Keywords:
Grape leaf disease
Machine learning
Deep learning
Anthracnose
Black Rot
Downy Mildew

  • Abstract

    The diagnosis of diseases, such as grape leaf disease, on grape plantations plays a vital role in precision agriculture. When machine learning is applied to images of grape leaves, distinguishing between healthy and diseased samples with a high degree of precision becomes possible, which contributes to higher yields and improved qualities of the crops. This research aims to review grape leaf diseases, which include anthracnose, black rot, mites, and downy mildew, and the methodologies implemented for disease detection, e.g., visual, RS, ML, DL, and metaheuristic algorithms. On the basis of an analysis of 150 papers published between 2012 and 2022, the challenges associated with the current approaches are identified and categorized. This study also provides suggestions for increasing the precision and efficiency of grape leaf disease diagnosis, which may have profound effects on the agricultural industry. This paper provides a very informative review of the existing methods used for the detection of diseases in grape leaves, indicating possibilities for advancements in new detection techniques and improvements in current practices.

  • References

    1. Barros, L.B., Biasi, L.A., Carisse, O. and De Mio, L.L.M., 2015. Incidence of grape anthracnose on different VITIS labrusca and hibrid cultivars and rootstocks combination under humid subtropical climate. Australasian Plant Pathology, 44, pp.397-403.
    2. Murria, S., Kaur, N., Arora, N. and Mahal, A.K., 2018. Field reaction and metabolic alterations in grape (Vitis vinifera L.) varieties infested with anthracnose. Scientia horticulturae, 235, pp.286-293.
    3. Murria, S., Kaur, N., Arora, A. and Arora, N.K., 2018. Biochemical characterization of superior seedless variety of grape (Vitis vinifera L.) for resistance to anthracnose. Indian Phytopathology, 71, pp.399-405.
    4. Guginski-Piva, C.A., Bogo, A., Gomes, B.R., Menon, J.K., Nodari, R.O. and Welter, L.J., 2018. Morphological and molecular characterization of Colletotrichum nymphaeae and C. fructicola associated with anthracnose symptoms of grape in Santa Catarina State, southern Brazil. Journal of Plant Diseases and Protection, 125, pp.405-413.
    5. Li, Z., Zhang, S., Han, R., Zhang, H., Li, K. and Wang, X., 2019. Infection process and host responses to Elsinoë ampelina, the causal organism of grapevine anthracnose. European Journal of Plant Pathology, 155, pp.571-582.
    6. Kolhalkar, N.R. and Krishnan, V.L., 2020. Mechatronics system for diagnosis and treatment of major diseases in grape vineyards based on image processing. Materials Today: Proceedings, 23, pp.549-556.
    7. Mousavi, S. and Farahani, G., 2022. A Novel Enhanced VGG16 Model to Tackle Grapevine Leaves Diseases with Automatic Method. IEEE Access, 10, pp.111564-111578.
    8. Molitor, D., Augenstein, B., Mugnai, L., Rinaldi, P.A., Sofia, J., Hed, B., Dubuis, P.H., Jermini, M., Kührer, E., Bleyer, G. and Hoffmann, L., 2016. Composition and evaluation of a novel web-based decision support system for grape black rot control. European Journal of Plant Pathology, 144, pp.785-798.
    9. Jaisakthi, S.M., Mirunalini, P. and Thenmozhi, D., 2019, February. Grape leaf disease identification using machine learning techniques. In 2019 International Conference on Computational Intelligence in Data Science (ICCIDS) (pp. 1-6). IEEE.
    10. Ji, M., Zhang, L. and Wu, Q., 2020. Automatic grape leaf diseases identification via UnitedModel based on multiple convolutional neural networks. Information Processing in Agriculture, 7(3), pp.418-426.
    11. Tang, Z., Yang, J., Li, Z. and Qi, F., 2020. Grape disease image classification based on lightweight convolution neural networks and channelwise attention. Computers and Electronics in Agriculture, 178, p.105735.
    12. Dwivedi, R., Dey, S., Chakraborty, C. and Tiwari, S., 2021. Grape disease detection network based on multi-task learning and attention features. IEEE Sensors Journal, 21(16), pp.17573-17580.
