Abstract
The classification probability output based on machine vision and near infrared spectroscopy technology was established, and the classification decision model suitable for heterogeneous data was built by using DS evidence fusion rules. The directional gradient histogram and principal component extraction method were used to extract spectral features, and support vector machine and AdaBoost classifier were used for recognition. On this basis, the potato classification model based on feature layer fusion was constructed. By using multi-source information fusion technology, a multi-source information fusion agricultural product quality identification model was established, which integrated the classification decision of nondestructive testing and feature level fusion. The simulation results showed that, compared with the single identification model, the recognition rate of multi-source information fusion identification model was improved by 12.7%~30.2%, and reached more over 95.7%.
Publication Date
5-28-2021
First Page
139
Last Page
143
DOI
10.13652/j.issn.1003-5788.2021.05.026
Recommended Citation
Xue-jun, LI and Hong, CHENG
(2021)
"Coupling method of near infrared spectroscopy and multi source information for potato quality identification,"
Food and Machinery: Vol. 37:
Iss.
5, Article 26.
DOI: 10.13652/j.issn.1003-5788.2021.05.026
Available at:
https://www.ifoodmm.cn/journal/vol37/iss5/26
References
[1] 赵利平, 吴德刚. 基于小波与模糊相融合的苹果分级算法[J]. 食品与机械, 2020, 36（4）: 142-145.
[2] 王立扬, 张瑜, 沈群, 等. 基于改进型LeNet-5的苹果自动分级方法[J]. 中国农机化学报, 2020, 41（7）: 105-110.
[3] 樊泽泽, 柳倩, 柴洁玮, 等. 基于颜色与果径特征的苹果树果实检测与分级[J]. 计算机工程与科学, 2020, 42（9）: 1 599-1 607.
[4] 陈兵旗, 吴召恒, 李红业, 等. 机器视觉技术的农业应用研究进展[J]. 科技导报, 2018, 36（11）: 54-62.
[5] 王侨, 陈兵旗, 朱德利, 等. 基于机器视觉的定向播种用玉米种粒精选装置研究[J]. 农业机械学报, 2017, 48（2）: 27-37.
[6] 贾洪雷, 王刚, 郭明卓, 等. 基于机器视觉的玉米植株数量获取方法与试验[J]. 农业工程学报, 2015, 31（3）: 215-220.
[7] 王红军, 熊俊涛, 黎邹邹, 等. 基于机器视觉图像特征参数的马铃薯质量和形状分级方法[J]. 农业工程学报, 2016, 32（8）: 272-277.
[8] 仇裕淇, 黄振楠, 阮昭, 等. 机器视觉技术在农业生产智能化中的应用综述[J]. 机械研究与应用, 2019, 32（2）: 202-206.
[9] 贾志鑫, 傅玲琳, 杨信廷, 等. 机器视觉技术在水产食品感官检测方面的应用研究进展[J]. 机械研究与应用, 2019, 32（2）: 320-325.
[10] 陈超, 柳琦, 李钒, 等. 红外光谱技术在食品安全检测中的研究与应用[J]. 食品与机械, 2013, 29（5）: 219-224.
[11] 黄星奕, 管超, 丁然, 等. 基于嗅觉可视化和近红外光谱融合技术的海鲈鱼新鲜度评价[J]. 农业工程学报, 2015, 31（8）: 277-282.
[12] 杨智灵, 李涛, 任保增. 近红外光谱技术在食品安全检测中的最新研究进展[J]. 食品与机械, 2013, 29（5）: 237-240.
[13] 张勇, 王督, 李雪, 等. 基于近红外光谱技术的农产品产地溯源研究进展[J]. 食品安全质量检测学报, 2018, 9（23）: 6 161-6 166.
[14] 李娟, 梁漱玉. 近红外快速无损检测食用油品质的研究进展[J]. 食品与机械, 2016, 32（11）: 225-228.