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An efficient ANFIS based pre-harvest ripeness estimation technique for fruits

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Abstract

The ripeness estimation of fruits plays a significant role in marketing and evaluation of quality. However, due to the subjectivity and slow speed in the case of manual assessment, the agriculture industry leads to the need for automation. In this research work, an efficient ANFIS based Pre-harvest Ripeness Estimation (APRE) technique is proposed for the ripeness estimation of fruits based on color. There are three main phases of the proposed work: Data Processing, Feature Selection and Fuzzy Logic Implementation. In the first phase, the data set of images of fruits is prepared in the image acquisition phase. Then images are pre-processed to make them equal in size. In Image Segmentation phase, a fruit is extracted from its background. In the next phase, two color features: red-green color difference and red-green color ratio are calculated based on extracted RGB color attributes and R-G is chosen based on performance comparison in terms of classification accuracy. In phase three, Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized for designing and implementing the classification system which classifies the fruits into six ripeness phases. The experimental results show that the APRE performs better than SVM, Decision Tree, and KNN in terms of accuracy, precision, recall, sensitivity and F-measure.

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References

  1. Alavi N (2013) Quality determination of Mozafati dates using Mamdani fuzzy inference system. J Saudi Soc Agric Sci 12(2):137–142

    Google Scholar 

  2. Arefi A, Motlagh AM (2013) Development of an expert system based on wavelet transform and artificial neural networks for the ripe tomato harvesting robot. Aust J Crop Sci 7:699–705

    Google Scholar 

  3. Bhargava A, Bansal A (2018) Fruits and vegetables quality evaluation using computer vision: a review. J King Saud Univ - Comput Inf Sci 6:113–120

    Google Scholar 

  4. Blanes C, Cortés V, Ortiz C, Mellado M, Talens P (2015) Non-destructive assessment of mango firmness and ripeness using a robotic gripper. Food Bioprocess Technol 8(9):1914–1924

    Article  Google Scholar 

  5. Bloice MD, Stocker C, Holzinger A (2017) Augmentor: an image augmentation library for machine learning. J Open Source Softw 19(2):13–26

    Google Scholar 

  6. Capizzi G, Lo Sciuto G, Napoli C, Tramontana E, Woźniak 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 

  7. Casson A, Beghi R, Giovenzana V, Fiorindo I, Tugnolo A, Guidetti R (2020) Environmental advantages of visible and near infrared spectroscopy for the prediction of intact olive ripeness. Biosyst Eng 189:1–10

    Article  Google Scholar 

  8. Choi K, Lee G, Han YJ, Bunn JM (1995) Tomato maturity evaluation using color image analysis. Trans ASAE 38:171–176

    Article  Google Scholar 

  9. De-la-Torre M et al (2019) Multivariate analysis and machine learning for ripeness classification of cape gooseberry fruits. Processes 7(12):1–15

    Article  Google Scholar 

  10. Dhanachandra N, Manglem K, Chanu YJ (2015) Image segmentation using K -means clustering algorithm and subtractive clustering algorithm. Procedia - Procedia Comput Sci 54:764–771

    Article  Google Scholar 

  11. Du D, Wang J, Wang B, Zhu L, Hong X (2019) Ripeness prediction of postharvest kiwifruit using a MOS E-nose combined with Chemometrics

  12. El Khaled D, Novas N, Gazquez JA, Garcia RM, Manzano-Agugliaro F (2015) Fruit and vegetable quality assessment via dielectric sensing. Sensors 15(7):15363–15397

    Article  Google Scholar 

  13. El-hariri E, El-Bendary N, Fouad M, Platos J, Hassanien AE, Hussein AMM (2013) Multi-class SVM Based Classification Approach for Tomato Ripeness, In 4th Int Conf Innovations Bio-Inspired Comput Appl, pp. 13–24

  14. Eyarkai Nambi V, Thangavel K, Manickavasagan A, Shahir S (2017) Comprehensive ripeness-index for prediction of ripening level in mangoes by multivariate modelling of ripening behaviour. Int Agrophysics 31(1):35–44

    Article  Google Scholar 

  15. Eyarkai Nambi V, Thangavel K, Shahir S, Thirupathi V (2016) Comparison of various RGB image features for nondestructive prediction of ripening quality of ‘Alphonso’ mangoes for easy adoptability in machine vision applications: a multivariate approach. J Food Qual 39(6):816–825

    Article  Google Scholar 

  16. Gao W, Zhu Y, Zhang W, Zhang K, Gao H (2019) A hierarchical recurrent approach to predict scene graphs from a visual-attention-oriented perspective. Comput Intell 35(3):496–516

    Article  MathSciNet  Google Scholar 

  17. Goel N, Sehgal P (2015) Fuzzy classification of pre-harvest tomatoes for ripeness estimation - an approach based on automatic rule learning using decision tree. Appl Soft Comput J 36:45–56

    Article  Google Scholar 

  18. Gupta N, Sehgal P (2014) Adaptive segmentation technique for recognizing tomatoes at different maturity stages, In Adv Intell Syst Comput Innovation, pp. 121–131