    13. Kaur, P., Harnal, S., Tiwari, R., Upadhyay, S., Bhatia, S., Mashat, A. and Alabdali, A.M., 2022. Recognition of leaf disease using hybrid convolutional neural network by applying feature reduction. Sensors, 22(2), p.575.
    14. Jaber, L.R., 2015. Grapevine leaf tissue colonization by the fungal entomopathogen Beauveria bassiana sl and its effect against downy mildew. BioControl, 60, pp.103-112.
    15. Bruisson, S., Maillot, P., Schellenbaum, P., Walter, B., Gindro, K. and Deglène-Benbrahim, L., 2016. Arbuscular mycorrhizal symbiosis stimulates key genes of the phenylpropanoid biosynthesis and stilbenoid production in grapevine leaves in response to downy mildew and grey mould infection. Phytochemistry, 131, pp.92-99.
    16. Padol, P.B. and Sawant, S.D., 2016, December. Fusion classification technique used to detect downy and Powdery Mildew grape leaf diseases. In 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC) (pp. 298-301). IEEE.
    17. Zhang, X., Zhou, Y., Li, Y., Fu, X. and Wang, Q., 2017. Screening and characterization of endophytic Bacillus for biocontrol of grapevine downy mildew. Crop protection, 96, pp.173-179.
    18. Özer, N., Uzun, H.İ., Aktürk, B., Özer, C., Akkurt, M. and Aydın, S., 2021. Resistance assessment of grapevine leaves to downy mildew with sporulation area scoring. European Journal of Plant Pathology, 160, pp.337-348.
    19. Sanghavi, K., Sanghavi, M. and Rajurkar, A.M., 2021. Early-stage detection of Downey and Powdery Mildew grape disease using atmospheric parameters through sensor nodes. Artificial Intelligence in Agriculture, 5, pp.223-232.
    20. Hernández, I., Gutiérrez, S., Ceballos, S., Iñíguez, R., Barrio, I. and Tardaguila, J., 2021. Artificial intelligence and novel sensing technologies for assessing downy mildew in grapevine. Horticulturae, 7(5), p.103.
    21. Gutiérrez, S., Hernández, I., Ceballos, S., Barrio, I., Díez-Navajas, A.M. and Tardaguila, J., 2021. Deep learning for the differentiation of downy mildew and spider mite in grapevine under field conditions. Computers and Electronics in Agriculture, 182, p.105991.
    22. Duso, C., Pozzebon, A., Kreiter, S., Tixier, M.S. and Candolfi, M., 2012. Management of phytophagous mites in European vineyards. Arthropod management in vineyards: pests, approaches, and future directions, pp.191-217.
    23. Pennington, T., Kraus, C., Alakina, E., Entling, M.H. and Hoffmann, C., 2017. Minimal pruning and reduced plant protection promote predatory mites in grapevine. Insects, 8(3), p.86.
    24. Vogelweith, F. and Thiery, D., 2018. An assessment of the non-target effects of copper on the leaf arthropod community in a vineyard. Biological Control, 127, pp.94-100.
    25. LaPlante, E.R., Fleming, M.B., Migicovsky, Z. and Weber, M.G., 2021. Genome-Wide Association Study Reveals a Genomic Region Associated with Mite-Recruitment Phenotypes in the Domesticated Grapevine (Vitis vinifera). Genes, 12(7), p.1013.
    26. Reiff, J.M., Ehringer, M., Hoffmann, C. and Entling, M.H., 2021. Fungicide reduction favors the control of phytophagous mites under both organic and conventional viticulture. Agriculture, Ecosystems & Environment, 305, p.107172.
    27. Yang, R., Lu, X., Huang, J., Zhou, J., Jiao, J., Liu, Y., Liu, F., Su, B. and Gu, P., 2021. A multi-source data fusion decision-making method for disease and pest detection of grape foliage based on ShuffleNet V2. Remote Sensing, 13(24), p.5102.
    28. Oberti, R., Marchi, M., Tirelli, P., Calcante, A., Iriti, M. and Borghese, A.N., 2014. Automatic detection of powdery mildew on grapevine leaves by image analysis: Optimal view-angle range to increase the sensitivity. Computers and Electronics in Agriculture, 104, pp.1-8.
    29. Macmillan, C.D. and Costello, M.J., 2015. Evaluation of a brushing machine for estimating density of spider mites on grape leaves. Experimental and Applied Acarology, 67, pp.583-594.
