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A general purpose multi-fruit system for assessing the quality of fruits with the application of recurrent neural network

  • Data analytics and machine learning
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Abstract

In the industry of agricultural farming, defected fruits are the major reason for financial calamities across the globe. It affects both the quality and competence of the fruits. Quality detection is a post-harvest process that requires highly skilled labor and time. Therefore, the automatic detection of quality of fruits is an important step in the harvesting process which helps to save the man power and time consumption. Different systems have been designed using image processing and learning techniques that detect the quality of fruits and classify them. To speed up the fruit sorting process, a system has been designed using machine learning and deep learning techniques. The proposed system works with total nine types of fruits: apple, banana, pear, guava, grape, mango, pomegranate, orange and tomato. Recurrent neural network is used as a proposed deep learning classifier which is trained using good and bad extracted features through implementation of principal component analysis. A simple contrast enhancement technique, preceded by grayscale conversion, has been used for balancing the unstable light in the input fruit image that can suppress the object definition. In the segmentation phase, canny edge detection is used for discovering the boundaries of the fruits. The comparative analysis with existing multi-fruit or single-fruit systems clearly shows that the proposed general purpose system overshadows them by achieving better accuracy (98.47%), precision (98.93%), recall (75.44%) and mean square error (1.53%) values.

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References

  • Anushya A (2020) Quality recognition of banana images using classifiers. Int J Comput Sci Mob Comput 9(1):100–106

    Google Scholar 

  • Ashok V, Vinod D (2014) Automatic quality evaluation of fruits using probabilistic neural network approach. In: Proceedings of the international conference on contemporary computing and informatics, pp 308–311

  • Azizah LM, Umayah SF, Riyadi S, Damarjati C, Utama NA (2017) Deep learning implementation using convolutional neural network in mangosteen surface defect detection. In: Proceedings of the 7th IEEE international conference on control system, computing and engineering, pp 242–246

  • Bai Z, Li Y, Wozniak M, Zhou M, Li D (2020) DecomVQANet: decomposing visual question answering deep network via tensor decomposition and regression. Pattern Recognit 110:107538

    Article  Google Scholar 

  • Bargoti S, Underwood JP (2017) Image segmentation for fruit detection and yield estimation in apple orchards. J Field Robot 34(6):1039–1060

    Article  Google Scholar 

  • Bhargava A, Bansal A (2020) Quality evaluation of mono and bi-colored apples with computer vision and multispectral imaging. Multimed Tools Appl 79:7857–7874

    Article  Google Scholar 

  • Capizzi G, Sciuot GL, Napoli C, Tramontana E, Wozniac M (2016) A novel neural networks-based texture image processing algorithm for orange defects classification. Int J Comput Sci Appl 13(2):45–60

    Google Scholar 

  • Cavallo DP, Cefola M, Pace B, Logrieco AF, Attolico G (2019) Non-destructive and contactless quality evaluation of table grapes by a computer vision system. Comput Electron Agric 156:558–564

    Article  Google Scholar 

  • Chen SW, Shivakumar SS, Dcunha S, Das J, Okon E, QuC TCJ, Kumar V (2017) Counting apples and oranges with deep learning: a data-driven approach. IEEE Robot Autom Lett 2(2):781–788

    Article  Google Scholar 

  • Chithra P, Henila M (2020) Apple fruit sorting using novel thresholding and area calculation algorithms. Soft Comput 25(1):431–445

    Article  Google Scholar 

  • Costa AZD, Figueroa HE, Fracarolli JA (2020) Computer vision based detection of external defects on tomatoes using deep learning. Biosyst Eng 190:131–144

    Article  Google Scholar 

  • Dhakate M, Ingole AB (2015) Diagnosis of pomegranate plant diseases using neural network. In: Proceedings of the 5th national conference on computer vision, pattern recognition, image processing and graphics, pp 1–4

  • Dubey SR, Jalal AS (2015a) Apple disease classification using color, texture and shape features from images. SIViP 10(5):819–826

    Article  Google Scholar 

  • Dubey SR, Jalal AS (2015b) Application of image processing in fruit and vegetable analysis: a review. J Intell Syst 24(4):1–20

