Environmental assessment of soluble solids contents and pH of orange using hyperspectral method and machine learning

被引:0
作者
Rasekh, Mansour [1 ]
Ardabili, Sina [2 ]
Mosavi, Amir [3 ,4 ]
机构
[1] Univ Mohaghegh Ardabili, Dept Biosyst Engn, Ardebil, Iran
[2] Univ Mohaghegh Ardabili, Fac Adv Technol, Dept Engn Sci, Namin, Iran
[3] Obuda Univ, John von Neumann Fac Informat, Budapest, Hungary
[4] Ludovika Univ Publ Serv, Budapest, Hungary
来源
SMART AGRICULTURAL TECHNOLOGY | 2024年 / 9卷
关键词
Environmental assessment; Machine learning; Non-destructive method; Navel orange; Life cycle assessment; Precision agriculture; Artificial intelligence; Data science; Big data; RAPID DETECTION;
D O I
10.1016/j.atech.2024.100544
中图分类号
S2 [农业工程];
学科分类号
0828 ;
摘要
Progress in non-destructive methods to detect the characteristics of fruits is a new and attractive process for researchers and specialists in this field. On the other hand, these researchers move toward identifying their impacts on their surroundings in line with diagnostic efficiency. One of these essential impacts is the environmental impact of the non-destructive detection process of fruits. Navel oranges are one of the most popular and widely consumed fruits, whose maturity indices such as soluble solids contents (SSC) values and acidity are considered as parameters in determining the quality of this product. This study used the hyperspectral method in the vis-NIR range to evaluate and measure navel oranges' SSC and acidity values. In the following, by applying the life cycle assessment method, the environmental impacts of measuring and evaluating these two parameters of the characteristics of navel oranges were investigated. The Impact2002+ method was used to evaluate the impact of the life cycle list. Based on the findings, the environmental impacts of SSC measurement are about 40, 42, 20, and 18 % higher than those of the environmental impacts of pH measurement from the point of view of endpoint impacts for Human Health, Ecosystem quality, climate change, and resources, respectively. The random forest modeling results showed a suitable and acceptable correlation and relationship (over 90 %) between the wavelengths selected from the feature selection stage and environmental impacts.
引用
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页数:9
相关论文
共 20 条
[1]   Multiyear life energy and life cycle assessment of orange production in Iran [J].
Alishah, Ali ;
Motevali, Ali ;
Tabatabaeekoloor, Reza ;
Hashemi, Seyyed Jafar .
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2019, 26 (31) :32432-32445
[2]   Biowaste-to-biomethane or biowaste-to-energy? An LCA study on anaerobic digestion of organic waste [J].
Ardolino, Filomena ;
Parrillo, Francesco ;
Arena, Umberto .
JOURNAL OF CLEANER PRODUCTION, 2018, 174 :462-476
[3]  
Bibalani GH, 2011, J MED PLANTS RES, V5, P1238
[4]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[5]   Environmental advantages of visible and near infrared spectroscopy for the prediction of intact olive ripeness [J].
Casson, Andrea ;
Beghi, Roberto ;
Giovenzana, Valentina ;
Fiorindo, Ilaria ;
Tugnolo, Alessio ;
Guidetti, Riccardo .
BIOSYSTEMS ENGINEERING, 2020, 189 :1-10
[6]   Visible Near Infrared Spectroscopy as a Green Technology: An Environmental Impact Comparative Study on Olive Oil Analyses [J].
Casson, Andrea ;
Beghi, Roberto ;
Giovenzana, Valentina ;
Fiorindo, Ilaria ;
Tugnolo, Alessio ;
Guidetti, Riccardo .
SUSTAINABILITY, 2019, 11 (09)
[7]   Image Acquisition, Preprocessing and Classification of Citrus Fruit Diseases: A Systematic Literature Review [J].
Dhiman, Poonam ;
Kaur, Amandeep ;
Balasaraswathi, V. R. ;
Gulzar, Yonis ;
Alwan, Ali A. ;
Hamid, Yasir .
SUSTAINABILITY, 2023, 15 (12)
[8]   Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins [J].
Feng, Lei ;
Wu, Baohua ;
Zhu, Susu ;
He, Yong ;
Zhang, Chu .
FRONTIERS IN NUTRITION, 2021, 8
[9]   The new international standards for life cycle assessment:: ISO 14040 and ISO 14044 [J].
Finkbeiner, M ;
Inaba, A ;
Tan, RBH ;
Christiansen, K ;
Klüppel, HJ .
INTERNATIONAL JOURNAL OF LIFE CYCLE ASSESSMENT, 2006, 11 (02) :80-85
[10]  
Gerami K., 2023, New Technol. Food Industry, V10, P203