Skip to main content
SpringerLink
Log in

Review of plant leaf recognition

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

Plants can be seen everywhere in daily life and are closely connected with our lives. The recognition and classification of plants are of great significance to ecological and environmental protection. Traditional plant identification methods are complex, and experts cannot classify multiple plant species quickly. More and more researchers pay attention to image processing and pattern recognition and use them to identify and classify plant leaves quickly. Based on this, this paper summarizes and classifies the methods of plant leaf recognition in recent years. First, we analyze these studies and classify them using different features and classifiers, such as shape, texture, color features, support vector machines, K nearest neighbors, convolutional neural networks, and so on. Secondly, compare the recognition results of plant leaf recognition methods under different datasets. Finally, the recognition of plant leaves is summarized, and future research and development have prospected.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

Data sharing not applicable to this paper as no datasets were generated during the current study.

References

  • Abdurrasyid H, Yeni H, Stéphane D (2016) Leaf shape recognition using centroid contour distance. In: IOP conference 31:012002

  • Adeel A, Khan MA, Sharif M, Azam F, Shah JH, Umer T, Wan S (2019) Diagnosis and recognition of grape leaf diseases: an automated system based on a novel saliency approach and canonical correlation analysis based multiple features fusion. Sustain Comput Inf Syst 24:100349

    Google Scholar 

  • Adeel A, Khan M, Akram T, Sharif A, Yasmin M, Saba T, Javed K (2020) Entropy controlled deep features selection framework for grape leaf diseases recognition. Expert Systems e12569

  • Amala S, Sreekumar K (2017) Literature review of image features and classifiers used in leaf based plant recognition through image analysis approach. In: 2017 international conference on inventive communication and computational technologies (ICICCT) pp 145–149

  • Amorim WP, Tetila EC, Pistori H, Papa JP (2019) Semi-supervised learning with convolutional neural networks for UAV images automatic recognition. Comput Electron Agric 164:104932

  • Anubha Pearline S, Sathiesh Kumar V, Harini S (2019) A study on plant recognition using conventional image processing and deep learning approaches. J Intell Fuzzy Syst 1–8

  • Aparajita S, Chen M (2016) Leaf analysis for plant recognition. In: 2016 7th IEEE international conference on software engineering and service science (ICSESS) pp 914–917

  • Arafat SY, Saghir MI, Ishtiaq M, Bashir U (2016) Comparison of techniques for leaf classification. In: Sixth international conference on digital information & communication technology & its applications pp 136–141

  • Arun K, Vinod P, Deepak K, Poonam S (2016) Optimizing feature selection using particle swarm optimization and utilizing ventral sides of leaves for plant leaf classification. Proc Comput Sci 89:324–332

    Article  Google Scholar 

  • Bagal VC, Manza RR (2016) Feature extraction of plant species from leaf architecture. In: 2016 international conference on electrical, electronics, and optimization techniques (ICEEOT) pp 4079–4081

  • Bertrand S, Ben A, Rihab C, Guillaume C, Didier V (2018) Bark and leaf fusion systems to improve automatic tree species recognition. Ecol Inf 46:57–73

    Article  Google Scholar 

  • Caglayan A, Guclu O, Can AB (2013) A plant recognition approach using shape and color features in leaf images. Lect Notes Comput Sci 8157:161–170

    Article  Google Scholar 

  • Cantero SVAB, Diogo NG, Leonardo FDSS, Wesley NG (2018) Importance of vertices in complex networks applied to texture analysis. IEEE Trans Cybern 50(2):777–786

  • Cao Y, Yan CJ, Li JY, Hong Z (2017) Leaf vein extraction and angle measurement using hue information and line detection In: 2017 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), pp 223–225

  • Chaki J, Parekh R, Bhattacharya S (2018) Plant leaf classification using multiple descriptors: a hierarchical approach. J King Saud Univ 32:1158–1172

