Promising real-time fruit and vegetable quality detection technologies applicable to manipulator picking process

Jintao Feng, Qinyi Yang, Hao Tian, Zhipeng Wang, Shijie Tian, Huirong Xu

Abstract


In recent years, worldwide research on fruit and vegetable quality detection technology includes machine vision, spectroscopy, acoustic vibration, tactile sensors, etc. These technologies have also been gradually applied to fruit and vegetable grading and sorting lines in recent years, greatly improving the income of farmers. There have been numerous reviews of these techniques. Most of the published research on fruit and vegetable quality detection technology is still carried out in the laboratory. The emphases have been on quality feature extraction, model establishment and experimental verification. The successful application in the fruit and vegetable sorting production line proves that these studies have high application potential and value, and we look forward to the performance of these sensing technologies in the fruit and vegetable picking field. Therefore, in this paper, based on the future highly automated fruit and vegetable picking mode, we will focus on three kinds of fruit and vegetable quality detection technologies including machine vision, tactile sensor and spectroscopy, to provide some reference for future research. Since there are currently limited cases of detecting quality during the fruit and vegetable picking, experiments performed on prototypes of manipulator, or devices such as Nanocilia sensors, portable spectrometers, etc., which are compact and convenient to mount on manipulator will be reviewed. Several tables and mosaics showing the performance of the three technologies in the detection of fruit and vegetable quality over the past five years have been listed. The performance of each sensing technology was relatively satisfactory in the laboratory in general. However, in the picking scenario, there are still many challenges to be solved. Different from industrial environments, agricultural scenarios are complex and changeable. Fragile and vulnerable agricultural products pose another challenge. The development of portable devices and nanomaterials have become important breakthroughs. Optical and tactile detection methods, as well as the integration of different quality detection methods, are expected to be the trends of research and development.
Key words: fruit and vegetable quality detection; machine vision; spectroscopy; tactile sensors; picking scenario
DOI: 10.25165/j.ijabe.20241702.7678

Citation: Feng J T, Yang Q Y, Tian H, Wang Z P, Tian S J, Xu H R. Promising real-time fruit and vegetable quality detection
technologies applicable to manipulator picking process. Int J Agric & Biol Eng, 2024; 17(2): 14–26.

Keywords


fruit and vegetable quality detection; machine vision; spectroscopy; tactile sensors; picking scenario

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References


Zhang Z, Lu R. Automated infield sorting and handling of apples. In: Karkee M, Zhang Q (Ed. ). Fundamentals of agricultural and field robotics. Cham: Springer. 2021; pp.267‒295.

Pan L Q, Zhang Q, Zhang W, Sun Y, Hu P C, Tu K. Detection of cold injury in peaches by hyperspectral reflectance imaging and artificial neural network. Food Chemistry, 2016; 192: 134–141.

Zhang Z, Lu Y Z, Lu R F. Development and evaluation of an apple infield grading and sorting system. Postharvest Biology and Technology, 2021; 180: 111588.

Zhang B H, Gu B X, Tian G Z, Zhou J, Huang J C, Xiong Y J. Challenges and solutions of optical-based nondestructive quality inspection for robotic fruit and vegetable grading systems: A technical review. Trends in Food Science & Technology, 2018; 81: 213–231.

Zujevs A, Osadcuks V, Ahrendt P. Trends in robotic sensor technologies for fruit harvesting: 2010–2015. Procedia Computer Science, 2015; 77: 227–233.

Kondo N, Ting K C. Robotics for bioproduction systems. Michigan: American Society of Agricultural Engineers. 1998; 325p.

Bhargava A, Bansal A. Fruits and vegetables quality evaluation using computer vision: A review. Journal of King Saud University-Computer and Information Sciences, 2021; 33(3): 243–257.

Ding C Q, Feng Z, Wang D C, Cui D, Li W H. Acoustic vibration technology: Toward a promising fruit quality detection method. Comprehensive Reviews in Food Science and Food Safety, 2021; 20(2): 1655‒1680.

Shu F W, Lesur P, Xie Y X, Pagani A, Stricker D. SLAM in the field: An evaluation of monocular mapping and localization on challenging dynamic agricultural environment. In 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA: IEEE, 2021; pp.1760‒1770 .