    Google Scholar 

  • Gao H, Zhu F, Cai J (2010) A review of non-destructive detection for fruit quality. Comput Comput Technol Agric III IFIP Adv Inf Commun Technol 317:133–140

    Google Scholar 

  • Gómez-Sanchis J, Martín-Guerrero JD, Soria-Olivas E, Martínez-Sober M, Magdalena-Benedito R, Blasco J (2012) Detecting rottenness caused by Penicillium genus fungi in citrus fruits using machine learning techniques. Expert Syst Appl 39(1):780–785

    Article  Google Scholar 

  • Guo L, Wozniak M (2020) An image super-resolution reconstruction method with single frame character based on wavelet neural network in internet of things. Mob Netw Appl 26(1):390–403

    Article  Google Scholar 

  • Itakura K, Saito Y, Suzuki T, Kondo N, Hosoi F (2019) Estimation of citrus maturity with fluorescence spectroscopy using deep learning. Horticulturae 5(1):2. https://doi.org/10.3390/horticulturae5010002

    Article  Google Scholar 

  • Jawale D, Deshmukh M (2017) Real time automatic bruise detection in (Apple) fruits using thermal camera. In: Proceedings of the international conference on communication and signal processing, pp 1080–1085

  • Kanakaraddi S, Iliger P, Gaonkar A, Alagoudar M, Prakash A (2014) Analysis and grading of pathogenic disease of chili fruit using image processing. J Intell Syst 24(4):46–50

    Google Scholar 

  • Keresztes B, Abdelghafour F, Randriamanga D, Costa JPD, Germain C (2018) Real-time fruit detection using deep neural networks. In: Proceedings of the 14th international conference on precision agriculture, pp 1–10

  • Kestur R, Meduri A, Narasipura O (2019) MangoNet: a deep semantic segmentation architecture for a method to detect and count mangoes in an open orchard. Eng Appl Artif Intell 77:59–69

    Article  Google Scholar 

  • Khan MA, Akram T, Sharif M, Awais M, Javed K, Ali H, Saba T (2018) CCDF: automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features. Comput Electron Agric 155:220–236

    Article  Google Scholar 

  • Khoje SA, Bodhe SK, Adsul A (2013) Automated skin defect identification system for fruit grading based on discrete curvelet transform. Int J Eng Technol 5(4):3251–3256

    Google Scholar 

  • Kumar Y, Kaur K, Singh G (2020) Machine learning aspects and its applications towards different research areas. In: Proceedings of international conference on computation, automation and knowledge management (ICCAKM), pp 150–156

  • Lihuan S, Liu W, Xiaohong Z, Guohua H, Zhidong Z (2017) Fabrication of electronic nose system and exploration on its applications in mango fruit (M. indica cv. Datainong) quality rapid determination. J Food Meas Charact 11(4):1969–1977

    Article  Google Scholar 

  • Lu H, Zheng H, Hu Y, Lou H, Kong X (2011) Bruise detection on red bayberry (Myrica rubra Sieb. & Zucc.) using fractal analysis and support vector machines. J Food Eng 104(1):149–153

    Article  Google Scholar 

  • Mureşan H, Oltean M (2018) Fruit recognition from images using deep learning. Acta Univ Sapientiae Inform 10(1):26–42

    Article  Google Scholar 

  • Naik S (2019) Non-destructive Mango (Mangifera indica L., CV. Kesar) grading using convolutional neural network and support vector machine. In: Proceedings of the international conference on sustainable computing in science, technology and management, pp 670–678

  • Nandi CS, Tudu B, Koley C (2016) A machine vision technique for grading of harvested mangoes based on maturity and quality. IEEE Sens J 16(16):6387–6396

    Article  Google Scholar 

  • Ohali YA (2010) Computer vision based date fruit grading system: design and implementation. J King Saud Univ Comput Inf Sci 23(1):29–36

    Google Scholar 

  • Pandey R, Naik S, Marfatia R (2013) Image processing and machine learning for automated fruit grading system: a technical review. Int J Comput Appl 81(16):29–39