    Google Scholar 

  • Chaki J, Dey N, Moraru L, Fuqian S (2019) Fragmented plant leaf recognition: bag-of-features, fuzzy-color and edge-texture histogram descriptors with multi-layer perceptron. Optik 181:639–650

  • Chakkaravarthy SS, Sajeevan G, Kamalanaban E, Varun Kuma KA (2016) Automatic leaf vein feature extraction for first degree veins. In: International conference on computing & network communications, pp 581–592

  • Chen BJW, During HJ, Anten NPR (2012) Detect thy neighbor: identity recognition at the root level in plants. Plant Sci 195(none):157–167

    Article  Google Scholar 

  • Chen J, Liu Q, Gao LW (2019) Visual tea leaf disease recognition using a convolutional neural network model. Symmetry 11(3):343

    Article  Google Scholar 

  • Cheng Q, Zhao H, Wang C, Du H (2018) An android application for plant identification. In: 2018 IEEE 4th information technology and mechatronics engineering conference (ITOEC), pp 60–64

  • Deeba K, Amutha B (2019) Identification of vegetable plant species using support vector machine. In: Kumar A, Mozar S (eds) ICCCE 2018. Springer, Singapore, pp 639–644

    Chapter  Google Scholar 

  • Demisse GG, Aouada D, Ottersten B (2018) Deformation based curved shape representation. IEEE Trans Pattern Anal Mach Intell 40(6):1338–1351

    Article  Google Scholar 

  • Do TB, Nguyen HH, Nguyen TTN, Vu H, Tran TTH, Le TL (2017) Plant identification using score-based fusion of multi-organ images. In: International conference on knowledge and systems engineering, pp 191–196

  • Eko P, Adityo DR, Nanik S, Chastine F (2018) Mango leaf classification with boundary moments of centroid contour distances as shape features. In: International seminar on intelligent technology & its applications, pp 317–320

  • Elnemr HA (2017) Feature selection for texture-based plant leaves classification. In: 2017 International conference on advanced control circuits systems (ACCS) systems 2017 international conference on new paradigms in electronics information technology (PEIT), pp 91–97

  • Emir S, Samim K (2016) Phase preserving Fourier descriptor for shape-based image retrieval. Signal Process Image Commun 40(C):82–96

    Google Scholar 

  • Florindo JB (2020) Dstnet: successive applications of the discrete Schroedinger transform for texture recognition. Inf Sci 507:356–364

    Article  Google Scholar 

  • Florindo J, Bruno O (2017) Discrete Schroedinger transform for texture recognition. Inf Sci 415–416:142–155

    Article  MATH  Google Scholar 

  • Fu B, Mao MM, Zhao XL, Shan Z, Yang ZL, He L, Wang ZP (2019) Recognition of plants with complicated background by leaf features. J Phys 1176:032053

    Google Scholar 

  • Geetharamani G, Arun Pandian J (2019) Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Comput Electr Eng 76:323–338

    Article  Google Scholar 

  • Ghasab MAJ, Khamis S, Mohammad F, Fariman HJ (2015) Feature decision-making ant colony optimization system for an automated recognition of plant species. Expert Syst Appl 42:2361–2370

  • Ghyar BS, Birajdar GK (2017) Computer vision based approach to detect rice leaf diseases using texture and color descriptors. In: International conference on inventive computing and informatics (ICICI), pp1074–1078

  • Gogul I, Sathiesh Kumar V (2017) Flower species recognition system using convolution neural networks and transfer learning. In: 2017 fourth international conference on signal processing, communication and networking (ICSCN), pp 1–6

  • Goyal N, Gupta K, Kumar N (2019) Multiclass twin support vector machine for plant species identification. Multimed Tools Appl 78:27785–27808

    Article  Google Scholar 

  • He KM, Zhang XY, Ren SQ, Sun J (2016) Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition, pp 770–778

  • Hlaing CS, Zaw SMM (2017) Plant diseases recognition for smart farming using model-based statistical features. In: 2017 IEEE 6th global conference on consumer electronics (GCCE), pp 1–4

