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

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.

• Autores: Suarez, Oscar J.; Macias-Garcia, Edgar; Vega, Carlos J.; Peñaloza, Yersica C.; Hernández Díaz, Nicolás; Garrido, Victor M.
• Fecha de publicación: 2023
• Institución: Universidad Tecnológica de Bolívar
• Repositorio: Repositorio Institucional UTB
• Idioma: eng
• OAI Identifier: oai:repositorio.utb.edu.co:20.500.12585/12322
• Acceso en línea: https://hdl.handle.net/20.500.12585/12322
• Palabra clave: Object Detection; Deep Learning; IOU; LEMB
• Rights: openAccess
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/