Detection of citrus diseases using artificial intelligence: A systematic review
DOI:
https://doi.org/10.62486/latia2025122Keywords:
Deep Neural Networks, Adaboost, Deep Learning, CitrusAbstract
Early detection of citrus diseases is important for the global agricultural industry, facing threats such as Huanglongbing and canker. This study reviews the current status of the use of artificial intelligence to improve detection accuracy and speed. A systematic literature review was conducted from 2019 to 2023, using databases such as Scopus, IEEE Xplore and ACM, focusing on identifying the fruits studied, prevalent diseases, AI algorithms used and their accuracies, as well as technical challenges in implementing AI systems. The results highlight that oranges, lemons and mandarins are the most investigated fruits, with Huanglongbing, black spot and canker as the most studied diseases. AI algorithms such as Deep Neural Networks (DNN) and Adaboost show high accuracies, essential to improve disease detection. However, challenges include lack of labeled data, adaptation to different agricultural conditions, and effective integration in dynamic agricultural environments. This study reveals the need to advance data quality and algorithm adaptability to strengthen sustainability and efficiency in disease detection in citrus crops
References
Zhou Z, Zhang Y, Gu Z, Yang SX. Deep learning approaches for object recognition in plant diseases: a review. Intell Robot [Internet]. 2023 Oct 28;3(4):514–37. Available from: https://www.oaepublish.com/articles/ir.2023.29 DOI: https://doi.org/10.20517/ir.2023.29
Dinesh P, Lakshmanan R. Symptoms Based Image Predictive Analysis for Citrus Orchards Using Machine Learning Techniques: A Review. Int J Recent Innov Trends Comput Commun [Internet]. 2023 Sep 20;11(8):53–71. Available from: https://ijritcc.org/index.php/ijritcc/article/view/7924 DOI: https://doi.org/10.17762/ijritcc.v11i8.7924
Sun L, Nasrullah, Ke F, Nie Z, Wang P, Xu J. Citrus Genetic Engineering for Disease Resistance: Past, Present and Future. Int J Mol Sci [Internet]. 2019 Oct 23;20(21):5256. Available from: https://www.mdpi.com/1422-0067/20/21/5256 DOI: https://doi.org/10.3390/ijms20215256
Peng K, Ma W, Lu J, Tian Z, Yang Z. Application of Machine Vision Technology in Citrus Production. Appl Sci [Internet]. 2023 Aug 17;13(16):9334. Available from: https://www.mdpi.com/2076-3417/13/16/9334 DOI: https://doi.org/10.3390/app13169334
Terentev A, Dolzhenko V, Fedotov A, Eremenko D. Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review. Sensors [Internet]. 2022 Jan 19;22(3):757. Available from: https://www.mdpi.com/1424-8220/22/3/757 DOI: https://doi.org/10.3390/s22030757
Mohammed EA, Mohammed GH. Citrus leaves disease diagnosis. Indones J Electr Eng Comput Sci [Internet]. 2023 Aug 1;31(2):925. Available from: https://ijeecs.iaescore.com/index.php/IJEECS/article/view/30613 DOI: https://doi.org/10.11591/ijeecs.v31.i2.pp925-932
Ghanei Ghooshkhaneh N, Mollazade K. Optical Techniques for Fungal Disease Detection in Citrus Fruit: A Review. Food Bioprocess Technol [Internet]. 2023 Aug 18;16(8):1668–89. Available from: https://link.springer.com/10.1007/s11947-023-03005-4 DOI: https://doi.org/10.1007/s11947-023-03005-4
Luaibi AR, Salman TM, Miry AH. Detection of citrus leaf diseases using a deep learning technique. Int J Electr Comput Eng [Internet]. 2021 Apr 1;11(2):1719. Available from: http://ijece.iaescore.com/index.php/IJECE/article/view/23056 DOI: https://doi.org/10.11591/ijece.v11i2.pp1719-1727