  • Hoong NC, Chen Y, Ch’ng YS, Tan CS, Yeap ZQ, Loh YC, Wu SS, Yam MF (2018) Application of mid-infared spectroscopy with multivariate analysis for the discrimination of toxic plant, Gelsemium elegans. Vib Spectrosc 99:13–24

  • Hu RX, Jia W, Ling HB, Huang DS (2012) Multiscale distance matrix for fast plant leaf recognition. IEEE Trans Image Process 21(11):4667–4672

    Article  MathSciNet  MATH  Google Scholar 

  • Hu J, Chen ZB, Yang M, Zhang RG, Cui YJ (2018) A multiscale fusion convolutional neural network for plant leaf recognition. IEEE Signal Process Lett 25(6):853–857

    Article  Google Scholar 

  • Jiang HX (2020) The analysis of plants image recognition based on deep learning and artificial neural network. IEEE Access 8:68828–68841

  • Isnanto RR, Hidayatno A, Eskanesiari, Zahra AA, Septiana R (2017) Herb leaves recognition using combinations of Hu’s moment variants - backpropagation neural network and 2-d gabor filter - learning vector quantization (LVQ). In: 2017 4th international conference on information technology, computer, and electrical engineering (ICITACEE), pp 219–224

  • Isnanto R, Riyadi M, Awaj M (2018) Herb leaves recognition using gray level co-occurrence matrix and five distance-based similarity measures. Int J Electr Comput Eng 8:1920–1932

    Google Scholar 

  • Jalili LD, Morales A, Cervantes J, RuizCastilla JS (2016) Improving the performance of leaves identification by features selection with genetic algorithms. In: Workshop on engineering applications 103–114

  • Jendoubi S, Coquin D, Boukezzoula R (2019) Evidential two-step tree species recognition approach from leaves and bark. Expert Syst Appl 146:113154

    Article  Google Scholar 

  • Jiang BR, Wang P, Zhuang S, Li MS, Li ZF, Gong ZH (2018) Detection of maize drought based on texture and morphological features. Comput Electron Agric 151:50–60

    Article  Google Scholar 

  • Kala JR, Viriri S (2018) Plant specie classification using sinuosity coefficients of leaves. Image Anal Stereol 37(2):119–126

    Article  Google Scholar 

  • Kalyoncu C, Toygar N (2017) Gtclc: leaf classification method using multiple descriptors. IET Comput Vision 10(7):700–708

    Article  Google Scholar 

  • Kaur P, Pannu HS, Malhi AK (2019) Plant disease recognition using fractional-order zernike moments and svm classifier. Neural Comput Appl 31:8749–8768

    Article  Google Scholar 

  • Khalid S, Sajjad S, Jabbar S, Chang H (2017) Accurate and efficient shape matching approach using vocabularies of multi-feature space representations. J Real Time Image Process 13(3):449–465

    Article  Google Scholar 

  • Khan MA, Lali MIU, Sharif M, Javed K, Aurangzeb K, Haider SI, Altamrah AS, Akram T (2019) An optimized method for segmentation and classification of apple diseases based on strong correlation and genetic algorithm based feature selection. IEEE Access 7:46261–46277

    Article  Google Scholar 

  • Khan M, Akram T, Sharif M, Javed K, Raza M, Saba T (2020) An automated system for cucumber leaf diseased spot detection and classification using improved saliency method and deep features selection. Multimed Tools Appl 79:18627–18656

    Article  Google Scholar 

  • Kheirkhah FM, Asghari H (2019) Plant leaf classification using GIST texture features. IET Comput Vision 13(4):369–375

  • Khmag A, Al-Haddad SAR, Kamarudin N (2017) Recognition system for leaf images based on its leaf contour and centroid. In: IEEE student conference on research and development SCOReD pp 467–472

  • Kolivand H, Fern BM, Rahim MSM, Sulong G, Baker T, Tully D, Niedz RP (2018) An expert botanical feature extraction technique based on phenetic features for identifying plant species. PLoS ONE 13(2):191447

