Design of a Segmentation and Classification System for Seed Detection Based on Pixel Intensity Thresholds and Convolutional Neural Networks
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Date
2023
Authors
Suarez, Oscar J.
Macias-Garcia, Edgar
Vega, Carlos J.
Peñaloza, Yersica C.
Hernández Díaz, Nicolás
Garrido, Victor M.
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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. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Suarez, O. J., Macias-Garcia, E., Vega, C. J., Peñaloza, Y. C., Díaz, N. H., & Garrido, V. M. (2022, July). Design of a Segmentation and Classification System for Seed Detection Based on Pixel Intensity Thresholds and Convolutional Neural Networks. In IEEE Colombian Conference on Applications of Computational Intelligence (pp. 1-17). Cham: Springer Nature Switzerland.