Fractal image analysis and bruise damage evaluation of impact damage in guava

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY Information Processing in Agriculture Pub Date : 2023-02-14 DOI:10.1016/j.inpa.2023.02.004
Than Htike , Rattaporn Saengrayap , Hiroaki Kitazawa , Saowapa Chaiwong
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引用次数: 0

Abstract

Impact bruise damage and quality of ‘Gim Ju’ guava were investigated for different drop heights and number of drops using fractal image analysis. For the impact test, a stainless-steel metal ball (250 g) was dropped on fruit from three drop heights (0, 0.3, 0.6 m) either once or five times. Fruit quality was evaluated for impact energy, bruise area (BA), bruise volume (BV), bruise susceptibility, bruise score and pulp color (L*, a*, b* and C values). The fractal dimension (FD) value using fractal image analysis was analyzed at the bruise region. Results showed that five drops (0.3 m) with a high impact energy (3 678.75 J) and a single drop (0.6 m) with a low impact energy (1 471.50 J) exhibited no significant in BA, BV, bruise score as well as all color values (L*, a*, b* and C). While the FD value of a single drop from 0.6 m had a higher FD value than that of five drops from 0.3 m. It is indicated that FD exhibited a better performance to classify impact bruising level of guava than BA, BV and color parameters. The FD value gradually decreased with increase of storage time and bruise severity. The correlation coefficient (r) values of FD (r =  − 0.794 and − 0.745) between BA and BV were more significant than those L* (r =  − 0.660 and − 0.615) and a* (r = 0.579 and 0.473). The coefficient of determination (R2) of the polynomial equation in bruised fruit (R2 = 0.85 to 0.99) was greater than the control (no bruise) (R2 = 0.80). A higher R2val (0.88 and 0.92) was exhibited at five drops. Interestingly, FD analysis showed greater potential than color measurement to assess bruise impact damage in guava.

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番石榴撞击损伤的分形图像分析和挫伤损伤评估
利用分形图像分析法研究了不同下落高度和下落次数对 "Gim Ju "番石榴造成的撞击淤伤和质量影响。在冲击试验中,将一个不锈钢金属球(250 克)从三个高度(0、0.3、0.6 米)落在果实上,一次或五次。对果实质量的评估包括冲击能量、瘀伤面积(BA)、瘀伤体积(BV)、瘀伤敏感性、瘀伤评分和果肉颜色(L*、a*、b* 和 C 值)。利用分形图像分析法对瘀伤区域的分形维度(FD)值进行了分析。结果表明,高冲击能量(3 678.75 焦耳)的五滴(0.3 米)和低冲击能量(1 471.50 焦耳)的单滴(0.6 米)在 BA、BV、瘀伤评分以及所有颜色值(L*、a*、b* 和 C)方面均无显著差异。而从 0.6 米处滴下一滴的 FD 值要高于从 0.3 米处滴下五滴的 FD 值。随着贮藏时间和瘀伤严重程度的增加,FD 值逐渐降低。FD 与 BA 和 BV 的相关系数(r)值(r = - 0.794 和 - 0.745)比 L* (r = - 0.660 和 - 0.615)和 a* (r = 0.579 和 0.473)更显著。青果多项式方程的判定系数(R2)(R2 = 0.85 至 0.99)大于对照组(无青果)(R2 = 0.80)。五滴时的 R2 值(0.88 和 0.92)更高。有趣的是,在评估番石榴淤伤影响损害方面,FD 分析比颜色测量更有潜力。
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来源期刊
Information Processing in Agriculture
Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
21.10
自引率
0.00%
发文量
80
期刊介绍: Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining
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