    Article  Google Scholar 

  • Kolivand H, Fern BM, Saba T, Rahim MSM, Rehman A (2019) A new leaf venation detection technique for plant species classification. Arab J Sci Eng 44(4):3315–3327

    Article  Google Scholar 

  • Kour VP, Arora S (2019) Particle swarm optimization based support vector machine (p-svm) for the segmentation and classification of plants. IEEE Access 7:29374–29385

    Article  Google Scholar 

  • Kumar N, Belhumeur PN, Biswas A, Jacobs DW, John Kress W, Lopez ID, Soares JVB (2012) Leafsnap: a computer vision system for automatic plant species identification. In: Fitzgibbon A, Lazebnik S, Perona P, Sato Y, Schmid C (eds) Computer vision—ECCV 2012, pp 502–516

  • Kumar A, Patidar V, Khazanachi D, Saini P (2015) An approach to improve classification accuracy of leaf images using dorsal and ventral features. Int J Adv Comput Sci Appl 6(9):130–137

    Google Scholar 

  • Kumar PSVVSR, Rao KNV, Raju ASN, Kumar DJN (2016) Leaf classification based on shape and edge feature with k-nn classifier. In: International conference on contemporary computing & informatics pp 548–552

  • Kumar M, Gupta S, Gao XZ, Singh A (2019) Plant species recognition using morphological features and adaptive boosting methodology. IEEE Access 7:163912–163918

  • Larese MG, Granitto PM (2016) Finding local leaf vein patterns for legume characterization and classification. Mach Vision Appl 27:709–720

  • Le VNT, Beniamin A, Kamal A (2018) Effective plant discrimination based on the combination of local binary pattern operators and multiclass support vector machine methods. Inf Process Agric 6(1):116–131

  • Lee SH, Chan CS, Mayo SJ, Remagnino P (2017) How deep learning extracts and learns leaf features for plant classification. Patt Recogn 71:1–13

    Article  Google Scholar 

  • Lin LH, Li CH, Yang S, Li SZ, Wei Y (2019) Automated classification of wuyi rock tealeaves based on support vector machine. Concurr Comput 31(23):e4519

    Article  Google Scholar 

  • Liu N, Kan JM (2016) Improved deep belief networks and multi-feature fusion for leaf identification. Neurocomputing 216:460–467

  • Liu JC, Chiang CY, Chen S (2016) Image-based plant recognition by fusion of multimodal information. In: 2016 10th international conference on innovative mobile and internet services in ubiquitous computing (IMIS), pp 5–11

  • Lukic M, Tuba E, Tuba M (2017) Leaf recognition algorithm using support vector machine with hu moments and local binary patterns. In: 2017 IEEE 15th international symposium on applied machine intelligence and informatics (SAMI), pp 485–490

  • Ma XL, Shen JY, Liu RJ, Zhai HP (2016) Choice of tobacco leaf features based on selected probability of particle swarm algorithm. In: 2016 Chinese control and decision conference (CCDC), pp 3041–3044

  • Muammer T, Hanbay D (2019) Plant recognition system based on extreme learning machine by using shearlet transform and new geometric features. J Fac Eng Arch Gazi Univ 34:2097–2112

  • Muneer A, Fati S (2020) Efficient and automated herbs classification approach based on shape and texture features using deep learning. IEEE Access 8:196747–196764

  • Murat M, Chang SW, Abu A, Yap HJ, Yong KT (2017) Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach. PeerJ 5(1):e3792

    Article  Google Scholar 

  • Naresh YG, Nagendraswamy HS (2015) Classification of medicinal plants: an approach using modified LBP with symbolic representation. Neurocomputing 173:1789–1797

  • Pearline A, Kumar S (2019) DDLA: dual deep learning architecture for classification of plant species. IET Image Process 13(12):2176–2182

  • Pham TN, Tran LV, Dao SVT (2020) Early disease classification of mango leaves using feed-forward neural network and hybrid metaheuristic feature selection. IEEE Access 8:189960–189973

