Abstract
Early diagnosis of plant diseases is one of the key elements determining plant productivity. The productivity and quality of plants are significantly reduced when plant diseases are not identified and prevented in a timely manner, which results in major financial losses for producers. Olive is a plant with high added value. While the fruit and oil of olive are consumed as food, its oil is used in cosmetics, medicine, etc. It is also used in industries. In addition, active substances such as oleuropein, triterpene, maslinic acid, and flavonoid found in olive leaves are also used in the pharmaceutical industry. Considering all these valuable uses of olive, the importance of productivity is understood. Plant diseases are one of the most significant factors affecting the yield of olives. Among these diseases, fungal disease called peacock eye can spread to the whole tree through the leaves. This disease causes reduced crop production, defoliation, and rot of tree branches. In this study, an efficient method was developed to detect peacock eye disease from olive leaves. In the first stage, an original dataset of healthy and diseased leaves was created. Then, by extracting deep features from this dataset with CNN models, diseased and healthy leaf classification was performed with the transfer learning approach. As a result of the experiments, very satisfactory results were obtained around 98.63%.
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Data availability
The datasets created and analyzed during the current study are available from https://www.kaggle.com/datasets/serhathoca/zeytin.
References
Rodrigues R, Alves RC, Oliveira MBPP (2023) “Exploring Olive Pomace for Skincare Applications: A Review. Cosmetics. https://doi.org/10.3390/cosmetics10010035
Sittek L-M, Schmidts TM, Schlupp P (2021) Polyphenol-Rich Olive Mill Wastewater Extract and Its Potential Use in Hair Care Products. J Cosmet Dermatol Sci Appl. https://doi.org/10.4236/jcdsa.2021.114029
Alkhatib A, Tsang C, Tuomilehto J (2018) Olive Oil Nutraceuticals in the Prevention and Management of Diabetes: From Molecules to Lifestyle. Int J Mol Sci. https://doi.org/10.3390/ijms19072024
Terés S et al (2008) Oleic acid content is responsible for the reduction in blood pressure induced by olive oil. Proc Natl Acad Sci 105(37):13811–13816. https://doi.org/10.1073/pnas.0807500105
Kiełbasa K, Bayar Ş, Varol EA, Sreńscek-Nazzal J, Bosacka M, Michalkiewicz B (2022) Thermochemical conversion of lignocellulosic biomass - olive pomace - into activated biocarbon for CO2 adsorption. Ind Crops Prod. https://doi.org/10.1016/j.indcrop.2022.115416
Nasopoulou C, Zabetakis I (2013) Agricultural and Aquacultural Potential of Olive Pomace A Review. J Agric Sci. https://doi.org/10.5539/jas.v5n7p116
Messina G, Modica G (2022) The Role of Remote Sensing in Olive Growing Farm Management: A Research Outlook from 2000 to the Present in the Framework of Precision Agriculture Applications. Remote Sens. https://doi.org/10.3390/rs14235951
Kleef F, Salman M (2022) Antifungal Effect of Ambrosia artemisiifolia L. Extract and Chemical Fungicide Against Spilocaea oleagina Causing Olive Leaf Spot. Arab J Sci Eng 47(1):113–117. https://doi.org/10.1007/s13369-021-05397-x
Meshram V, Patil K, Meshram V, Hanchate D, Ramkteke SD (2021) Machine learning in agriculture domain: A state-of-art survey. Artif Intell Life Sci. https://doi.org/10.1016/j.ailsci.2021.100010
Alruwaili M, Alanazi S, El-Ghany SA, Shehab A (2019) An Efficient Deep Learning Model for Olive Diseases Detection. Int J Adv Comput Sci Appl IJACSA 10:8
Uğuz S (2020) Automatic Olive Peacock Spot Disease Recognition System Development by Using Single Shot Detector. Sak Univ J Comput Inf Sci 3:3
Uğuz S, Uysal N (2021) Classification of olive leaf diseases using deep convolutional neural networks. Neural Comput Appl 33(9):4133–4149. https://doi.org/10.1007/s00521-020-05235-5
