Version 1
: Received: 1 June 2023 / Approved: 2 June 2023 / Online: 2 June 2023 (04:19:23 CEST)
How to cite:
Chen, S.; Kuo, Y.; Zhang, R.; Cheng, T.; Chu, P.; Kang, L.; Lin, C. The Development of Peanut Defect Detection Technique Using Hyperspectral Images. Preprints2023, 2023060125. https://doi.org/10.20944/preprints202306.0125.v1
Chen, S.; Kuo, Y.; Zhang, R.; Cheng, T.; Chu, P.; Kang, L.; Lin, C. The Development of Peanut Defect Detection Technique Using Hyperspectral Images. Preprints 2023, 2023060125. https://doi.org/10.20944/preprints202306.0125.v1
Chen, S.; Kuo, Y.; Zhang, R.; Cheng, T.; Chu, P.; Kang, L.; Lin, C. The Development of Peanut Defect Detection Technique Using Hyperspectral Images. Preprints2023, 2023060125. https://doi.org/10.20944/preprints202306.0125.v1
APA Style
Chen, S., Kuo, Y., Zhang, R., Cheng, T., Chu, P., Kang, L., & Lin, C. (2023). The Development of Peanut Defect Detection Technique Using Hyperspectral Images. Preprints. https://doi.org/10.20944/preprints202306.0125.v1
Chicago/Turabian Style
Chen, S., Li-Wei Kang and Chinsu Lin. 2023 "The Development of Peanut Defect Detection Technique Using Hyperspectral Images" Preprints. https://doi.org/10.20944/preprints202306.0125.v1
Abstract
In Taiwan, peanuts are classified by hand. The kernel quality is judged based on the appearance of peanuts. The identification takes considerable time and labor. Fatigue can induce misrecogni-tion or grade cognition instability due. Peanut defects are generally divided into health, underde-velopment, insect bite, and rupture. This study employed two imaging methods push-boom FX10 and Snapshot. Deep learning was used to detect peanut defects. A push-broom instrument was used for testing. There were 1,560 peanuts, including 240 good and 240 bad ones. There were a total of 1,080 good peanuts and bad peanuts in the test set data, the band selection was used, and a CNN model was built. Based on the results from three methods, 3DCNN could classify with 97%accuracy. The Snapshot was used to achieve the real-time design of a lightweight CNN model. Finally, five bands were selected using PCA, and the screening could be accurate and efficient. The maximum overall accuracy was about 98.5%. Kappa was 97.3% with real-time recognition. This will be a major advantage in the future practical application and commercialization processes. This technique can reduce labor costs, achieve intelligent detection and intelligent grading, and bring breakthroughs to smart agriculture for other crops as well.
Keywords
Hyperspectral; Deep Learning; Peanuts; Band selection
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