    Article  Google Scholar 

  • Prasad S, Singh PP (2017) Medicinal plant leaf information extraction using deep features. In: Tencon IEEE region 2722–2726

  • Prasad S, Peddoju SK, Ghosh D (2017a) An adaptive plant leaf mobile informatics using RSSC. Multimed Tools Appl 76(20):21339–21363

    Article  Google Scholar 

  • Prasad S, Peddoju SK, Ghosh D (2017b) Efficient plant leaf representations: A comparative study. 2017 TENCON IEEE Region, pp 1175–1180

  • Punnarai S, Bryan S, Philip M, Gerard P (2016) Texture and shape attribute selection for plant disease monitoring in a mobile cloud-based environment. In: 2016 IEEE international conference on image processing (ICIP), pp 489–493

  • Qin F, Liu DX, Sun BD, Ruan L, Ma ZH, Wang HG (2016) Identification of alfalfa leaf diseases using image recognition technology. PLoS ONE 11(12):e0168274

  • Rafael RH, Asdrubal LC, Valentin TM, Carlos RHA (2016) Plant identification using new geometric features with standard data mining methods. In: IEEE international conference on networking, pp1–4

  • Ramamoorthy P, Vilasini M (2020) CNN approaches for classification of Indian leaf species using smartphones. Comput Mater Contin 62(3):1445–1472

    Google Scholar 

  • Rishabh Y, Kumar RY, Sushama N (2019) Plant leaf disease detection and classification using particle swarm optimization. In: Lecture notes in computer science 11407:294–306

  • Rizal IR, Ajulian ZA, Patricia J (2016) Pattern recognition on herbs leaves using region-based invariants feature extraction. In: International conference on information technology, pp 455–459

  • Roy D, Panda P, Roy K (2018) Tree-CNN: A hierarchical deep convolutional neural network for incremental learning. Neural Networks 121:148–160

  • Sabrol H, Kumar S (2020) Plant leaf disease detection using adaptive neuro-fuzzy classification. AISC 943:434–443

  • Saleem G, Akhtar M, Ahmed N, Qureshi WS (2019) Automated analysis of visual leaf shape features for plant classification. Comput Electron Agric 157:270–280

    Article  Google Scholar 

  • Shah MP, Singha S, Suyash P (2017) Awate. Leaf classification using marginalized shape context and shape+texture dual-path deep convolutional neural network. In: IEEE international conference on image processing, pp 860–864

  • Shan ZL, Zhao XL, Chen Z, Mao MM (2017) Plant recognition based on multi-feature and locality preserving projections fusion algorithm. In: 2016 international conference on computer, mechatronics and electronic engineering (CMEE 2016), pp 5347

  • Shao Y (2019) Supervised global-locality preserving projection for plant leaf recognition. Comput Electron Agric 158:102–108

    Article  Google Scholar 

  • Shao MW, Du JX, Wang J, Zhai CM (2014) Recognition of leaf image set based on manifold-manifold distance. ICIC2014 LNCS 8588:332–337

  • Sharif M, Khan M, Iqbal Z, Azam F, Lali MI, Javed M (2018) Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection. Comput Electron Agric 150:220–234

  • She YH, Wang XY, Gui T, Cai JK (2019) Lawn plant identification and segmentation based on least squares support vector machine and multifeature fusion. J Electron Imaging 28:23034

    Article  Google Scholar 

  • Shitala Prasad P, Kumar S, Ghosh D (2017) An efficient low vision plant leaf shape identification system for smart phones. Multimed Tools Appl 76(5):6915–6939

    Article  Google Scholar 

  • Soleimani PA, Gholamreza C, Payam Z, Jafar M (2018) Curvature-based pattern recognition for cultivar classification of anthurium flowers. Postharvest Biol Technol 139:67–74

    Article  Google Scholar 

  • Sulc M, Matas J (2017) Fine-grained recognition of plants from images. Plant Methods 13:115

