Swine live weight estimation by adaptive neuro-fuzzy inference system

被引：5
|
作者：
Okinda, Cedric [1 ]
Liu, Longhen [1 ]
Zhang, Guangyue [1 ]
Shen, Mingxia [1 ]
机构：
[1] Nanjing Agr Univ, Coll Engn, Nanjing 210031, Jiangsu, Peoples R China
来源：
INDIAN JOURNAL OF ANIMAL RESEARCH | 2018年 / 52卷 / 06期
关键词：
Adaptive Neuro-Fuzzy Inference System; Contactless; Features; Modelling; Predictive Power; GROWING-FINISHING PIGS; DIGITAL IMAGE-ANALYSIS; SIZE; PERFORMANCE; PREDICTION; GROWTH; SHEEP; WALK; COWS;
D O I：
10.18805/ijar.v0iOF.7250
中图分类号：
S8 [畜牧、 动物医学、狩猎、蚕、蜂];
学科分类号：
0905 ;
摘要：
Swine live weight is an important aspect in the production of pork products and also to Stockmen, with reference to market costs, feed conversion, and animal health. The objective of this study was to develop a contactless, stress-free method of swine live weight estimation by machine vision technology. This novel approach was based on image processing for features extraction and Adaptive Neuro-Fuzzy Inference System (ANFIS) for modelling. Firstly, the model determines which input combination holds the highest predictive ability, secondly, used the feature combination with the best predictive power to correlate to live-weight. The test results showed that the average relative error of our proposed system was about 3% and a standard deviation of 0.7%. Thus, development of a practical imaging system for swine live weight estimation by the proposed method is feasible.
引用
收藏
页码：923 / 928
页数：6
相关论文
共 50 条
  • [1] Adaptive Neuro-fuzzy Inference system into Induction Motor : Estimation
    Boussada, Zina
    Ben Hamed, Mouna
    Sbita, Lassaad
    [J]. 2014 INTERNATIONAL CONFERENCE ON ELECTRICAL SCIENCES AND TECHNOLOGIES IN MAGHREB (CISTEM), 2014,
  • [2] Illuminant Estimation Using Adaptive Neuro-Fuzzy Inference System
    Luo, Yunhui
    Wang, Xingguang
    Wang, Qing
    Chen, Yehong
    [J]. APPLIED SCIENCES-BASEL, 2021, 11 (21):
  • [3] Channel estimation based on adaptive neuro-fuzzy inference system in OFDM
    Seyman, M. Nuri
    Taspinar, Necmi
    [J]. IEICE TRANSACTIONS ON COMMUNICATIONS, 2008, E91B (07) : 2426 - 2430
  • [4] Design of Adaptive Neuro-Fuzzy Inference System for Estimation of Grinding Performance
    Baseri, H.
    [J]. MATERIALS AND MANUFACTURING PROCESSES, 2011, 26 (05) : 757 - 763
  • [5] Runoff estimation using modified adaptive neuro-fuzzy inference system
    Nath, Amitabha
    Mthethwa, Fisokuhle
    Saha, Goutam
    [J]. ENVIRONMENTAL ENGINEERING RESEARCH, 2020, 25 (04) : 545 - 553
  • [6] Unsaturated soils permeability estimation by adaptive neuro-fuzzy inference system
    Jokar, Mehdi Hashemi
    Khosravi, Abdolkarim
    Heidaripanah, Ali
    Soltani, Fazlollah
    [J]. SOFT COMPUTING, 2019, 23 (16) : 6871 - 6881
  • [7] State of charge estimation based on adaptive neuro-fuzzy inference system
    Guan Jiansheng
    Xu Wenjin
    Zhang Abu
    [J]. ICCSE'2006: Proceedings of the First International Conference on Computer Science & Education: ADVANCED COMPUTER TECHNOLOGY, NEW EDUCATION, 2006, : 840 - 843
  • [8] Unsaturated soils permeability estimation by adaptive neuro-fuzzy inference system
    Mehdi Hashemi Jokar
    Abdolkarim Khosravi
    Ali Heidaripanah
    Fazlollah Soltani
    [J]. Soft Computing, 2019, 23 : 6871 - 6881
  • [9] Implementation of Adaptive Neuro-Fuzzy Inference System in Fault Location Estimation
    Abdullah, Amalina
    Banmongkol, Channarong
    Hoonchareon, Naebboon
    Hidaka, Kunihiko
    [J]. 9TH INTERNATIONAL CONFERENCE ON ROBOTIC, VISION, SIGNAL PROCESSING AND POWER APPLICATIONS: EMPOWERING RESEARCH AND INNOVATION, 2017, 398 : 737 - 748
  • [10] Improved adaptive neuro-fuzzy inference system
    Benmiloud, Tarek
    [J]. NEURAL COMPUTING & APPLICATIONS, 2012, 21 (03): : 575 - 582
12345