Deep learning-powered machine vision technology for intelligent perception of fruits and vegetables quality: Progress, challenges, and prospects

Abstract

Abstract: Accurate analysis of fruits and vegetables quality is of great significance for ensuring food safety, improving consumer satisfaction, and promoting the sustainable development of the fruit and vegetable industry. Machine vision technology has been widely used in the field of fruits and vegetables industries in recent years. Traditional machine learning algorithms often have limitations when dealing with the large amounts of complex image data generated by machine vision technology, and their performance cannot meet the actual needs. The integration of machine vision technology and deep learning algorithms has enabled efficient analysis and processing of complex fruit and vegetable images. The fruits and vegetables quality detection system based on machine vision and deep learning has achieved remarkable results in practical applications and has provided strong technical support for the intelligent upgrading of the fruit and vegetable industry. This review summarizes the research progress of machine vision technology driven by deep learning in the field of fruit and vegetable quality analysis in recent years. It also focuses on the current challenges faced in this field and proposes future development trends, including the construction of public datasets, the development of lightweight models and 3D sensing devices, multimodal fusion, model interpretability, the development of portable and miniaturized devices, and the construction of a full-industry-chain intelligent fruit and vegetable management system empowered by the Internet of Things (IoT) and blockchain technology. These efforts are expected to promote the technological upgrading and collaborative innovation of the fruit and vegetable industries.

Key words: Fruit and vegetable, Machine vision, Deep learning, Convolutional Neural Networks, Freshness?

CLC Number:

550.10

References

[1] NARENDERAN S T, MEYYANATHAN S N, BABU B. Review of pesticide residue analysis in fruits and vegetables. Pre-treatment, extraction and detection techniques [J]. Food Research International, 2020, 133: 109141. DOI:10.1016/j.foodres.2020.109141.
[2] BARRETT D M, BEAULIEU J C, SHEWFELT R. Color, flavor, texture, and nutritional quality of fresh-cut fruits and vegetables: Desirable levels, instrumental and sensory measurement, and the effects of processing [J]. Critical Reviews in Food Science and Nutrition, 2010, 50(5): 369-89. DOI:10.1080/10408391003626322.
[3] BROSNAN T, SUN D W. Improving quality inspection of food products by computer vision - a review [J]. Journal of Food Engineering, 2004, 61(1): 3-16. DOI:10.1016/s0260-8774(03)00183-3.
[4] BHARGAVA A, BANSAL A. Fruits and vegetables quality evaluation using computer vision: A review [J]. Journal of King Saud University-Computer and Information Sciences, 2021, 33(3): 243-57. DOI:10.1016/j.jksuci.2018.06.002.
[5] ILESANMI A E, ILESANMI T O. Methods for image denoising using convolutional neural network: a review [J]. Complex & Intelligent Systems, 2021, 7(5): 2179-98. DOI:10.1007/s40747-021-00428-4.
[6] PRADHYUMNA P, SHREYA G P, MOHANA. Graph Neural Network (GNN) in image and video understanding using deep learning for computer vision applications [J]. 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC), 2021: 1183-9. DOI:10.1109/icesc51422.2021.9532631.
[7] XU S F, GENG S J, XU P F, et al. Cognitive fusion of graph neural network and convolutional neural network for enhanced hyperspectral target detection [J]. Ieee Transactions on Geoscience and Remote Sensing, 2024, 62: 5515915. DOI:10.1109/tgrs.2024.3392188.
[8] MEENU M, KURADE C, NEELAPU B C, et al. A concise review on food quality assessment using digital image processing [J]. Trends in Food Science & Technology, 2021, 118: 106-24. DOI:10.1016/j.tifs.2021.09.014.
[9] ALI M M, HASHIM N, ABD AZIZ S, et al. Utilisation of deep learning with multimodal data fusion for determination of pineapple quality using thermal imaging [J]. Agronomy-Basel, 2023, 13(2): 401. DOI:10.3390/agronomy13020401.
[10] ROY K, CHAUDHURI S S, PRAMANIK S. Deep learning based real-time Industrial framework for rotten and fresh fruit detection using semantic segmentation [J]. Microsystem Technologies-Micro-and Nanosystems-Information Storage and Processing Systems, 2021, 27(9): 3365-75. DOI:10.1007/s00542-020-05123-x.