    Article  Google Scholar 

  • Sun SS, Jiang M, He DJ, Long Y, Song HB (2019) Recognition of green apples in an orchard environment by combining the grabcut model and ncut algorithm. Biosyst Eng 187:201–213

    Article  Google Scholar 

  • Suresha M, Shreekanth KN, Thirumalesh BV (2017) Recognition of diseases in paddy leaves using knn classifier 663–666

  • Tan M, Le QV (2021) EfficientNetV2: smaller models and faster training. In: Proceedings of the 38th international conference on machine learning, vol 139, pp 1–11

  • Tan JW, Chang SW, Sameem BAK, Jen YH, Yong KT (2018) Deep learning for plant species classification using leaf vein morphometric. IEEE/ACM Trans Comput Biol Bioinf 17(1):82–90

  • Thanh TKN, Truong QB, Truong QD, Xuan HH (2018) Depth learning with convolutional neural network for leaves classifier based on shape of leaf vein. In: Nguyen NT, Hoang DH, Hong T-P, Pham H, Trawiński B (eds) Information intelligent, systems database. Springer, Cham, pp 565–575

    Chapter  Google Scholar 

  • Tharwat A, Gaber T, Hassanien AE (2017) One-dimensional vs. two-dimensional based features: plant identification approach. J Appl Logic 24:15–31

    Article  MathSciNet  Google Scholar 

  • Turkoglu M, Hanbay D (2019a) Plant recognition system based on deep features and color-LBP method. In: Signal processing and communications applications conference 1–4

  • Turkoglu M, Hanbay D (2019b) Leaf-based plant species recognition based on improved local binary pattern and extreme learning machine. Physica A 527:121297

    Article  Google Scholar 

  • Turkoglu M, Hanbay D (2019c) Recognition of plant leaves: an approach with hybrid features produced by dividing leaf images into two and four parts. Appl Math Comput 352:1–14

    Article  MathSciNet  MATH  Google Scholar 

  • Vijayalakshmi B, Mohan V (2016) Kernel-based pso and frvm: an automatic plant leaf type detection using texture, shape, and color features. Comput Electron Agric 125:99–112

    Article  Google Scholar 

  • Wang ZB, Cui J (2020) Plant recognition based on Jaccard distance and bow. Multimed Syst 26:495–508

    Article  Google Scholar 

  • Wang B, Wang D (2019) Plant leaves classification: a few-shot learning method based on siamese network. IEEE Access 7:1–1

    MathSciNet  Google Scholar 

  • Wang QC, Zhao JH, Li MZ, Cao CR, Lei YQ (2016) Preserving discriminant manifold subspace learning for plant leaf recognition. In: International conference on natural computation & fuzzy systems & knowledge discovery 1744–1749

  • Wang ZB, Sun XG, Zhang YN, Zhu Y, Ma YD (2018) Leaf recognition based on DPCNN and bow. Neural Process Lett 47(1):99–115

    Article  Google Scholar 

  • Wang B, Gao Y, Sun C, Blumenstein M, La Salle J (2019) Chord bunch walks for recognizing naturally self-overlapped and compound leaves. IEEE Trans Image Process 28(12):5963–5976

    Article  MathSciNet  MATH  Google Scholar 

  • Wu YR, Li JH (2019) Multi-feature sparse constrain model for crop disease recognition. Appl Ecol Environ Res 17(4):9229–9245

    Article  Google Scholar 

  • Wu XH, Zhu J, Wu B, Sun J, Dai CX (2018) Discrimination of tea varieties using FTIR spectroscopy and allied Gustafson-Kessel clustering. Comput Electron Agric 147:64–69

    Article  Google Scholar 

  • Li XX, Zhu CG, Bai XB, Mao FH, Fu ZT, Zhang LX (2019) Recognition method of cucumber leaves diseases based on visual spectrum and support vector machine. Spectrosc Spectral Anal 39:2250