Matiacevich S, Silva P, Enrione J, Osorio F. Quality assessment of blueberries by computer vision. Procedia Food Science, 2011; 1: 421–425.

Ismail N, Malik O A. Real-time visual inspection system for grading fruits using computer vision and deep learning techniques. Information Processing in Agriculture, 2022; 9(1): 24–37.

Hu Z L, Tang J S, Zhang P, Jiang J F. Deep learning for the identification of bruised apples by fusing 3D deep features for apple grading systems. Mechanical Systems and Signal Processing, 2020; 145: 106922.

Gongal A, Amatya S, Karkee M, Zhang Q, Lewis K. Sensors and systems for fruit detection and localization: A review. Computers and Electronics in Agriculture, 2015; 116: 8–19.

Ren S Q, He K M, Girshick R, Sun J. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017; 39(6): 1137–1149.

Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, et al. SSD: Single shot multibox detector. Computer Vision–ECCV2016, 2016; 9905: 21–37.

Redmon J, Farhadi A J A P A. Yolov3: An incremental improvement. arXiv e-prints, 2018; 02767.

Dubey S R, Jalal A S. Apple disease classification using color, texture and shape features from images. Signal, Image and Video Processing, 2016; 10(5): 819–826.

Moallem P, Serajoddin A, Pourghassem H. Computer vision-based apple grading for golden delicious apples based on surface features. Information Processing in Agriculture, 2017; 4(1): 33–40.

Dorj U-O, Lee M, Yun S-s. An yield estimation in citrus orchards via fruit detection and counting using image processing. Computers and Electronics in Agriculture, 2017; 140: 103–112.

Santos Pereira L F, Barbon Jr S, Valous N A, Barbin D F. Predicting the ripening of papaya fruit with digital imaging and random forests. Computers and Electronics in Agriculture, 2018; 145: 76–82.

Rong D, Rao X Q, Ying Y B. Computer vision detection of surface defect on oranges by means of a sliding comparison window local segmentation algorithm. Computers and Electronics in Agriculture, 2017; 137: 59–68.

Nandi C S, Tudu B, Koley C. A machine vision technique for grading of harvested mangoes based on maturity and quality. IEEE Sensors Journal, 2016; 16(16): 6387–6396.

Tan K, Lee W S, Gan H, Wang S W. Recognising blueberry fruit of different maturity using histogram oriented gradients and colour features in outdoor scenes. Biosystems Engineering, 2018; 176: 59–72.

Hadimani L, Garg N M. Automatic surface defects classification of Kinnow mandarins using combination of multi-feature fusion techniques. Journal of Food Process Engineering, 2021; 44(1): e13589.

Sabzi S, Abbaspour-Gilandeh Y, García-Mateos G. A new approach for visual identification of orange varieties using neural networks and metaheuristic algorithms. Information Processing in Agriculture, 2018; 5(1): 162–172.

Wan P, Toudeshki A, Tan H, Ehsani R. A methodology for fresh tomato maturity detection using computer vision. Computers and Electronics in Agriculture, 2018; 146: 43–50.

De Luna R G, Dadios E P, Bandala A A, Vicerra R R A. Tomato growth stage monitoring for smart farm using deep transfer learning with machine learning-based maturity grading. Agrivita, 2020; 42(1): 24–36.

Korchagin S A, Gataullin S T, Osipov A V, Smirnov M V, Suvorov S V, Serdechnyi D V. Development of an optimal algorithm for detecting damaged and diseased potato tubers moving along a conveyor belt using computer vision systems. Agronomy, 2021; 11(10).

Abayomi-Alli O O, Damaševičius R, Misra S, Maskeliūnas R. Cassava disease recognition from low-quality images using enhanced data augmentation model and deep learning. Expert Systems, 2021; 38(7): e12746.

Tian Y N, Yang G D, Wang Z, Li E Liang Z Z. Detection of apple lesions in orchards based on deep learning methods of CycleGAN and YOLOV3-Dense. Journal of Sensors, 2019; 2019: 7630926.

da Costa A Z, Figueroa H E H, Fracarolli J A. Computer vision based detection of external defects on tomatoes using deep learning. Biosystems Engineering, 2020; 190(C): 131–144.