    30. Irvin, N.A., Bistline-East, A. and Hoddle, M.S., 2016. The effect of an irrigated buckwheat cover crop on grape vine productivity, and beneficial insect and grape pest abundance in southern California. Biological Control, 93, pp.72-83.
    31. Rangel, B.M.S., Fernandez, M.A.A., Murillo, J.C., Ortega, J.C.P. and Arreguín, J.M.R., 2016, February. KNN-based image segmentation for grapevine potassium deficiency diagnosis. In 2016 International Conference on Electronics, Communications and Computers (CONIELECOMP) (pp. 48-53). IEEE.
    32. Adão, T., Pinho, T.M., Ferreira, A., Sousa, A., Pádua, L., Sousa, J., Sousa, J.J., Peres, E. and Morais, R., 2019, August. Digital Ampelographer: a CNN based preliminary approach. In Progress in Artificial Intelligence: 19th EPIA Conference on Artificial Intelligence, EPIA 2019, Vila Real, Portugal, September 3–6, 2019, Proceedings, Part I (pp. 258-271). Cham: Springer International Publishing.
    33. Nagi, R. and Tripathy, S.S., 2020. Infected area segmentation and severity estimation of grapevine using fuzzy logic. In Advances in Computational Intelligence: Proceedings of Second International Conference on Computational Intelligence 2018 (pp. 57-67). Springer Singapore.
    34. Bellvert, J., Marsal, J., Girona, J. and Zarco-Tejada, P.J., 2015. Seasonal evolution of crop water stress index in grapevine varieties determined with high-resolution remote sensing thermal imagery. Irrigation Science, 33, pp.81-93.
    35. Zovko, M., Žibrat, U., Knapič, M., Kovačić, M.B. and Romić, D., 2019. Hyperspectral remote sensing of grapevine drought stress. Precision agriculture, 20, pp.335-347.
    36. Fuentes, S., Tongson, E.J., De Bei, R., Gonzalez Viejo, C., Ristic, R., Tyerman, S. and Wilkinson, K., 2019. Non-invasive tools to detect smoke contamination in grapevine canopies, berries and wine: A remote sensing and machine learning modeling approach. Sensors, 19(15), p.3335.
    37. Moghimi, A., Pourreza, A., Zuniga-Ramirez, G., Williams, L.E. and Fidelibus, M.W., 2020. A novel machine learning approach to estimate grapevine leaf nitrogen concentration using aerial multispectral imagery. Remote Sensing, 12(21), p.3515.
    38. Arab, S.T., Noguchi, R., Matsushita, S. and Ahamed, T., 2021. Prediction of grape yields from time-series vegetation indices using satellite remote sensing and a machine-learning approach. Remote Sensing Applications: Society and Environment, 22, p.100485.
    39. Debnath, S., Paul, M., Rahaman, D.M., Debnath, T., Zheng, L., Baby, T., Schmidtke, L.M. and Rogiers, S.Y., 2021. Identifying individual nutrient deficiencies of grapevine leaves using hyperspectral imaging. Remote Sensing, 13(16), p.3317.
    40. Peng, X., Chen, D., Zhou, Z., Zhang, Z., Xu, C., Zha, Q., Wang, F. and Hu, X., 2022. Prediction of the Nitrogen, Phosphorus and Potassium Contents in Grape Leaves at Different Growth Stages Based on UAV Multispectral Remote Sensing. Remote Sensing, 14(11), p.2659.
    41. Sannakki, S.S., Rajpurohit, V.S., Nargund, V.B. and Kulkarni, P., 2013, July. Diagnosis and classification of grape leaf diseases using neural networks. In 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT) (pp. 1-5). IEEE.
    42. Patil, S.S. and Thorat, S.A., 2016, August. Early detection of grapes diseases using machine learning and IoT. In 2016 second international conference on Cognitive Computing and Information Processing (CCIP) (pp. 1-5). IEEE.
    43. Agrawal, N., Singhai, J. and Agarwal, D.K., 2017, October. Grape leaf disease detection and classification using multi-class support vector machine. In 2017 International Conference on Recent Innovations in Signal processing and Embedded Systems (RISE) (pp. 238-244). IEEE.