Adhiwibawa MAS, Nugroho WH, Solimun. Detection of Anomalies in Citrus Leaves Using Digital Image Processing and T 2 Hotelling Multivariate Control Chart. In: 2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT) [Internet]. IEEE; 2019. p. 310–4. Available from: https://ieeexplore.ieee.org/document/8834453/ DOI: https://doi.org/10.1109/ICAIIT.2019.8834453
Dhiman P, Kaur A, Hamid Y, Alabdulkreem E, Elmannai H, Ababneh N. Smart Disease Detection System for Citrus Fruits Using Deep Learning with Edge Computing. Sustainability [Internet]. 2023 Mar 3;15(5):4576. Available from: https://www.mdpi.com/2071-1050/15/5/4576 DOI: https://doi.org/10.3390/su15054576
Karthik R, Hussain S, George TT, Mishra R. A dual track deep fusion network for citrus disease classification using group shuffle depthwise feature pyramid and Swin transformer. Ecol Inform [Internet]. 2023 Dec;78:102302. Available from: https://linkinghub.elsevier.com/retrieve/pii/S157495412300331X DOI: https://doi.org/10.1016/j.ecoinf.2023.102302
Tang Y, Yang J, Zhuang J, Hou C, Miao A, Ren J, et al. Early detection of citrus anthracnose caused by Colletotrichum gloeosporioides using hyperspectral imaging. Comput Electron Agric [Internet]. 2023 Nov;214:108348. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0168169923007366 DOI: https://doi.org/10.1016/j.compag.2023.108348
M.R. Tamjis SA. Leaf Disease Detection by Using Convolutional Pretrained Model. Int J Commun Networks Inf Secur [Internet]. 2022 Dec 31;14(1s):114–20. Available from: https://www.ijcnis.org/index.php/ijcnis/article/view/5619 DOI: https://doi.org/10.17762/ijcnis.v14i1s.5619
Kitchenham B, Charters S. Guidelines for performing Systematic Literature Reviews in Software Engineering. Keele Univ y Univ Durham [Internet]. 2007; Available from: https://legacyfileshare.elsevier.com/promis_misc/525444systematicreviewsguide.pdf
Osco LP, Arruda M dos S de, Marcato Junior J, da Silva NB, Ramos APM, Moryia ÉAS, et al. A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery. ISPRS J Photogramm Remote Sens [Internet]. 2020 Feb;160:97–106. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0924271619302989 DOI: https://doi.org/10.1016/j.isprsjprs.2019.12.010
Dhiman P, Kukreja V, Manoharan P, Kaur A, Kamruzzaman MM, Dhaou I Ben, et al. A Novel Deep Learning Model for Detection of Severity Level of the Disease in Citrus Fruits. Electronics [Internet]. 2022 Feb 8;11(3):495. Available from: https://www.mdpi.com/2079-9292/11/3/495 DOI: https://doi.org/10.3390/electronics11030495
Hassam M, Khan MA, Armghan A, Althubiti SA, Alhaisoni M, Alqahtani A, et al. A Single Stream Modified MobileNet V2 and Whale Controlled Entropy Based Optimization Framework for Citrus Fruit Diseases Recognition. IEEE Access [Internet]. 2022;10:91828–39. Available from: https://ieeexplore.ieee.org/document/9866065/ DOI: https://doi.org/10.1109/ACCESS.2022.3201338
Barbedo JGA, Castro GB. A Study on CNN-Based Detection of Psyllids in Sticky Traps Using Multiple Image Data Sources. AI [Internet]. 2020 May 18;1(2):198–208. Available from: https://www.mdpi.com/2673-2688/1/2/13 DOI: https://doi.org/10.3390/ai1020013
Dananjayan S, Tang Y, Zhuang J, Hou C, Luo S. Assessment of state-of-the-art deep learning based citrus disease detection techniques using annotated optical leaf images. Comput Electron Agric [Internet]. 2022 Feb;193:106658. Available from: https://linkinghub.elsevier.com/retrieve/pii/S016816992100675X DOI: https://doi.org/10.1016/j.compag.2021.106658