Zhu L, Spachos P. Support vector machine and YOLO for a mobile food grading system. Internet of Things, 2021; 13: 100359.

Behera S K, Rath A K, Sethy P K. Maturity status classification of papaya fruits based on machine learning and transfer learning approach. Information Processing in Agriculture, 2021; 8(2): 244–250.

Zhang W Z, Tan A, Zhou G X, Chen A, Li M X, Chen X, et al. A method for classifying citrus surface defects based on machine vision. Journal of Food Measurement and Characterization, 2021; 15(3): 2877–2888.

Qiao J, Sasao A, Shibusawa S, Kondo N, Morimoto E. Mapping yield and quality using the mobile fruit grading robot. Biosystems Engineering, 2005; 90(2): 135–142.

Qiao J, Sasao A, Shibusawa S, Kondo N, Morimoto E. Mobile fruit grading robot (Part 1): Development of a robotic system for grading sweet peppers. Journal of JSAM, 2004; 66(2): 113‒122.

Bac C W, Hemming J, van Tuijl B A J, Barth R, Wais E, van Henten E J. Performance evaluation of a harvesting robot for sweet pepper. Journal of Field Robotics, 2017; 34(6): 1123–1139.

Hsieh K-W, Huang B-Y, Hsiao K-Z, Tuan Y-H, Shih F-P, Hsieh L-C, et al. Fruit maturity and location identification of beef tomato using R-CNN and binocular imaging technology. Journal of Food Measurement and Characterization, 2021; 15(6): 5170–5180.

Hayashi S, Shigematsu K, Yamamoto S, Kobayashi K, Kohno Y, Kamata J, et al. Evaluation of a strawberry-harvesting robot in a field test. Biosystems Engineering, 2010; 105(2): 160–171.

Xiong Y, Peng C, Grimstad L, From P J, Isler V. Development and field evaluation of a strawberry harvesting robot with a cable-driven gripper. Computers and Electronics in Agriculture, 2019; 157: 392–402.

Lehnert C, English A, Mccool C, McCool C, Tow A W, Perez T. Autonomous sweet pepper harvesting for protected cropping systems. IEEE Robotics and Automation Letters, 2017; 2(2): 872–879.

Yaguchi H, Nagahama K, Hasegawa T, Inaba M. Development of an autonomous tomato harvesting robot with rotational plucking gripper. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea: IEEE, 2016; pp.652‒657.

Arad B, Balendonck J, Barth R, Ben-Shahar O, Edan Y, Hellström T, et al. Development of a sweet pepper harvesting robot. Journal of Field Robotics, 2020; 37(6): 1027–1039.

Zhang Z, Igathinathane C, Li J, Chen H, Lu Y, Flores P. Technology progress in mechanical harvest of fresh market apples. Computers and Electronics in Agriculture, 2020; 175: 105606.

Jadhav T, Singh K, Abhyankar A. Volumetric estimation using 3D reconstruction method for grading of fruits. Multimedia Tools and Applications, 2019; 78(2): 1613–1634.

Reese D Y, Lefcourt A M, Kim S M, Lo Y M. Whole surface image reconstruction for machine vision inspection of fruit. In: Optics for natural resources, agriculture, and foods II, Boston, MA, United States: International Society for Optics and Photonics, 2007 .

Amirulah R. Real time visual system for starfruit maturity index classification. MS dissertation. Johor : Universiti Teknologi Malaysia, 2012; 12. 94p.

Benelli A, Cevoli C, Fabbri A. In-field hyperspectral imaging: An overview on the ground-based applications in agriculture. Journal Of Agricultural Engineering, 2020; 51(3): 129–139.

Wendel A, Underwood J, Walsh K. Maturity estimation of mangoes using hyperspectral imaging from a ground based mobile platform. Computers and Electronics in Agriculture, 2018; 155: 298–313.

Benelli A, Cevoli C, Ragni L, Fabbri A. In-field and non-destructive monitoring of grapes maturity by hyperspectral imaging. Biosystems Engineering, 2021; 207: 59–67.

Sun Y, Wang Y H, Xiao H, Gu X Z, Pan L Q, Tu Kang. Hyperspectral imaging detection of decayed honey peaches based on their chlorophyll content. Food Chemistry, 2017; 235: 194–202.