[11] YUAN Y, JI Z T, FAN Y W, et al. Deep learning-assisted fluorescence spectroscopy for food quality and safety analysis [J]. Trends in Food Science & Technology, 2025, 156: 104821. DOI:10.1016/j.tifs.2024.104821.
[12] HUA X H, LI H X, ZENG J B, et al. A review of target recognition technology for fruit picking robots: From digital Image processing to deep learning [J]. Applied Sciences-Basel, 2023, 13(7): 4160. DOI:10.3390/app13074160.
[13] KAUSHAL S, TAMMINENI D K, RANA P, et al. Computer vision and deep learning-based approaches for detection of food nutrients/nutrition: New insights and advances [J]. Trends in Food Science & Technology, 2024, 146: 104408. DOI:10.1016/j.tifs.2024.104408.
[14] AHMED M T, MONJUR O, KHALIDUZZAMAN A, et al. A comprehensive review of deep learning-based hyperspectral image reconstruction for agri-food quality appraisal [J]. Artificial Intelligence Review, 2025, 58(4): 96. DOI:10.1007/s10462-024-11090-w.
[15] AMEMIYA T, LEOW C S, BUAYAI P, et al. Appropriate grape color estimation based on metric learning for judging harvest timing [J]. Visual Computer, 2022, 38(12): 4083-94. DOI:10.1007/s00371-022-02666-0.
[16] ESPEJO-GARCIA B, MYLONAS N, ATHANASAKOS L, et al. Towards weeds identification assistance through transfer learning [J]. Computers and Electronics in Agriculture, 2020, 171: 105306. DOI:10.1016/j.compag.2020.105306.
[17] MASUDA K, UCHIDA R, FUJITA N, et al. Application of deep learning diagnosis for multiple traits sorting in peach fruit [J]. Postharvest Biology and Technology, 2023, 201: 112348. DOI:10.1016/j.postharvbio.2023.112348.
[18] SIRICHAROEN P, YOMSATIEANKUL W, BUNSRI T. Recognizing the sweet and sour taste of pineapple fruits using residual networks and green-relative color transformation attached with Mask R-CNN [J]. Postharvest Biology and Technology, 2023, 196: 112174. DOI:10.1016/j.postharvbio.2022.112174.
[19] SONG W R, JIANG N F, WANG H, et al. Evaluation of machine learning methods for organic apple authentication based on diffraction grating and image processing [J]. Journal of Food Composition and Analysis, 2020, 88: 103437. DOI:10.1016/j.jfca.2020.103437.
[20] LIU Y, WEI C J, YOON S C, et al. Development of multimodal fusion technology for tomato maturity assessment [J]. Sensors, 2024, 24(8): 2467. DOI:10.3390/s24082467.
[21] OLISAH C C, TREWHELLA B, LI B, et al. Convolutional neural network ensemble learning for hyperspectral imaging-based blackberry fruit ripeness detection in uncontrolled farm environment [J]. Engineering Applications of Artificial Intelligence, 2024, 132: 107945. DOI:10.1016/j.engappai.2024.107945.
[22] CHANG C, PARTHIBAN R, KALAVALLY V, et al. Unharvested palm fruit bunch ripeness detection with hybrid color correction [J]. Smart Agricultural Technology, 2024, 9: 100643. DOI:10.1016/j.atech.2024.100643.
[23] MOJARAVSCKI D, MAGALHAES P S G. Comparative evaluation of color correction as Image preprocessing for olive identification under natural light using cell phones [J]. Agriengineering, 2024, 6(1): 155-70. DOI:10.3390/agriengineering6010010.
[24] WU F Y, YANG Z, MO X K, et al. Detection and counting of banana bunches by integrating deep learning and classic image-processing algorithms [J]. Computers and Electronics in Agriculture, 2023, 209: 107827. DOI:10.1016/j.compag.2023.107827.
[25] YOUSAF J, ABUOWDA Z, RAMADAN S, et al. Autonomous smart palm tree harvesting with deep learning-enabled date fruit type and maturity stage classification [J]. Engineering Applications of Artificial Intelligence, 2025, 139: 109506. DOI:10.1016/j.engappai.2024.109506.