  19. Hazarika S, Hebb RL, Rizvi SSH (2018) Prediction of ripening stage of cameo apple using Fourier-transform infrared spectroscopy. Int J Fruit Sci 18(2):188–198

    Article  Google Scholar 

  20. Hossin M, Sulaiman MN (2015) A Review on Evaluation Metrics for Data Classification Evaluations. Int J Data Min Knowl Manag Process 5:1–11

    Google Scholar 

  21. Jana S, Parekh R (2016) Intra-class recognition of fruits using color and texture features with neural classifiers. Int J Comput Appl 148(11):1–6

    Google Scholar 

  22. Li B, Emr N, Malling E, Me K (2018) Advances in Non-Destructive Early Assessment of Fruit Ripeness towards Defining Optimal Time of Harvest and Yield Prediction—A Review. Plants 7(1):3

    Article  Google Scholar 

  23. Mansor AR, Othman M, Bakar MNA (2014) Fuzzy ripening mango index using RGB colour sensor model. Res Arts, Sci Commer 5(2):101–110

    Google Scholar 

  24. McCool C, Sa I, Dayoub F, Lehnert C, Perez T, Upcroft B (2016) Visual detection of occluded crop: For automated harvesting. In: IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, pp. 2506–2512

  25. Mon KL (2014) Automatic Image Segmentation Using Edge and Marker-Controlled Watershed Transformation, In Int Conf Adv Eng Technol, pp. 100–104

  26. Naik S, Patel B (2017) Machine vision based fruit classification and grading-a review. Int J Comput Appl 170(9):975–8887

    Google Scholar 

  27. Roy P, Dutta S, Dey N, Dey G, Chakraborty S, Ray R (2014) Adaptive thresholding: A comparative study, 2014 Int Conf Control Instrumentation Commun Comput Technol ICCICCT 2014, pp. 1182–1186

  28. Rungpichayapichet P, Mahayothee B, Nagle M, Khuwijitjaru P, Müller J (2016) Robust NIRS models for non-destructive prediction of postharvest fruit ripeness and quality in mango. Postharvest Biol Technol 111:31–40

    Article  Google Scholar 

  29. Sa I, Ge Z, Dayoub F, Upcroft B, Perez T, McCool C (2016) Deepfruits: A fruit detection system using deep neural networks, Sensors, vol. 16, no. 8

  30. Santos Pereira LF, Barbon S, Valous NA, Barbin DF (2018) Predicting the ripening of papaya fruit with digital imaging and random forests. Comput Electron Agric 145(8):76–82

    Article  Google Scholar 

  31. Semary NA, Tharwat A, Elhariri E, Hassanien AE (2015) Fruit-based tomato grading system using features fusion and support vector machine. In: Filev D, Jabłkowski J, Kacprzyk J, Krawczak M, Popchev I, Rutkowski L, Sgurev V, Sotirova E, Szynkarczyk P, Zadrozny S (eds) Intelligent Systems. Springer International Publishing, Cham, pp 401–410

    Google Scholar 

  32. Taghadomi-Saberi S, Omid M, Emam-Djomeh Z, Faraji-Mahyari KH (2015) Determination of cherry color parameters during ripening by artificial neural network assisted image processing technique. J Agric Sci Technol 17(3):589–600

    Google Scholar 

  33. Taofik A, Ismail N, Gerhana YA, Komarujaman K, Ramdhani MA (2018) Design of Smart System to Detect Ripeness of Tomato and Chili with New Approach in Data Acquisition, IOP Conf Ser Mater Sci Eng, vol. 288, no. 1

  34. Wang X, Mao H, Han X, Yin J (2011) Vision-based judgment of tomato maturity under growth conditions. Afr J Biotechnol 10(18):3616–3623

    Google Scholar 

  35. Xiang R, Ying Y, Jiang H (2011) Research on image segmentation methods of tomato in natural conditions. Proceedings - 4th International Congress on Image and Signal Processing, CISP 2011. 3.

  36. Yin H, Chai Y, Yang SX, Mittal GS (2009) Ripe tomato extraction for a harvesting robotic system, In IEEE Int Conf Syst Man Cybern, pp. 2984–2989

  37. Yin J, Mao H, Hu Y, Wang X, Chen S (2006) An automatic segmentation method for multi-tomatoes image under complicated natural background, In Proc Agric Hydrol Appl Remote Sens, pp. 27–36

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Authors and Affiliations

  1. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Shubhdeep Kaur, Sukhchandan Randhawa & Avleen Malhi

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  1. Shubhdeep Kaur
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  2. Sukhchandan Randhawa
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  3. Avleen Malhi
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Correspondence to Sukhchandan Randhawa.

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Kaur, S., Randhawa, S. & Malhi, A. An efficient ANFIS based pre-harvest ripeness estimation technique for fruits. Multimed Tools Appl 80, 19459–19489 (2021). https://doi.org/10.1007/s11042-021-10741-2

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  • DOI: https://doi.org/10.1007/s11042-021-10741-2

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