Tian X, Li J B, Wang Q Y, Fan S X, Huang W Q. A bi-layer model for nondestructive prediction of soluble solids content in apple based on reflectance spectra and peel pigments. Food Chemistry, 2018; 239: 1055–1063.

Gao Z M, Shao Y Y, Xuan G T, Wang Y X, Liu Y, Han X. Real-time hyperspectral imaging for the in-field estimation of strawberry ripeness with deep learning. Artificial Intelligence in Agriculture, 2020; 4: 31–38.

Munera S, Gómez-Sanchís J, Aleixos N, Vila-Francés J, Colelli G, Cubero S, et al. Discrimination of common defects in loquat fruit cv. ‘Algerie’ using hyperspectral imaging and machine learning techniques. Postharvest Biology and Technology, 2021; 171: 111356.

Zhang B, Xie Y, Zhou J, Wang K, Zhang Z. State-of-the-art robotic grippers, grasping and control strategies, as well as their applications in agricultural robots: A review. Computers and Electronics in Agriculture, 2020; 177: 105694.

Tian S J, Xu H R. Mechanical-based and optical-based methods for nondestructive evaluation of fruit firmness. Food Reviews International, 2023; 39(7): 4009–4039.

Li T, Sun X G, Shu X, Wang C K, Wang Y F, Chen G, et al. Robot grasping system and grasp stability prediction based on flexible tactile sensor array. Machines, 2021; 9(6): 9060119 .

Xie H, Liu H B, Luo S, Seneviratne L D, Althoefer K. Fiber optics tactile array probe for tissue palpation during minimally invasive surgery. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan: IEEE, 2013; pp.2539‒2544 .

Chen L L, Feng J Y, Li T K, Li X F, Zhang J H. High-Tactile Sensitivity of Piezoresistive Sensors With a Micro-Crack Structure Induced by Thin Film Tension. IEEE Electron Device Letters, 2019; 40(9): 1519–1521.

Krishna G M, Rajanna K. Tactile sensor based on piezoelectric resonance. IEEE Sensors Journal, 2004; 4(5): 691–697.

Hoshi T, Shinoda H. Tactile sensing using nonlinear elasticity. In SICE Annual Conference 2005, Okayama University, Japan, 2005 .

Andrussow I, Sun H B, Kuchenbecker K J J, Martius G. Minsight: A fingertip-sized vision-based tactile sensor for robotic manipulation. Advanced Intelligent Systems, 2023; 5(8): 2300042.

Blanes C, Mellado M, Beltrán P. Tactile sensing with accelerometers in prehensile grippers for robots. Mechatronics, 2016; 33: 1‒12.

Blanes C, Ortiz C, Mellado M, Beltrán P. Assessment of eggplant firmness with accelerometers on a pneumatic robot gripper. Computers and Electronics in Agriculture, 2015; 113: 44–50.

Bandyopadhyaya I, Babu D, Kumar A, Roychowdhury J. Tactile sensing based softness classification using machine learning. In 2014 IEEE International Advance Computing Conference (IACC), Gurgaon, India: IEEE, 2014; pp.1231‒1236 .

Hu G R, Zhang E Y, Zhou J G Zhao J, Gao Z N, et al. Infield apple detection and grading based on multi-feature fusion. Horticulturae, 2021; 7(9): 276 .

Spiers A J, Liarokapis M V, Calli B, Dollar A M. Single-grasp object classification and feature extraction with simple robot hands and tactile sensors. IEEE Transactions on Haptics, 2016; 9(2): 207–220.

Zhou J, Meng Y M, Wang M J, Memon M S, Yang X R, et al. Surface roughness estimation by optimal tactile features for fruits and vegetables. International Journal of Advanced Robotic Systems, 2017; 14(4): 1729881417721866.

Scimeca L, Maiolino P, Cardin-catalan D, del Pobil A P, Morales A, Iida F. Non-destructive robotic assessment of mango ripeness via multi-point soft haptics. In: 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada: IEEE, 2019; pp.1821‒1826 .

Alfadhel A, Kosel J. Magnetic nanocomposite cilia tactile sensor. Advanced Materials, 2015; 27(47): 7888–7892.