Partel V, Nunes L, Stansly P, Ampatzidis Y. Automated vision-based system for monitoring Asian citrus psyllid in orchards utilizing artificial intelligence. Comput Electron Agric [Internet]. 2019 Jul;162:328–36. Available from: https://linkinghub.elsevier.com/retrieve/pii/S016816991930554X DOI: https://doi.org/10.1016/j.compag.2019.04.022
Lee S, Choi G, Park HC, Choi C. Automatic Classification Service System for Citrus Pest Recognition Based on Deep Learning. Sensors [Internet]. 2022 Nov 18;22(22):8911. Available from: https://www.mdpi.com/1424-8220/22/22/8911 DOI: https://doi.org/10.3390/s22228911
Khattak A, Asghar MU, Batool U, Asghar MZ, Ullah H, Al-Rakhami M, et al. Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network Model. IEEE Access [Internet]. 2021;9:112942–54. Available from: https://ieeexplore.ieee.org/document/9481921/ DOI: https://doi.org/10.1109/ACCESS.2021.3096895
Syed-Ab-Rahman SF, Hesamian MH, Prasad M. Citrus disease detection and classification using end-to-end anchor-based deep learning model. Appl Intell [Internet]. 2022 Jan 13;52(1):927–38. Available from: https://link.springer.com/10.1007/s10489-021-02452-w DOI: https://doi.org/10.1007/s10489-021-02452-w
Yasmeen U, Attique Khan M, Tariq U, Ali Khan J, Asfand E. Yar M, Avais Hanif C, et al. Citrus Diseases Recognition Using Deep Improved Genetic Algorithm. Comput Mater Contin [Internet]. 2022;71(2):3667–84. Available from: https://www.techscience.com/cmc/v71n2/45812 DOI: https://doi.org/10.32604/cmc.2022.022264
Dhiman P, Kaur A, Kukreja V. Citrus Fruit Disease Detection Techniques: A Survey and Comparative Analysis of Relevant Approaches. Int J Comput Digit Syst [Internet]. 2023 Oct 1;14(1):10127–48. Available from: https://journal.uob.edu.bh/handle/123456789/5167 DOI: https://doi.org/10.12785/ijcds/140187
Khanramaki M, Askari Asli-Ardeh E, Kozegar E. Citrus pests classification using an ensemble of deep learning models. Comput Electron Agric [Internet]. 2021 Jul;186:106192. Available from: https://linkinghub.elsevier.com/retrieve/pii/S016816992100209X DOI: https://doi.org/10.1016/j.compag.2021.106192
Zhang M, Liu S, Yang F, Liu J. Classification of Canker on Small Datasets Using Improved Deep Convolutional Generative Adversarial Networks. IEEE Access [Internet]. 2019;7:49680–90. Available from: https://ieeexplore.ieee.org/document/8648422/ DOI: https://doi.org/10.1109/ACCESS.2019.2900327
Elaraby A, Hamdy W, Alanazi S. Classification of Citrus Diseases Using Optimization Deep Learning Approach. Koundal D, editor. Comput Intell Neurosci [Internet]. 2022 Feb 10;2022:1–10. Available from: https://www.hindawi.com/journals/cin/2022/9153207/ DOI: https://doi.org/10.1155/2022/9153207
Dou S, Wang L, Fan D, Miao L, Yan J, He H. Classification of Citrus Huanglongbing Degree Based on CBAM-MobileNetV2 and Transfer Learning. Sensors [Internet]. 2023 Jun 14;23(12):5587. Available from: https://www.mdpi.com/1424-8220/23/12/5587 DOI: https://doi.org/10.3390/s23125587
Zia Ur Rehman M, Ahmed F, Attique Khan M, Tariq U, Shaukat Jamal S, Ahmad J, et al. Classification of Citrus Plant Diseases Using Deep Transfer Learning. Comput Mater Contin [Internet]. 2022;70(1):1401–17. Available from: https://www.techscience.com/cmc/v70n1/44377 DOI: https://doi.org/10.32604/cmc.2022.019046
He C, Li X, Liu Y, Yang B, Wu Z, Tan S, et al. Combining multicolor fluorescence imaging with multispectral reflectance imaging for rapid citrus Huanglongbing detection based on lightweight convolutional neural network using a handheld device. Comput Electron Agric [Internet]. 2022 Mar;194:106808. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0168169922001259 DOI: https://doi.org/10.1016/j.compag.2022.106808