[26] XUE W, DING H F, JIN T, et al. CucumberAI: Cucumber fruit morphology identification system based on artificial intelligence [J]. Plant Phenomics, 2024, 6: 0193. DOI:10.34133/plantphenomics.0193.
[27] FAN J B, SALAM M S B, RONG X W, et al. Peach fruit thinning image detection based on improved YOLOv8 and data enhancement techniques [J]. Ieee Access, 2024, 12: 191199-218. DOI:10.1109/access.2024.3516484.
[28] WANG B K, LI M Q, WANG Y Q, et al. A smart fruit size measuring method and system in natural environment [J]. Journal of Food Engineering, 2024, 373: 112020. DOI:10.1016/j.jfoodeng.2024.112020.
[29] PIPITSUNTHONSAN P, PAN L R, PENG S L, et al. Palm bunch grading technique using a multi-input and multi-label convolutional neural network [J]. Computers and Electronics in Agriculture, 2023, 210: 107864. DOI:10.1016/j.compag.2023.107864.
[30] LU Y W, WANG R, HU T Y, et al. Nondestructive 3D phenotyping method of passion fruit based on X-ray micro-computed tomography and deep learning [J]. Frontiers in Plant Science, 2023, 13: 1087904. DOI:10.3389/fpls.2022.1087904.
[31] BLOK P M, MAGISTRI F, STACHNISS C, et al. High-throughput 3D shape completion of potato tubers on a harvester [J]. Computers and Electronics in Agriculture, 2025, 228: 109673. DOI:10.1016/j.compag.2024.109673.
[32] GAO Y, WANG Q Y, RAO X Q, et al. OrangeStereo: A navel orange stereo matching network for 3D surface reconstruction [J]. Computers and Electronics in Agriculture, 2024, 217: 108626. DOI:10.1016/j.compag.2024.108626.
[33] CAI X J, ZHU Y, LIU S W, et al. FastSegFormer: A knowledge distillation-based method for real-time semantic segmentation of surface defects in navel oranges [J]. Computers and Electronics in Agriculture, 2024, 217: 108604. DOI:10.1016/j.compag.2023.108604.
[34] ZHOU X, AMPATZIDIS Y, LEE W S, et al. Deep learning-based postharvest strawberry bruise detection under UV and incandescent light [J]. Computers and Electronics in Agriculture, 2022, 202: 107389. DOI:10.1016/j.compag.2022.107389.
[35] VAN DE LOOVERBOSCH T, HE J Q, TEMPELAERE A, et al. Inline nondestructive internal disorder detection in pear fruit using explainable deep anomaly detection on X-ray images [J]. Computers and Electronics in Agriculture, 2022, 197: 106962. DOI:10.1016/j.compag.2022.106962.
[36] FAN S X, LIANG X T, HUANG W Q, et al. Real-time defects detection for apple sorting using NIR cameras with pruning-based YOLOV4 network [J]. Computers and Electronics in Agriculture, 2022, 193: 106715. DOI:10.1016/j.compag.2022.106715.
[37] DENG L M, LI J, HAN Z Z. Online defect detection and automatic grading of carrots using computer vision combined with deep learning methods [J]. Lwt-Food Science and Technology, 2021, 149: 111832. DOI:10.1016/j.lwt.2021.111832.
[38] LU J Q, CHEN W D, LAN Y B, et al. Design of citrus peel defect and fruit morphology detection method based on machine vision [J]. Computers and Electronics in Agriculture, 2024, 219: 108721. DOI:10.1016/j.compag.2024.108721.
[39] YANG Y, LIU Z F, HUANG M, et al. Automatic detection of multi-type defects on potatoes using multispectral imaging combined with a deep learning model [J]. Journal of Food Engineering, 2023, 336: 111213. DOI:10.1016/j.jfoodeng.2022.111213.
[40] WONGGASEM K, CHAKRANON P, WONGCHAISUWAT P. Automated quality inspection of baby corn using image processing and deep learning [J]. Artificial Intelligence in Agriculture, 2024, 11: 61-9. DOI:10.1016/j.aiia.2024.01.001.
[41] CHEN H Z, QIAO H L, FENG Q X, et al. Rapid detection of pomelo fruit quality using near-infrared hyperspectral imaging combined with chemometric methods [J]. Frontiers in Bioengineering and Biotechnology, 2021, 8: 616943. DOI:10.3389/fbioe.2020.616943.