    Google Scholar 

  • Xu G, Li C, Wang Q (2019) Unified multi-scale method for fast leaf classification and retrieval using geometric information. IET Image Proc 13(12):2328–2334

    Article  Google Scholar 

  • Yan L, Pang L, Wang H, Xiao J (2020) Recognition of different Longjing fresh tea varieties using hyperspectral imaging technology and chemometrics. J Food Process Eng 43:e13378

    Article  Google Scholar 

  • Yang C (2021) Plant leaf recognition by integrating shape and texture features. Patt Recogn 112:107809

    Article  Google Scholar 

  • Yang R, Kan J (2020) Classification of tree species at the leaf level based on hyperspectral imaging technology. J Appl Spectrosc 87:1–10

    Article  Google Scholar 

  • Yang C, Qian Y (2018) Multiscale Fourier descriptor based on triangular features for shape retrieval. Signal Process. 71:11

    Google Scholar 

  • Yang C, Wei H (2019) Plant species recognition using triangle-distance representation. IEEE Access 7:178108–178120

    Article  Google Scholar 

  • Yang CZ, Wei H, Yu Q (2016) Multiscale triangular centroid distance for shape-based plant leaf recognition. In: European conference on artificial intelligence, pp 269–276

  • Yan Z, Guo YYB, Wangpeng H (2019) O2o method for fast shape retrieval. IEEE Trans Image Process 28(11):5366–5378

  • Yousefi E, Baleghi Y, Sakhaei SM (2017) Rotation invariant wavelet descriptors, a new set of features to enhance plant leaves classification. Comput Electron Agric 140:70–76

    Article  Google Scholar 

  • Yu X, Xiong S, Gao Y, Yang Z, Yuan X (2016) Multiscale crossing representation using combined feature of contour and venation for leaf image identification. In: 2016 international conference on digital image computing: techniques and applications (DICTA), pp 1–6

  • Yu XH, Gao YS, Xiong SG, Yuan XH (2019) Multiscale contour steered region integral and its application for cultivar classification. IEEE Access 7:69087–69100

  • Wang Z, Sun X, Zhang Y, Zhu Y,  Ma Y (2016) Leaf recognition based on PCNN. Neural Comput Appl 27(4):899–908

    Article  Google Scholar 

  • Zeng W (2018) Research on deep convolutional neural networks for robust recognition of crop leaf diseases. University of Science and Technology of China

  • Zeng QM, Zhu TL, Zhuang XY, Zheng MX, Guo YB (2017) Using the periodic wavelet descriptor of plant leaf to identify plant species. Multimed Tools Appl 76:17873–17890

  • Zeng JX, Liu M, Fu X, Gu RY, Leng L (2019) Curvature bag of words model for shape recognition. IEEE Access 7:56163–57171

  • Zhang S, Wang H, Huang W (2017a) Two-stage plant species recognition by local mean clustering and weighted sparse representation classification. Clust Comput 20(2):1517–1525

    Article  Google Scholar 

  • Zhang S, Xiaowei W, You Z (2017b) Jaccard distance based weighted sparse representation for coarse-to-fine plant species recognition. PLoS ONE 12:e0178317

    Article  Google Scholar 

  • Zhang S, Zhang C, Zhu Y, You Z (2017c) Discriminant WSRC for large-scale plant species recognition. Comput Intell Neurosci 1–10:2017

    Google Scholar 

  • Zhang S, Xiaowei W, You Z, Zhang L (2017d) Leaf image based cucumber disease recognition using sparse representation classification. Comput Electron Agric 134:135–141

    Article  Google Scholar 

  • Zhang X, Qiao Y, Meng F, Fan C, Zhang M (2018a) Identification of maize leaf diseases using improved deep convolutional neural networks. IEEE Access 6:30370–30377

    Article  Google Scholar 

  • Zhang S, Wang H, Huang W, You Z (2018b) Plant diseased leaf segmentation and recognition by fusion of superpixel, k-means and phog. Optik Int J Light Electron Optics 157:866–872