Barman U, Choudhury RD, Sahu D, Barman GG. Comparison of convolution neural networks for smartphone image based real time classification of citrus leaf disease. Comput Electron Agric [Internet]. 2020 Oct;177:105661. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0168169920302258 DOI: https://doi.org/10.1016/j.compag.2020.105661
Sánchez-DelaCruz E, Salazar López JP, Lara Alabazares D, Tello Leal E, Fuentes-Ramos M. Deep learning framework for leaf damage identification. Concurr Eng [Internet]. 2021 Mar 23;29(1):25–34. Available from: http://journals.sagepub.com/doi/10.1177/1063293X21994953 DOI: https://doi.org/10.1177/1063293X21994953
Janarthan S, Thuseethan S, Rajasegarar S, Lyu Q, Zheng Y, Yearwood J. Deep Metric Learning Based Citrus Disease Classification With Sparse Data. IEEE Access [Internet]. 2020;8:162588–600. Available from: https://ieeexplore.ieee.org/document/9186111/ DOI: https://doi.org/10.1109/ACCESS.2020.3021487
Sujatha R, Chatterjee JM, Jhanjhi N, Brohi SN. Performance of deep learning vs machine learning in plant leaf disease detection. Microprocess Microsyst [Internet]. 2021 Feb;80:103615. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0141933120307626 DOI: https://doi.org/10.1016/j.micpro.2020.103615
Firdaus MH, Utami E, Ariatmanto D. Detection And Classification of Citrus Diseases Based on A Combination of Features Using the Densenet-169 Model. sinkron [Internet]. 2023 Oct 1;8(4):2592–601. Available from: https://jurnal.polgan.ac.id/index.php/sinkron/article/view/12974 DOI: https://doi.org/10.33395/sinkron.v8i4.12974
Chavarro-Mesa E, Delahoz-Dominguez E, Fennix-Agudelo M, Miranda-Castro W, Angel-Diaz JE. Preliminary machine learning model for citrus greening disease (Huanglongbing-HLB) prediction in Colombia. In: 2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) [Internet]. IEEE; 2020. p. 1–4. Available from: https://ieeexplore.ieee.org/document/9247900/ DOI: https://doi.org/10.1109/ColCACI50549.2020.9247900
Chen Q, Liu X, Dong C, Tong T, Yang C, Chen R, et al. Deep Convolutional Network for Citrus Leaf Diseases Recognition. In: 2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom) [Internet]. IEEE; 2019. p. 1490–4. Available from: https://ieeexplore.ieee.org/document/9047360/ DOI: https://doi.org/10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00215
Dang-Ngoc H, Cao TNM, Dang-Nguyen C. Citrus Leaf Disease Detection and Classification Using Hierarchical Support Vector Machine. In: 2021 International Symposium on Electrical and Electronics Engineering (ISEE) [Internet]. IEEE; 2021. p. 69–74. Available from: https://ieeexplore.ieee.org/document/9418680/ DOI: https://doi.org/10.1109/ISEE51682.2021.9418680
Doh B, Zhang D, Shen Y, Hussain F, Doh RF, Ayepah K. Automatic Citrus Fruit Disease Detection by Phenotyping Using Machine Learning. In: 2019 25th International Conference on Automation and Computing (ICAC) [Internet]. IEEE; 2019. p. 1–5. Available from: https://ieeexplore.ieee.org/document/8895102/ DOI: https://doi.org/10.23919/IConAC.2019.8895102
Dongmei Z, Ke W, Hongbo G, Peng W, Chao W, Shaofeng P. Classification and identification of citrus pests based on InceptionV3 convolutional neural network and migration learning. In: 2020 International Conference on Internet of Things and Intelligent Applications (ITIA) [Internet]. IEEE; 2020. p. 1–7. Available from: https://ieeexplore.ieee.org/document/9312359/ DOI: https://doi.org/10.1109/ITIA50152.2020.9312359