[42] TSAKIRIDIS N L, SAMARINAS N, KOKKAS S, et al. In situ grape ripeness estimation via hyperspectral imaging and deep autoencoders [J]. Computers and Electronics in Agriculture, 2023, 212: 108098. DOI:10.1016/j.compag.2023.108098.
[43] YAN Y J, WONG W K, CHEN C J, et al. Hyperspectral signature-band extraction and learning: an example of sugar content prediction of Syzygium samarangense [J]. Scientific Reports, 2023, 13(1): 15100. DOI:10.1038/s41598-023-41603-6.
[44] SHAH I H, WU J H, DING X T, et al. A non-destructive approach: Estimation of melon Fruit quality attributes and nutrients using hyperspectral imaging coupled with machine learning [J]. Smart Agricultural Technology, 2025, 10: 100811. DOI:10.1016/j.atech.2025.100811.
[45] MARTINS J A, RODRIGUES D, CAVACO A M, et al. Estimation of soluble solids content and fruit temperature in 'Rocha' pear using Vis-NIR spectroscopy and the SpectraNet-32 deep learning architecture [J]. Postharvest Biology and Technology, 2023, 199: 112281. DOI:10.1016/j.postharvbio.2023.112281.
[46] ONG P, CHEN S M, TSAI C Y, et al. A non-destructive methodology for determination of cantaloupe sugar content using machine vision and deep learning [J]. Journal of the Science of Food and Agriculture, 2022, 102(14): 6586-95. DOI:10.1002/jsfa.12024.
[47] ZHOU C X, ZHANG X, LIU Y, et al. Research on hyperspectral regression method of soluble solids in green plum based on one-dimensional deep convolution network [J]. Spectrochimica Acta Part a-Molecular and Biomolecular Spectroscopy, 2023, 303: 123151. DOI:10.1016/j.saa.2023.123151.
[48] SUN Z Z, TIAN H, HU D, et al. Integrating deep learning and data fusion for enhanced oranges soluble solids content prediction using machine vision and Vis/NIR spectroscopy [J]. Food Chemistry, 2025, 464: 141488. DOI:10.1016/j.foodchem.2024.141488.
[49] SHI Q, LI Y L, ZHANG F, et al. Whale optimization algorithm-based multi-task convolutional neural network for predicting quality traits of multi-variety pears using near-infrared spectroscopy [J]. Postharvest Biology and Technology, 2024, 215: 113018. DOI:10.1016/j.postharvbio.2024.113018.
[50] LIU Q C, JIANG X N, WANG F, et al. Evaluation and process monitoring of jujube hot air drying using hyperspectral imaging technology and deep learning for quality parameters [J]. Food Chemistry, 2025, 467: 141999. DOI:10.1016/j.foodchem.2024.141999.
[51] SHORTEN C, KHOSHGOFTAAR T M. A survey on image data augmentation for deep learning [J]. Journal of Big Data, 2019, 6(1): 60. DOI:10.1186/s40537-019-0197-0.
[52] DE ALWIS S, OFOGHI B, NA M H, et al. Enhanced strawberry image classification using multi-task deep neural learning[C]// Proceedings of the 37th Annual ACM Symposium on Applied Computing, New York, America, F.Apr 25-29, 2022. Assoc Computing Machinery, 2022:971-8.
[53] MAO X J, XIAO W M, WAN Y Q, et al. Dispersive solid-phase extraction using microporous metal-organic framework UiO-66: Improving the matrix compounds removal for assaying pesticide residues in organic and conventional vegetables [J]. Food Chemistry, 2021, 345: 128807. DOI:10.1016/j.foodchem.2020.128807.
[54] MAO X J, WAN Y Q, LI Z M, et al. Analysis of organophosphorus and pyrethroid pesticides in organic and conventional vegetables using QuEChERS combined with dispersive liquid-liquid microextraction based on the solidification of floating organic droplet [J]. Food Chemistry, 2020, 309: 125755. DOI:10.1016/j.foodchem.2019.125755.
[55] YANG Q C, YIN L, HU X D, et al. Detection of drug residues in bean sprouts by hyperspectral imaging combined with 1DCNN with channel attention mechanism [J]. Microchemical Journal, 2024, 206: 111497. DOI:10.1016/j.microc.2024.111497.
