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2023 | OriginalPaper | Chapter

Design of a Segmentation and Classification System for Seed Detection Based on Pixel Intensity Thresholds and Convolutional Neural Networks

Authors : Oscar J. Suarez, Edgar Macias-Garcia, Carlos J. Vega, Yersica C. Peñaloza, Nicolás Hernández Díaz, Victor M. Garrido

Published in: Applications of Computational Intelligence

Publisher: Springer Nature Switzerland

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Abstract

Due to the computational power and memory of modern computers, computer vision techniques and neural networks can be used to develop a visual inspection system of agricultural products to satisfy product quality requirements. This chapter employs artificial vision techniques to classify seeds in RGB images. As a first step, an algorithm based on pixel intensity threshold is developed to detect and classify a set of different seed types, such as rice, beans, and lentils. Then, the information inferred by this algorithm is exploited to develop a neural network model, which successfully achieves learning classification and detection tasks through a semantic-segmentation scheme. The applicability and satisfactory performance of the proposed algorithms are illustrated by testing with real images, achieving an average accuracy of 92% in the selected set of classes. The experimental results verify that both algorithms can directly detect and classify the proposed set of seeds in input RGB images.

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Metadata
Title
Design of a Segmentation and Classification System for Seed Detection Based on Pixel Intensity Thresholds and Convolutional Neural Networks
Authors
Oscar J. Suarez
Edgar Macias-Garcia
Carlos J. Vega
Yersica C. Peñaloza
Nicolás Hernández Díaz
Victor M. Garrido
Copyright Year
2023
Book
Applications of Computational Intelligence

Print ISBN: 978-3-031-29782-3

Electronic ISBN: 978-3-031-29783-0

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-29783-0_1

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