Kernel-like impurity detection according to colour band spectral image using GA/SVM

Chen, F.; Chen, P.; Li, D.; Fang, Cheng

doi:10.1179/1743131x15y.0000000022

Cited by 2 publications

(1 citation statement)

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“…The results show that the designed Wheat-V2 model can recognize the impurities in wheat images effectively with high recognition average accuracy of 98.07%. The research by Chen et al [77] uses hybrid method of Genetic Algorithm and Support Vector Machine (SVM) to detect kernel-like impurities (KLIs) for wheat quality evaluation. The images of kernels were acquired using a machine vision with a linear color charged coupled device (CCD).…”

Section: E Agriculturementioning

confidence: 99%

Application of Artificial Intelligence in Particle and Impurity Detection and Removal: A Survey

Shuyuti,

Salami,

Dahari

et al. 2024

IEEE Access

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With the rapid development of artificial intelligence (AI), especially in machine learning and deep learning technologies, the particle and impurity detection and removal processes employed many industries have been improved. Particles and impurities at any size, shape and at any condition can be detected using the advanced technology in both areas. This paper presents a comprehensive overview of research papers that discuss the application of AI techniques for the detection and removal of particles and impurities. The publications featured in this review were mainly retrieved from the Web of Science (WoS) database, covering the timeframe from 2000 to 2023. This paper also covers the review on the impurity detection and removal specifically in edible bird's nest (EBN). The aim of this paper is to provide a valuable resource for the future development of AI applications in particle and impurity detection and removal technologies that have not been addressed in this study. Through the review and analysis of AI for particle and impurity detection and removal techniques in recent years, this paper includes the following parts: research trend in particle and impurity detection in general and AI in particle and impurity detection, applications of AI methods in particle and impurity detection in related industries including in EBN and AI application in particle and impurity removal. This review study will offer advantages to researchers engaged in the field of AI with regards to the detection and removal of particles and impurities.

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Section: E Agriculturementioning

confidence: 99%