Ribeiro P, Cardoso S, Bernardino A, Jamone L. Fruit quality control by surface analysis using a bio-inspired soft tactile sensor. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA: IEEE, 2021; pp.8875‒8881.

Carvalho M, Ribeiro P, Romão V, Cardoso S. Smart fingertip sensor for food quality control: Fruit maturity assessment with a magnetic device. Journal of Magnetism and Magnetic Materials, 2021; 536: 168116.

Ward-Cherrier B, Pestell N, Cramphorn L, Winstone B, Giannaccini M E, Rossiter J, et al. The TacTip family: Soft optical tactile sensors with 3D-printed biomimetic morphologies. Soft Robotics, 2018; 5(2): 216–227.

Dong S, Yuan W Z, Adelson E H. Improved gelsight tactile sensor for measuring geometry and slip. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada: IEEE, 2017 .

Donlon E, Dong S Y, Liu M, Li J H, Adelson E, Rodriguez A. GelSlim: A high-resolution, compact, robust, and calibrated tactile-sensing finger. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, Nevada, USA: IEEE/RSJ, 2018 .

Lambeta M, Chou P W, Tian S, Yang B, Maloon B, Most V R, et al. DIGIT: A novel design for a low-cost compact high-resolution tactile sensor with application to in-hand manipulation. IEEE Robotics and Automation Letters, 2020; 5(3): 3838–3845.

Wang H, Peng J, Xie C, et al. Fruit quality evaluation using spectroscopy technology: A review. Sensors, 2015; 15(5): 11889–11927.

Wang D C, Ding C Q, Feng Z, Ji S Y, Cui D. Recent advances in portable devices for fruit firmness assessment. Critical Reviews in Food Science and Nutrition, 2023; 63(8): 1143–1154.

Fan S X, Li J B, Zhang Y H, Tian X, Wang Q Y, He X, et al. On line detection of defective apples using computer vision system combined with deep learning methods. Journal of Food Engineering, 2020; 286: 110102 .

Cortés V, Blanes C, Blasco J, Ortíz C, Aleixos N, Mellado M, et al. Integration of simultaneous tactile sensing and visible and near-infrared reflectance spectroscopy in a robot gripper for mango quality assessment. Biosystems Engineering, 2017; 162: 112–123.

Zhao M, Peng Y L, Li L. A robot system for the autodetection and classification of apple internal quality attributes. Postharvest Biology and Technology, 2021; 180: 111615.

Borba K R, Aykas D P, Milani M I, Colnago L A, Ferreira M D, Rodriguez-Saona L E. Portable near infrared spectroscopy as a tool for fresh tomato quality control analysis in the field. Applied Sciences, 2021; 11(3209): 11073209 .

Yu Y, Zhang Q L, Huang J P, Zhu J, Liu J W, et al. Nondestructive determination of SSC in Korla fragrant pear using a portable near-infrared spectroscopy system. Infrared Physics & Technology, 2021; 116: 103785.

Sohaib Ali Shah S, Zeb A, Qureshi W S, Malik A U, Tiwana M, Walsh K, et al. Mango maturity classification instead of maturity index estimation: A new approach towards handheld NIR spectroscopy. Infrared Physics & Technology, 2021; 115: 103639.

Das A J, Wahi A, Kothari I, Raskar R. Ultra-portable, wireless smartphone spectrometer for rapid, non-destructive testing of fruit ripeness. Scientific Reports, 2016; 6(1): 32504.

Pissard A, Marques E J N, Dardenne P, Lateur M, Pasquini C, Pimentel M F, et al. Evaluation of a handheld ultra-compact NIR spectrometer for rapid and non-destructive determination of apple fruit quality. Postharvest Biology and Technology, 2021; 172: 111375.

Blakey R J. Evaluation of avocado fruit maturity with a portable near-infrared spectrometer. Postharvest Biology and Technology, 2016; 121: 101–105.

Subedi P P, Walsh K B. Assessment of avocado fruit dry matter content using portable near infrared spectroscopy: method and instrumentation optimisation. Postharvest Biology and Technology, 2020; 161: 111078.

