Bibliographic citations
Armas, R., Tupac, M. (2022). Desarrollo de un equipo electrónico de conteo e identificación de semillas forestales basado en procesamiento digital de imágenes [Tesis, Universidad Peruana de Ciencias Aplicadas (UPC)]. http://hdl.handle.net/10757/667155
Armas, R., Tupac, M. Desarrollo de un equipo electrónico de conteo e identificación de semillas forestales basado en procesamiento digital de imágenes [Tesis]. PE: Universidad Peruana de Ciencias Aplicadas (UPC); 2022. http://hdl.handle.net/10757/667155
@misc{renati/403712,
title = "Desarrollo de un equipo electrónico de conteo e identificación de semillas forestales basado en procesamiento digital de imágenes",
author = "Tupac Orellana, Miguel Angel",
publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
year = "2022"
}
This research proposes a portable electronic device that allows the identification of forest seeds, which are stored to be germinated and finally planted or used for research studies. At present there are solutions that have a very high cost and take up too much space, so that in the Forestry Laboratory oriented to teaching, the identification is done by visual inspection, this causes in some cases visual fatigue and decrease in the reliability of the results generated by the laboratory technicians. The state of the art proposes different studies that try to solve the problem of seed identification, such as the use of SVM (Support Vector Machine), which obtains 98.82% only for one type of sunflower seed. Another method used is the extraction of morphological characteristics of mussels to identify up to five species achieving a recognition rate of 95%. It is worth mentioning that most solutions identify only one type of seed and of a very similar size. With all the above mentioned, we propose an electronic equipment that consists of an enclosure of dimensions (60 cm x 60 cm x 80cm), an electromechanical mechanism that moves a camera which takes images at different distances according to the size of the seeds that are entered, all this is controlled by a computer of reduced board which is embedded an image processing algorithm with artificial intelligence (CNN), the results obtained were an accuracy of 95% in the identification, which is considered satisfactory for the requirements of the problem posed.
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