An intelligent cocoa quality testing framework based on deep learning techniques

被引：0
|
作者：
Essah R. [1 ,3 ]
Anand D. [2 ]
Singh S. [1 ,3 ]
机构：
[1] Chandigarh University, Department of Computer Science.
[2] Sir Padampat Singhania University, Department of CSE, Udaipur
[3] Chandigarh University, Department of Computer Science
来源：
Measurement: Sensors | 2022年 / 24卷
关键词：
CNN; Cocoa beans; Deep learning; DNN and Transfer learning; Fermentation; Harvesting;
D O I：
10.1016/j.measen.2022.100466
中图分类号：
学科分类号：
摘要：
The export of cocoa beans is economically significant in Ghana and numerous other tropical nations. Before the cocoa beans can be processed into finished goods, they must undergo a post-harvest treatment. Fermentation is one of the most crucial steps in this process because it improves the final product's quality. Traditionally, a cut-test is used to evaluate the degree of cocoa fermentation for quality control purposes. Nonetheless, this strategy is subjective and has a number of limitations. In this paper, an attempt has been made to propose a quality testing framework for cocoa beans using deep learning techniques. There are various deep learning techniques that are used by various authors, such as CNN and DNN. Here, we use the transfer learning method to check the quality of cocoa beans. The proposed mechanism is implemented using Python in the Jupiter Notebook. © 2022 The Authors
引用
收藏
相关论文
共 50 条
  • [21] INNES: An intelligent network penetration testing model based on deep reinforcement learning
    Qianyu Li
    Miao Hu
    Hao Hao
    Min Zhang
    Yang Li
    Applied Intelligence, 2023, 53 : 27110 - 27127
  • [22] Intelligent damage recognition of composite materials based on deep learning and ultrasonic testing
    Li, Caizhi
    He, Weifeng
    Nie, Xiangfan
    Wei, Xiaolong
    Guo, Hanyi
    Wu, Xin
    Xu, Haojun
    Zhang, Tiejun
    Liu, Xinyu
    AIP ADVANCES, 2021, 11 (12)
  • [23] DEVIATE: A Deep Learning Variance Testing Framework
    Pham, Hung Viet
    Kim, Mijung
    Tan, Lin
    Yu, Yaoliang
    Nagappan, Nachiappan
    2021 36TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING ASE 2021, 2021, : 1286 - 1290
  • [24] Validating a Deep Learning Framework by Metamorphic Testing
    Ding, Junhua
    Kang, Xiaojun
    Hu, Xin-Hua
    2017 IEEE/ACM 2ND INTERNATIONAL WORKSHOP ON METAMORPHIC TESTING (MET 2017), 2017, : 28 - 34
  • [25] An Intelligent Edge-as-a-Service Framework to Combat COVID-19 Using Deep Learning Techniques
    Hassan, Mohammad Mehedi
    AlRakhami, Mabrook S.
    Alabrah, Amerah A.
    AlQahtani, Salman A.
    MATHEMATICS, 2023, 11 (05)
  • [26] A Deep Learning-Based Intelligent Quality Detection Model for Machine Translation
    Chen, Meijuan
    IEEE ACCESS, 2023, 11 : 89469 - 89477
  • [27] Wood Quality Defect Detection Based on Deep Learning and Multicriteria Framework
    Sun, Ping'an
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2022, 2022
  • [28] HDQGF:Heterogeneous Data Quality Guarantee Framework Based on Deep Learning
    Zhang, Yun
    Jin, Zongze
    Zhu, Weilin
    Chi, Lei
    Wang, Weiping
    PROCEEDINGS OF THE 2021 IEEE 24TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK IN DESIGN (CSCWD), 2021, : 901 - 906
  • [29] Intelligent Resource Allocation Method for Wireless Communication Networks Based on Deep Learning Techniques
    Hui, Hancheng
    JOURNAL OF SENSORS, 2021, 2021
  • [30] Posting Techniques in Indoor Environments Based on Deep Learning for Intelligent Building Lighting System
    Lin, Xiaoping
    Duan, Peiyong
    Zheng, Yuanjie
    Cai, Wenjian
    Zhang, Xin
    IEEE ACCESS, 2020, 8 (08): : 13674 - 13682
12345