Ksibi A, Ayadi M, Soufiene BO, Jamjoom MM, Ullah Z (2022) MobiRes-Net: A Hybrid Deep Learning Model for Detecting and Classifying Olive Leaf Diseases. App Sci. https://doi.org/10.3390/app122010278
Raouhi EM, Lachgar M, Hrimech H, Kartit A (2022) Optimization techniques in deep convolutional neuronal networks applied to olive diseases classification. Artif Intell Agric 6:77–89. https://doi.org/10.1016/j.aiia.2022.06.001
Alshammari H, Gasmi K, Ben Ltaifa I, Krichen M, Ben Ammar L, Mahmood MA (2022) Olive Disease Classification Based on Vision Transformer and CNN Models. Comput Intell Neurosci. https://doi.org/10.1155/2022/3998193
Alshammari HH, Taloba AI, Shahin OR (2023) Identification of olive leaf disease through optimized deep learning approach. Alex Eng J 72:213–224. https://doi.org/10.1016/j.aej.2023.03.081
Bocca P, Orellana A, Soria C, Carelli R (2023) On field disease detection in olive tree with vision systems. Array. https://doi.org/10.1016/j.array.2023.100286
W. Jackson, “The Transparency of Digital Imaging: Alpha Channel,” in Digital Image Compositing Fundamentals, W. Jackson, Ed., Berkeley, CA: Apress, 2015, pp. 39–48. doi: https://doi.org/10.1007/978-1-4842-4060-1_6.
Zhou Z, Xue-chang Z, Si-ming Z, Hua-fei X, Yue-ding S (2018) Semi-automatic Liver Segmentation in CT Images Through Intensity Separation and Region Growing. Procedia Comput Sci 131:220–225. https://doi.org/10.1016/j.procs.2018.04.206
Kiliçarslan S (2022) A novel nonlinear hybrid HardSReLUE activation function in transfer learning architectures for hemorrhage classification. Multimed Tools Appl. https://doi.org/10.1007/s11042-022-14313-w
A. G. Howard et al., ‘Mobilenets: Efficient convolutional neural networks for mobile vision applications’, ArXiv Prepr. ArXiv170404861, 2017.
K. He, X. Zhang, S. Ren, and J. Sun, ‘Deep residual learning for image recognition’, in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
Zhang Q (2022) A novel ResNet101 model based on dense dilated convolution for image classification. SN Appl Sci 4:1–13
Lin S-L (2021) Application combining VMD and ResNet101 in intelligent diagnosis of motor faults. Sensors 21(18):6065
Caie PD, Dimitriou N, Arandjelović O (2021) Chapter 8—Precision medicine in digital pathology via image analysis and machine learning. In: Cohen S (ed) Artificial intelligence and deep learning in pathology. Elsevier, pp 149–173. https://doi.org/10.1016/B978-0-323-67538-3.00008-7
Özlem A, Güngör O (2012) Rastgele orman algoritması kullanılarak çok bantlı görüntülerin sınıflandırılması. Jeodezi Ve Jeoinformasyon Derg 106:139–146
Gholami R, Fakhari N (2017) Chapter 27—Support vector machine: principles, parameters, and applications. In: Samui P, Sekhar S, Balas VE (eds) Handbook of neural computation. Academic Press, pp 515–535
Biesbroek R, Badloe S, Athanasiadis IN (2020) Machine learning for research on climate change adaptation policy integration: an exploratory UK case study. Reg Environ Change 20(3):85
R. Yacouby and D. Axman, ‘Probabilistic extension of precision, recall, and f1 score for more thorough evaluation of classification models’, in Proceedings of the first workshop on evaluation and comparison of NLP systems, 2020, pp. 79–91.
Kılıçarslan S (2022) PSO+ GWO: a hybrid particle swarm optimization and grey wolf optimization based algorithm for fine-tuning hyper-parameters of convolutional neural networks for cardiovascular disease detection. J Ambient Intell Humaniz Comput 14:87–97
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This research article was supported by Bandırma Onyedi Eylül University Scientific Research Projects Coordination Unit with the code “BAP-22-1004-010”.
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Diker, A., Elen, A., Közkurt, C. et al. An effective feature extraction method for olive peacock eye leaf disease classification. Eur Food Res Technol 250, 287–299 (2024). https://doi.org/10.1007/s00217-023-04386-8
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DOI: https://doi.org/10.1007/s00217-023-04386-8