[56] BIAN H T, MA B X, YU G W, et al. Fusion features of microfluorescence hyperspectral imaging for qualitative detection of pesticide residues in Hami melon [J]. Food Research International, 2024, 196: 115010. DOI:10.1016/j.foodres.2024.115010.
[57] HE W W, HE H Y, WANG F L, et al. Non-destructive detection and recognition of pesticide residues on garlic chive (Allium tuberosum) leaves based on short wave infrared hyperspectral imaging and one-dimensional convolutional neural network [J]. Journal of Food Measurement and Characterization, 2021, 15(5): 4497-507. DOI:10.1007/s11694-021-01012-7.
[58] SUN L, CUI X W, FAN X F, et al. Automatic detection of pesticide residues on the surface of lettuce leaves using images of feature wavelengths spectrum [J]. Frontiers in Plant Science, 2023, 13: 929999. DOI:10.3389/fpls.2022.929999.
[59] HU Y T, MA B X, WANG H T, et al. Detecting different pesticide residues on Hami melon surface using hyperspectral imaging combined with 1D-CNN and information fusion [J]. Frontiers in Plant Science, 2023, 14: 1105601. DOI:10.3389/fpls.2023.1105601.
[60] NIE P C, QU F F, LIN L, et al. Trace identification and visualization of multiple benzimidazole pesticide residues on Toona sinensis leaves using terahertz imaging combined with deep learning [J]. International Journal of Molecular Sciences, 2021, 22(7): 3425. DOI:10.3390/ijms22073425.
[61] WU G J, DU C X, PENG C Y, et al. Machine learning-assisted laccase-like activity nanozyme for intelligently onsite real-time and dynamic analysis of pyrethroid pesticides [J]. Journal of Hazardous Materials, 2024, 480: 136015. DOI:10.1016/j.jhazmat.2024.136015.
[62] BASAVARAJU A, DAVIDSON E, DIRACCA G, et al. Pesticide residue coverage estimation on citrus leaf using image analysis assisted by machine learning [J]. Applied Sciences-Basel, 2024, 14(22): 10087. DOI:10.3390/app142210087.
[63] CHOI J Y, SEO K, CHO J S, et al. Applying convolutional neural networks to assess the external quality of strawberries [J]. Journal of Food Composition and Analysis, 2021, 102: 104071. DOI:10.1016/j.jfca.2021.104071.
[64] SAGAR N, SURESH K P, SRIDHARA S, et al. Precision detection of grapevine downy and powdery mildew diseased leaves and fruits using enhanced ResNet50 with batch normalization [J]. Computers and Electronics in Agriculture, 2025, 232: 110144. DOI:10.1016/j.compag.2025.110144.
[65] CHUN S W, SONG D J, LEE K H, et al. Deep learning algorithm development for early detection of Botrytis cinerea infected strawberry fruit using hyperspectral fluorescence imaging [J]. Postharvest Biology and Technology, 2024, 214: 112918. DOI:10.1016/j.postharvbio.2024.112918.
[66] SHA W, HU K, WENG S Z. Statistic and network features of RGB and hyperspectral imaging for determination of black root mold Infection in apples [J]. Foods, 2023, 12(8): 1608. DOI:10.3390/foods12081608.
[67] ZHAI M C, WANG Z B, LI H, et al. Non-contact detection of sub-healthy apples with moldy core by air jet excitation and laser Doppler vibration sensing [J]. Postharvest Biology and Technology, 2025, 222: 113427. DOI:10.1016/j.postharvbio.2025.113427.
[68] YANG S, LI C X, LI X Y, et al. Early detection of fungal infection in citrus using biospeckle imaging [J]. Computers and Electronics in Agriculture, 2024, 225: 109293. DOI:10.1016/j.compag.2024.109293.
[69] PU Y J, CHEN L Y, JIANG H T, et al. A comprehensive review of intelligent packaging for fruits and vegetables: Target responders, classification, applications, and future challenges [J]. Comprehensive Reviews in Food Science and Food Safety, 2023, 22(2): 842-81. DOI:10.1111/1541-4337.13093.