    Article  Google Scholar 

  • Zhang S, Zhang C, Huang W (2018c) Integrating leaf and flower by local discriminant CCA for plant species recognition. Comput Electron Agric 155:150–156

    Article  Google Scholar 

  • Zhang W, Hansen MF, Smith M, Smith L, Grieve B (2018d) Photometric stereo for three-dimensional leaf venation extraction. Comput Ind 98:56–67

    Article  Google Scholar 

  • Zhang SW, Zhang CL, Wang Z, Kong WW (2018e) Combining sparse representation and singular value decomposition for plant recognition. Appl Soft Comput 67:164–171

  • Zhang S, Huang W, Zhang C (2019a) Three-channel convolutional neural networks for vegetable leaf disease recognition. Cogn Syst Res 53:31–41

    Article  Google Scholar 

  • Zhang X, Zhao WQ, Luo HZ, Chen L, Peng JY, Fan JP (2019b) Plant recognition via leaf shape and margin features. Multim Tools Appl 78:27463–27489

    Article  Google Scholar 

  • Zhang L, Zheng YL, Zhong GL, Wang Q (2019c) Research on leaf species identification based on principal component and linear discriminant analysis. Clust Comput 22:7795–7804

    Article  Google Scholar 

  • Zhang Q, Zeng S, Zhang B (2019d) Initial investigation of different classifiers for plant leaf classification using multiple features. In: 11th international conference on digital image processing (ICDIP) Proc. SPIE 1117922

  • Zhang LF, Zhang LP, Du B, You J, Tao DC (2019e) Hyperspectral image unsupervised classification by robust manifold matrix factorization. Inf Sci 485:154–169

  • Zhang S, Zhang C, Wang X (2020a) Plant species recognition based on global-local maximum margin discriminant projection. Knowl Based Syst 200:105998

    Article  Google Scholar 

  • Zhang YN, Cui J, Wang ZB, Kang JF, Min YF (2020b) Leaf image recognition based on bag of features. Appl Sci 10:5177

    Article  Google Scholar 

  • Zhang Y, Song CL, and Zhang DW (2020c) Deep learning-based object detection improvement for tomato disease. IEEE Access 8:56607–56614

  • Peng ZW, Wang X (2015) Study on leaf image feature extraction of cucumber disease. In: 2015 international conference on mechanical science and engineering, pp 1–5

  • Xiao ZY, Wang J (2020) Rapid nondestructive defect detection of Scindapsus aureus leaves based on PCA spectral feature optimization. IOP Conf Series 440:032018

    Google Scholar 

  • Zhang CL, Zhang SW, Yang JC, Shi YC, Chen J (2017) Apple leaf disease identification using genetic algorithm and correlation based feature selection method. Int J Agric Biol Eng 10(2):74–83

    Google Scholar 

  • Zhao C, Chan SSF, Cham Kuen CW, Chu LM (2015) Plant identification using leaf shapes-a pattern counting approach. Pattern Recogn 48(10):3203–3215

    Article  Google Scholar 

  • Zhong Y, Zhao M (2020) Research on deep learning in apple leaf disease recognition. Comput Electron Agric 168:105146

    Article  Google Scholar 

  • Zhou H, Yan CJ, Huang HH (2016) Tree species identification based on convolutional neural networks. In: 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics, pp 103–106

Download references

Funding

This study was funded by National Natural Science Foundation of China (Grant No. 61201421).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China

    Zhaobin Wang & Jing Cui

  2. Key Laboratory of Microbial Resources Exploitation and Application of Gansu Province, Institute of Biology, Gansu Academy of Sciences, Lanzhou, 730000, China

    Ying Zhu

Authors
  1. Zhaobin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jing Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ying Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhaobin Wang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Z., Cui, J. & Zhu, Y. Review of plant leaf recognition. Artif Intell Rev 56, 4217–4253 (2023). https://doi.org/10.1007/s10462-022-10278-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-022-10278-2

Keywords

Search

Navigation