    44. Jaisakthi, S.M., Mirunalini, P. and Thenmozhi, D., 2019, February. Grape leaf disease identification using machine learning techniques. In 2019 International Conference on Computational Intelligence in Data Science (ICCIDS) (pp. 1-6). IEEE.
    45. Alajas, O.J., Concepcion, R., Dadios, E., Sybingco, E., Mendigoria, C.H. and Aquino, H., 2021, June. Prediction of Grape Leaf Black Rot Damaged Surface Percentage Using Hybrid Linear Discriminant Analysis and Decision Tree. In 2021 International Conference on Intelligent Technologies (CONIT) (pp. 1-6). IEEE.
    46. Ansari, A.S., Jawarneh, M., Ritonga, M., Jamwal, P., Mohammadi, M.S., Veluri, R.K., Kumar, V. and Shah, M.A., 2022. Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf Disease. Journal of Food Quality, 2022.
    47. Ghoury, S., Sungur, C. and Durdu, A., 2019, April. Real-time diseases detection of grape and grape leaves using faster r-cnn and ssd mobilenet architectures. In International conference on advanced technologies, computer engineering and science (ICATCES 2019) (pp. 39-44).
    48. Bharate, A.A. and Shirdhonkar, M.S., 2020, March. Classification of grape leaves using KNN and SVM classifiers. In 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) (pp. 745-749). IEEE.
    49. Liu, B., Tan, C., Li, S., He, J. and Wang, H., 2020. A data augmentation method based on generative adversarial networks for grape leaf disease identification. IEEE Access, 8, pp.102188-102198.
    50. Santos, T.T., de Souza, L.L., dos Santos, A.A. and Avila, S., 2020. Grape detection, segmentation, and tracking using deep neural networks and three-dimensional association. Computers and Electronics in Agriculture, 170, p.105247
    51. Rao, U.S., Swathi, R., Sanjana, V., Arpitha, L., Chandrasekhar, K. and Naik, P.K., 2021. Deep learning precision farming: grapes and mango leaf disease detection by transfer learning. Global Transitions Proceedings, 2(2), pp.535-544.
    52. Zinonos, Z., Gkelios, S., Khalifeh, A.F., Hadjimitsis, D.G., Boutalis, Y.S. and Chatzichristofis, S.A., 2021. Grape leaf diseases identification system using convolutional neural networks and Lora technology. IEEE Access, 10, pp.122-133.
    53. Ali, A., Ali, S., Husnain, M., Saad Missen, M.M., Samad, A. and Khan, M., 2022. Detection of deficiency of nutrients in grape leaves using deep network. Mathematical Problems in Engineering, 2022.
    54. Lin, J., Chen, X., Pan, R., Cao, T., Cai, J., Chen, Y., Peng, X., Cernava, T. and Zhang, X., 2022. GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases. Agriculture, 12(6), p.887.
    55. Chouhan, S.S., Kaul, A., Singh, U.P. and Jain, S., 2018. Bacterial foraging optimization based radial basis function neural network (BRBFNN) for identification and classification of plant leaf diseases: An automatic approach towards plant pathology. Ieee Access, 6, pp.8852-8863.
    56. Andrushia, A.D. and Patricia, A.T., 2020. Artificial bee colony optimization (ABC) for grape leaves disease detection. Evolving Systems, 11, pp.105-117.
    57. Jain, S. and Dharavath, R., 2021. Memetic salp swarm optimization algorithm based feature selection approach for crop disease detection system. Journal of Ambient Intelligence and Humanized Computing, pp.1-19.
    58. Umamageswari, A., Bharathiraja, N. and Irene, D.S., 2021. A novel fuzzy C-means based chameleon swarm algorithm for segmentation and progressive neural architecture search for plant disease classification. ICT Express.
    59. Yağ, İ. and Altan, A., 2022. Artificial Intelligence-Based Robust Hybrid Algorithm Design and Implementation for Real-Time Detection of Plant Diseases in Agricultural Environments. Biology, 11(12), p.1732.
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Aher, P. G., Sabnis, V., & Jain, J. K. (2025). Deep learning for grape leaf disease detection: A review . Multidisciplinary Reviews, (| Accepted Articles). Retrieved from https://malque.pub/ojs/index.php/mr/article/view/7157
  • Article viewed - 282