[70] MUKHIDDINOV M, MUMINOV A, CHO J. Improved classification approach for fruits and vegetables freshness based on deep learning [J]. Sensors, 2022, 22(21): 8192. DOI:10.3390/s22218192.
[71] AMIN U, SHAHZAD M I, SHAHZAD A, et al. Automatic fruits freshness classification using CNN and Transfer learning [J]. Applied Sciences-Basel, 2023, 13(14): 8087. DOI:10.3390/app13148087.
[72] BU Y P, HU J X, CHEN C, et al. ResNet incorporating the fusion data of RGB & hyperspectral images improves classification accuracy of vegetable soybean freshness [J]. Scientific Reports, 2024, 14(1): 2568. DOI:10.1038/s41598-024-51668-6.
[73] ISMAIL N, MALIK O A. Real-time visual inspection system for grading fruits using computer vision and deep learning techniques [J]. Information Processing in Agriculture, 2022, 9(1): 24-37. DOI:10.1016/j.inpa.2021.01.005.
[74] ZHANG Y S, YANG X D, CHENG Y B, et al. Fruit freshness detection based on multi-task convolutional neural network [J]. Current Research in Food Science, 2024, 8: 100733. DOI:10.1016/j.crfs.2024.100733.
[75] WANG D Y, ZHANG M, ZHU Q B, et al. Intelligent vegetable freshness monitoring system developed by integrating eco-friendly fluorescent sensor arrays with deep convolutional neural networks [J]. Chemical Engineering Journal, 2024, 488: 150739. DOI:10.1016/j.cej.2024.150739.
[76] YUAN Y, CHEN J C, POLAT K, et al. An innovative approach to detecting the freshness of fruits and vegetables through the integration of convolutional neural networks and bidirectional long short-term memory network [J]. Current Research in Food Science, 2024, 8: 100723. DOI:10.1016/j.crfs.2024.100723.
[77] ABAYOMI-ALLI O O, DAMASEVICIUS R, MISRA S, et al. FruitQ: a new dataset of multiple fruit images for freshness evaluation [J]. Multimedia Tools and Applications, 2024, 83(4): 11433-60. DOI:10.1007/s11042-023-16058-6.
[78] YUAN Y, CHEN X L. Vegetable and fruit freshness detection based on deep features and principal component analysis [J]. Current Research in Food Science, 2024, 8: 100656. DOI:10.1016/j.crfs.2023.100656.
[79] WANG C L, LIU S C, WANG Y W, et al. Application of convolutional neural network-based detection methods in fresh fruit production: A comprehensive review [J]. Frontiers in Plant Science, 2022, 13: 868745. DOI:10.3389/fpls.2022.868745.
[80] GUO Z M, ZOU Y, SUN C J, et al. Nondestructive determination of edible quality and watercore degree of apples by portable Vis/NIR transmittance system combined with CARS-CNN [J]. Journal of Food Measurement and Characterization, 2024: 4058–73. DOI:10.1007/s11694-024-02476-z.
[81] MISHRA P, VERSCHOOR J, VRIES M N D, et al. Portable near-infrared spectral imaging combining deep learning and chemometrics for dry matter and soluble solids prediction in intact kiwifruit [J]. Infrared Physics & Technology, 2023, 131: 104677. DOI:10.1016/j.infrared.2023.104677.
[82] XIANG L R, WANG D Y. A review of three-dimensional vision techniques in food and agriculture applications [J]. Smart Agricultural Technology, 2023, 5: 100259. DOI:10.1016/j.atech.2023.100259.
[83] DUAN J L, LAI L Q, YANG Z, et al. Multi-feature language-image model for fruit quality image classification [J]. Computers and Electronics in Agriculture, 2024, 227: 109462. DOI:10.1016/j.compag.2024.109462.
[84] SHEN C, WANG R, NAWAZISH H, et al. Machine vision combined with deep learning-based approaches for food authentication: An integrative review and new insights [J]. Comprehensive Reviews in Food Science and Food Safety, 2024, 23(6): e70054. DOI:10.1111/1541-4337.70054.
[85] WANG Y, GU H W, YIN X L, et al. Deep leaning in food safety and authenticity detection: An integrative review and future prospects [J]. Trends in Food Science & Technology, 2024, 146: 104396. DOI:10.1016/j.tifs.2024.104396.