    Google Scholar 

  • Pereira LFS, Barbon S, Valous NA, Barbin DF (2018) Predicting the ripening of papaya fruit with digital imaging and random forests. Comput Electron Agric 145:76–82

    Article  Google Scholar 

  • Pujari JD, Yakkundimath R, Byadgi AS (2014) Recognition and classification of produce affected by identically looking Powdery Mildew disease. Acta Technol Agric 17(2):29–34

    Google Scholar 

  • Rivera NV, Gómez-Sanchis J, Chanona-Pérez J, Carrasco JJ, Millán-Giraldo M, Lorente D, Cubero S, Blasco J (2014) Early detection of mechanical damage in mango using NIR hyperspectral images and machine learning. Biosyst Eng 122:91–98

    Article  Google Scholar 

  • Rokunuzzaman M, Jayasuriya HPW (2013) Development of a low-cost machine vision system for sorting of tomatoes. Agric Eng Int CIGR J 15(1):173–180

    Google Scholar 

  • Sa I, Ge Z, Dayoub F, Upcroft B, Perez T, Mccool C (2016) DeepFruits: a fruit detection system using deep neural networks. Sensors 16(8):1–23

    Article  Google Scholar 

  • Sangeetha M, Kumaran MS (2020) Deep learning-based data imputation on time-variant data using recurrent neural network. Soft Comput 24:13369–13380

    Article  Google Scholar 

  • Saranya CP, Nagarajan N (2020) Efficient agricultural yield prediction using metaheuristic optimized artificial neural network using Hadoop framework. Soft Comput 24:12659–12669

    Article  Google Scholar 

  • Sendin K, Manley M, Williams PJ (2018) Classification of white maize defects with multispectral imaging. Food Chem 243:311–318

    Article  Google Scholar 

  • Varjão JOR, Botelho GM, Almeida TDS, Martins GADS, Silva WGD (2019) Citrus fruit quality classification using support vector machines. Int J Adv Eng Res Sci 6(7):59–65

    Article  Google Scholar 

  • Vasumathi MT, Kamarasan A (2019) Fruit disease prediction using machine learning over big data. Int J Recent Technol Eng 7(6S5):556–559

    Google Scholar 

  • Vaviya H, Vishwakarma V, Yadav A, Shah N (2019) Identification of artificially ripened fruits using machine learning. In: Proceedings of 2nd international conference on advances in science and technology, pp 1–6

  • Wang L, Li A, Tian X (2013) Detection of fruit skin defects using machine vision system. In: Proceedings of the 6th international conference on business intelligence and financial engineering, pp 44–48

  • Wozniak M, Polap D (2018) Adaptive neuro-heuristic hybrid model for fruit peel defects detection. Neural Netw 98:16–33

    Article  Google Scholar 

  • Wozniak M, Silka J, Wieczorek M, Alrashoud M (2020) Recurrent neural network model for IoT and networking malware threads detection. IEEE Trans Ind Inform 1:5583–5594

    Google Scholar 

  • Wu A, Zhu J, Ren T (2020) Detection of apple defect using laser-induced light backscattering imaging and convolutional neural network. Comput Electr Eng 81:1–9

    Article  Google Scholar 

  • Zaborowicz M, Boniecki P, Koszela K, Przybylak A, Przybył J (2017) Application of neural image analysis in evaluating the quality of greenhouse tomatoes. Sci Hortic 218:222–229

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Chandigarh Engineering College, Chandigarh Group of Colleges, Landran, Mohali, 140307, India

    Bhumica Dhiman & Yogesh Kumar

  2. Department of Computer Science and Information Management, Providence University, 200, Sec. 7, Taiwan Boulevard, Shalu Dist., Taichung City, 43301, Taiwan, R.O.C.

    Yu-Chen Hu

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  1. Bhumica Dhiman
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  2. Yogesh Kumar
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  3. Yu-Chen Hu
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Correspondence to Yu-Chen Hu.

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Dhiman, B., Kumar, Y. & Hu, YC. A general purpose multi-fruit system for assessing the quality of fruits with the application of recurrent neural network. Soft Comput 25, 9255–9272 (2021). https://doi.org/10.1007/s00500-021-05867-2

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