An Electronic Equipment for Automatic Identification of Forest Seed Species
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Issue Date
2023-01-01Keywords
CNNElectronic equipment
Forest seeds
Identification
Image processing
Forest Seed Identification
Electronic Equipment
Silviculture Laboratories
Visual Inspection
Support Vector Machines
Morphological Attributes
Image Acquisition Enclosure
Electromechanical Device
Single-board Computer
Convolutional Neural Network
Metadata
Show full item recordJournal
Communications in Computer and Information ScienceDOI
10.1007/978-3-031-24985-3_10Additional Links
https://link.springer.com/chapter/10.1007/978-3-031-24985-3_10Abstract
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.Type
info:eu-repo/semantics/articleRights
info:eu-repo/semantics/embargoedAccessLanguage
engISSN
18650929EISSN
18650937ae974a485f413a2113503eed53cd6c53
10.1007/978-3-031-24985-3_10
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