Gong X Y, Tang M, Gong Z J, Qiu Z P, Wang D M, Fan M K. Screening pesticide residues on fruit peels using portable Raman spectrometer combined with adhesive tape sampling. Food Chemistry, 2019; 295: 254–258.

Guo W C, Li W Q, Yang B, Zhu Z Z, Liu D Y, Zhu X H. A novel noninvasive and cost-effective handheld detector on soluble solids content of fruits. Journal of Food Engineering, 2019; 257: 1–9.

Sarkar S, Basak J K, Moon B E, Kim H T. A comparative study of PLSR and SVM-R with various preprocessing techniques for the quantitative determination of soluble solids content of hardy kiwi fruit by a portable Vis/NIR spectrometer. Foods, 2020; 9(8): 9081078.

Yang B, Guo W C, Li W Q, Li Q Q, Liu D Y, Zhu X H. Portable, visual, and nondestructive detector integrating Vis/NIR spectrometer for sugar content of kiwifruits. Journal of Food Process Engineering, 2019; 42(2): e12982.

Mishra P, Marini F, Brouwer B, Roger J M, Biancolillo A, Woltering E, et al. Sequential fusion of information from two portable spectrometers for improved prediction of moisture and soluble solids content in pear fruit. Talanta, 2021; 223: 121733.

Li M, Han D H, Liu W. Non-destructive measurement of soluble solids content of three melon cultivars using portable visible/near infrared spectroscopy. Biosystems Engineering, 2019; 188: 31–39.

Mishra P, Woltering E, El Harchioui N. Improved prediction of ‘Kent’ mango firmness during ripening by near-infrared spectroscopy supported by interval partial least square regression. Infrared Physics & Technology, 2020; 110: 103459.

Sulistyo S B, Siswantoro, Margiwiyatno A, Masrukhi, Mustofa A, Sudarmaji A, et al. Handheld arduino-based near infrared spectrometer for non-destructive quality evaluation of siamese oranges. In The 2nd International Conference on Sustainable Agriculture for Rural Development 2020, Purwokerto, Indonesia: IOP Publishing, 2021 .

Walsh K B, Blasco J, Zude-Sasse M, Sun X D. Visible-NIR ‘point’ spectroscopy in postharvest fruit and vegetable assessment: The science behind three decades of commercial use. Postharvest Biology and Technology, 2020; 168: 111246.

Yun Y-H, Bin J, Liu D-L, Xu L, Yan T L, Cao D S, et al. A hybrid variable selection strategy based on continuous shrinkage of variable space in multivariate calibration. Analytica Chimica Acta, 2019; 1058: 58–69.

Walsh K B, Mcglone V A, Han D H. The uses of near infra-red spectroscopy in postharvest decision support: A review. Postharvest Biology and Technology, 2020; 163: 111139.

Zheng W, Bai Y H, Luo H, Li Y H, Yang X, Zhang B H. Self-adaptive models for predicting soluble solid content of blueberries with biological variability by using near-infrared spectroscopy and chemometrics. Postharvest Biology and Technology, 2020; 169: 111286 .

Mishra P, Passos D. A synergistic use of chemometrics and deep learning improved the predictive performance of near-infrared spectroscopy models for dry matter prediction in mango fruit. Chemometrics and Intelligent Laboratory Systems, 2021; 212: 104287.

Mishra P, Passos D. Multi-output 1-dimensional convolutional neural networks for simultaneous prediction of different traits of fruit based on near-infrared spectroscopy. Postharvest Biology and Technology, 2022; 183: 111741.

Rong D, Wang H Y, Ying Y B, Zhang Z Y, Zhang Y S. Peach variety detection using VIS-NIR spectroscopy and deep learning. Computers and Electronics in Agriculture, 2020; 175: 105553.

Xu L X, Xie J, Cai F H, Wu J J. Spectral classification based on deep learning algorithms. Electronics, 2021; 10(16): 10161892 .

Li L, Huang W Q, Wang Z L, Liu S Q, He X, Fan S X, et al. Calibration transfer between developed portable Vis/NIR devices for detection of soluble solids contents in apple. Postharvest Biology and Technology, 2022; 183: 111720.

Silwal A, Karkee M, Zhang Q. A hierarchical approach to apple identification for robotic harvesting. Transactions of the ASABE, 2016; 59(5): 1079–1086.




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