Classification of olive leaf diseases using deep convolutional neural networks
In recent years, there have been significant achievements in object classification with various techniques using several deep learning architectures. These architectures are now also used for classification and detection of many plant diseases. Olives are important plant species which are grown in c...
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| Published in | Neural computing & applications Vol. 33; no. 9; pp. 4133 - 4149 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
London
Springer London
01.05.2021
Springer Nature B.V |
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| Online Access | Get full text |
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| Abstract | In recent years, there have been significant achievements in object classification with various techniques using several deep learning architectures. These architectures are now also used for classification and detection of many plant diseases. Olives are important plant species which are grown in certain regions of the world. The disease types that affect the olive plants vary on the region where it is grown. This study presents a data set consisting of 3400 olive leaves samples which also includes healthy leaves so as to detect Aculus olearius and Olive peacock spot diseases, which are common olive plant diseases in Turkey. This experimental study used transfer learning methods on VGG16 and VGG19 architectures, as well as on our proposed CNN architecture. Effects of data augmentation on performance were one aim of this research. In the experimental studies which applied data augmentation the highest success value in trained models was 95%, whereas in the experiments without data augmentation the highest value was 88%. Another subject of this research is the Adam, AdaGrad, Stochastic gradient descent and RMS Prop optimization algorithms’ effect on the network’s performance. As a result of the conducted experiments, Adam and SGD optimization algorithms were generally observed to generate superior results. |
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| AbstractList | In recent years, there have been significant achievements in object classification with various techniques using several deep learning architectures. These architectures are now also used for classification and detection of many plant diseases. Olives are important plant species which are grown in certain regions of the world. The disease types that affect the olive plants vary on the region where it is grown. This study presents a data set consisting of 3400 olive leaves samples which also includes healthy leaves so as to detect Aculus olearius and Olive peacock spot diseases, which are common olive plant diseases in Turkey. This experimental study used transfer learning methods on VGG16 and VGG19 architectures, as well as on our proposed CNN architecture. Effects of data augmentation on performance were one aim of this research. In the experimental studies which applied data augmentation the highest success value in trained models was 95%, whereas in the experiments without data augmentation the highest value was 88%. Another subject of this research is the Adam, AdaGrad, Stochastic gradient descent and RMS Prop optimization algorithms’ effect on the network’s performance. As a result of the conducted experiments, Adam and SGD optimization algorithms were generally observed to generate superior results. |
| Author | Uysal, Nese Uğuz, Sinan |
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| Cites_doi | 10.3389/fpls.2017.01741 10.1016/j.compag.2018.02.016 10.1186/s13007-019-0475-z 10.3390/sym10010011 10.1016/j.still.2011.11.002 10.1016/j.inpa.2019.10.003 10.1007/s11831-018-9255-6 10.1109/JSTARS.2016.2575360 10.1016/j.scienta.2012.02.024 10.1093/jxb/erp080 10.3390/s17092022 10.1016/j.proeng.2012.07.321 10.1016/j.eaef.2019.05.001 10.1109/5.726791 10.1016/j.biosystemseng.2019.02.002 10.1016/j.cropro.2005.05.003 10.1007/978-3-031-01821-3 10.1109/SPIN.2019.8711701 10.1109/DICTA.2016.7797091 10.3389/fpls.2016.01419 10.1016/j.compag.2018.01.009 10.1016/j.ecolecon.2013.10.005 10.1016/j.cropro.2008.04.007 10.1016/j.compag.2017.09.012 10.1016/j.compeleceng.2019.04.011 10.1016/j.compag.2019.01.041 10.1007/s00521-019-04228-3 10.1016/j.compag.2018.03.032 |
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| Keywords | Deep learning Data augmentation Convolutional neural networks Transfer learning Olive plant disease |
