Nowadays, new security and protection systems for citizens are being developed, since criminals have found techniques to violate those already known, such as those based on fingerprints, facial recognition, iris and voice. Thus, using biometric data, new systems are being developed that are more secure, infallible and fast to identify each person, making it impossible to impersonate them, as has happened with other methods. Recently new identification methods have been proposed based on hand geometry and palmprint based on texture techniques for the identification of hand characteristics such as ridges, edges, points, and textures. Following this trend, this paper presents a method based on the detection of the palm print, acquired by contact, through the use of a scanner. For this purpose, the image is segmented to detect the silhouette of the hand and delimit the working area, achieving greater speed in identification. The images are then used as input to a convolutional neural network VGG 16 for learning and identification of subjects, achieving 100% accuracy.
The avocado is a fruit that grows in tropical and subtropical areas, very popular in the markets due to its great nutritional qualities and medicinal properties. The avocado is a plant of great commercial interest for Peru and Colombia, countries that export this fruit. This tree is affected by a wide variety of diseases reducing its production, even causing the death of the plant. The most frequent disease of the avocado tree in the production zone of Peru is caused by the fungus Lasiodiplodia Theobromae, which is characterized in its initial stage by producing a chancre around the stems and branches of the tree. Detection is commonly made by manual inspection of the plants by an expert, which makes it difficult to detect the fungus in extensive plantations. Therefore, in this work we present a semi-automatic method for the detection of this disease based on image processing and machine learning techniques. For this purpose, an acquisition protocol was defined. The identification of the disease was performed by taking as input pre-processed images of the tree branches. A learning technique was evaluated, based on a shallow CNN, obtaining 93% accuracy.
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