TY - GEN
T1 - An Electronic Equipment for Automatic Identification of Forest Seed Species
AU - Tupac, Miguel
AU - Armas, Reiner
AU - Kemper, Guillermo
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - This work proposes an electronic equipment which identifies forest seeds for academic and research purposes. Existing integral solutions are prohibitively costly for silviculture laboratories used in forestry teaching. Thus, they must identify the seed by visual inspection, causing visual fatigue and results with low reliability. The state of the art proposes solutions using support vector machines, achieving a 98.82% accuracy for sunflower seeds. Other solutions extract morphological attributes of mussel seeds to identify up to 5 species with an accuracy of 95%. Most solutions only identify a single seed type with similar sizes. In this context, an electronic equipment is developed. It consists of an image acquisition enclosure, an electromechanical device to move a camera so different sizes of seeds can be imaged at different distances, and a single-board computer to control the image processing and artificial intelligence (convolutional neural network) algorithms. The equipment achieves an accuracy of 95%, which is satisfactory for potential users and silviculture specialists.
AB - This work proposes an electronic equipment which identifies forest seeds for academic and research purposes. Existing integral solutions are prohibitively costly for silviculture laboratories used in forestry teaching. Thus, they must identify the seed by visual inspection, causing visual fatigue and results with low reliability. The state of the art proposes solutions using support vector machines, achieving a 98.82% accuracy for sunflower seeds. Other solutions extract morphological attributes of mussel seeds to identify up to 5 species with an accuracy of 95%. Most solutions only identify a single seed type with similar sizes. In this context, an electronic equipment is developed. It consists of an image acquisition enclosure, an electromechanical device to move a camera so different sizes of seeds can be imaged at different distances, and a single-board computer to control the image processing and artificial intelligence (convolutional neural network) algorithms. The equipment achieves an accuracy of 95%, which is satisfactory for potential users and silviculture specialists.
KW - CNN
KW - Electronic equipment
KW - Forest seeds
KW - Identification
KW - Image processing
UR - https://www.scopus.com/pages/publications/85147991646
U2 - 10.1007/978-3-031-24985-3_10
DO - 10.1007/978-3-031-24985-3_10
M3 - Contribución a la conferencia
AN - SCOPUS:85147991646
SN - 9783031249846
T3 - Communications in Computer and Information Science
SP - 130
EP - 142
BT - Applied Technologies - 4th International Conference, ICAT 2022, Revised Selected Papers
A2 - Botto-Tobar, Miguel
A2 - Zambrano Vizuete, Marcelo
A2 - Montes León, Sergio
A2 - Torres-Carrión, Pablo
A2 - Durakovic, Benjamin
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International Conference on Applied Technologies, ICAT 2022
Y2 - 23 November 2022 through 25 November 2022
ER -