que tiene mejor rendimiento. También analizamos nuestros resultados con PCA y observamos que muchas clases son linealmente separables. Este es un esfuerzo continuo para mejorar la cosecha</p>
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