Kaur B, Sharma T, Goyal B, Dogra A. A Genetic Algorithm based Feature Optimization method for Citrus HLB Disease Detection using Machine Learning. In: 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT) [Internet]. IEEE; 2020. p. 1052–7. Available from: https://ieeexplore.ieee.org/document/9214107/ DOI: https://doi.org/10.1109/ICSSIT48917.2020.9214107
Senthilkumar C, Kamarasan M. Optimal Segmentation with Back-Propagation Neural Network (BPNN) Based Citrus Leaf Disease Diagnosis. In: 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT) [Internet]. IEEE; 2019. p. 78–82. Available from: https://ieeexplore.ieee.org/document/8987749/ DOI: https://doi.org/10.1109/ICSSIT46314.2019.8987749
Senthilkumar C, Kamarasan M. An Optimal Weighted Segmentation with Hough Transform based Feature Extraction and Classification Model for Citrus Disease. In: 2020 International Conference on Inventive Computation Technologies (ICICT) [Internet]. IEEE; 2020. p. 215–20. Available from: https://ieeexplore.ieee.org/document/9112530/ DOI: https://doi.org/10.1109/ICICT48043.2020.9112530
Song C, Wang C, Yang Y. Automatic Detection and Image Recognition of Precision Agriculture for Citrus Diseases. In: 2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE) [Internet]. IEEE; 2020. p. 187–90. Available from: https://ieeexplore.ieee.org/document/9301932/ DOI: https://doi.org/10.1109/ECICE50847.2020.9301932
Wen C, Zhang H, Li H, Li H, Chen J, Guo H, et al. Multi-scene citrus detection based on multi-task deep learning network. In: 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) [Internet]. IEEE; 2020. p. 912–9. Available from: https://ieeexplore.ieee.org/document/9282909/ DOI: https://doi.org/10.1109/SMC42975.2020.9282909
Yi B, Kong B, Xu C. Multi-stage Citrus Detection based on Improved Yolov4. In: 2022 The 5th International Conference on Control and Computer Vision [Internet]. New York, NY, USA: ACM; 2022. p. 56–62. Available from: https://dl.acm.org/doi/10.1145/3561613.3561623 DOI: https://doi.org/10.1145/3561613.3561623
Bu Y, Gan H, Gan Y. Current Status and Perspectives of the Artificial Intelligence-Based Identification of Citrus Huanglongbing. In: Proceedings of the 2020 2nd International Conference on Robotics, Intelligent Control and Artificial Intelligence [Internet]. New York, NY, USA: ACM; 2020. p. 175–80. Available from: https://dl.acm.org/doi/10.1145/3438872.3439077 DOI: https://doi.org/10.1145/3438872.3439077
Saini AK, Bhatnagar R, Srivastava DK. Automatic Detection and Recognition of Citrus Fruit & Leaves Diseases for Precision Agriculture. JUCS - J Univers Comput Sci [Internet]. 2022 Sep 28;28(9):930–48. Available from: https://lib.jucs.org/article/94133/ DOI: https://doi.org/10.3897/jucs.94133
Faisal S, Javed K, Ali S, Alasiry A, Marzougui M, Attique Khan M, et al. Deep Transfer Learning Based Detection and Classification of Citrus Plant Diseases. Comput Mater Contin [Internet]. 2023;76(1):895–914. Available from: https://www.techscience.com/cmc/v76n1/53107 DOI: https://doi.org/10.32604/cmc.2023.039781
Kukreja V, Dhiman P. A Deep Neural Network based disease detection scheme for Citrus fruits. In: 2020 International Conference on Smart Electronics and Communication (ICOSEC) [Internet]. IEEE; 2020. p. 97–101. Available from: https://ieeexplore.ieee.org/document/9215359/ DOI: https://doi.org/10.1109/ICOSEC49089.2020.9215359
Sharma R, Kaur S. Convolution Neural Network based Several Orange Leave Disease Detection and Identification Methods: A Review. In: 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT) [Internet]. IEEE; 2019. p. 196–201. Available from: https://ieeexplore.ieee.org/document/8987744/ DOI: https://doi.org/10.1109/ICSSIT46314.2019.8987744
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