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| References | MohantySPHughesDPSalathéMUsing deep learning for image-based plant disease detectionFront Plant Sci20167141910.3389/fpls.2016.01419 PriyadharshiniRAArivazhaganSArunMMirnaliniAMaize leaf disease classification using deep convolutional neural networksNeural Comput Appl201931128887889510.1007/s00521-019-04228-3 LisuCYuanyuanSKeWRapid diagnosis of nitrogen nutrition status in rice based on static scanning and extraction of leaf and sheath characteristicsInt J Agric Biol Eng2017103158164 LeCunYBottouLBengioYHaffnerPGradient-based learning applied to document recognitionProc IEEE199886112278232410.1109/5.726791 Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:150203167 ObanorFOJaspersMVJonesEEWalterMGreenhouse and field evaluation of fungicides for control of olive leaf spot in New ZealandCrop Prot200827101335134210.1016/j.cropro.2008.04.007 SrivastavaNHintonGKrizhevskyASutskeverISalakhutdinovRDropout: a simple way to prevent neural networks from overfittingJ Mach Learn Res20141511929195832315921318.68153 Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, 2012. pp 1097–1105 FuentesAYoonSKimSParkDA robust deep-learning-based detector for real-time tomato plant diseases and pests recognitionSensors2017179202210.3390/s17092022 AlruwailiMAlanaziSEl-GhanySAShehabAAn efficient deep learning model for olive diseases detectionInt J Adv Comput Sci Appl2019108486492 United Nations DoE, Social Affairs PD (2019) World population prospects 2019: highlights GeetharamaniGPandianAIdentification of plant leaf diseases using a nine-layer deep convolutional neural networkComput Electr Eng20197632333810.1016/j.compeleceng.2019.04.011 BarbedoJGAPlant disease identification from individual lesions and spots using deep learningBiosys Eng20191809610710.1016/j.biosystemseng.2019.02.002 Francis M, Deisy C (2019) Disease detection and classification in agricultural plants using convolutional neural networks—a visual understanding. In: 2019 6th ınternational conference on signal processing and ıntegrated networks (SPIN). IEEE, pp 1063–1068 DongHZhangXChoenYZhouYLiWLiZDry mycelium of Penicillium chrysogenum protects cotton plants against wilt diseases and increases yield under field conditionsCrop Prot200625432433010.1016/j.cropro.2005.05.003 Ji-YongSXiao-BoZJie-WenZKai-LiangWZheng-WeiCXiao-WeiHDe-TaoZHolmesMNondestructive diagnostics of nitrogen deficiency by cucumber leaf chlorophyll distribution map based on near infrared hyperspectral imagingSci Hortic201213819019710.1016/j.scienta.2012.02.024 Wallelign S, Polceanu M, Buche C (2018) Soybean plant disease ıdentification using convolutional neural network. In: The thirty-first ınternational flairs conference, 2018 UğuzSMakine öğrenmesi-Teorik yönleri ve Python uygulamaları ile bir yapay zeka ekolü2019TurkeyNobel Academic Publish(in Turkish) FerentinosKPDeep learning models for plant disease detection and diagnosisComput Electron Agric201814531131810.1016/j.compag.2018.01.009 AdeelAKhanMASharifMAzamFShahJHUmerTWanSDiagnosis and recognition of grape leaf diseases: an automated system based on a novel saliency approach and canonical correlation analysis based multiple features fusionSustain Comput Inform Syst201924100349 JiMZhangLWuQAutomatic grape leaf diseases identification via UnitedModel based on multiple convolutional neural networksInf Process Agric201910.1016/j.inpa.2019.10.003 VasilevISlaterDSpacagnaGRoelantsPZoccaVPython deep learning: exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow2019BirminghamPack Publishing Chollet F (2015) Keras. https://github.com/fchollet/keras BraunackMGarsideAMagareyRReduced tillage planting and the long-term effect on soil-borne disease and yield of sugarcane (Saccharum inter-specific hybrid) in Queensland, AustraliaSoil Tillage Res2012120859110.1016/j.still.2011.11.002 Wong SC, Gatt A, Stamatescu V, McDonnell MD (2016) Understanding data augmentation for classification: when to warp? In: 2016 international conference on digital image computing: techniques and applications (DICTA), 2016. IEEE, pp 1–6 CruzACLuvisiADe BellisLAmpatzidisYX-FIDO: an effective application for detecting olive quick decline syndrome with deep learning and data fusionFront Plant Sci20178174110.3389/fpls.2017.01741 AshourlooDAghighiHMatkanAAMobasheriMRRadAMAn investigation into machine learning regression techniques for the leaf rust disease detection using hyperspectral measurementIEEE J Sel Top Appl Earth Observ Remote Sens2016994344435110.1109/JSTARS.2016.2575360 HuGYangXZhangYWanMIdentification of tea leaf diseases by using an improved deep convolutional neural networkSustain Comput Info Syst201924100353 Al-TarawnehMSAn empirical investigation of olive leave spot disease using auto-cropping segmentation and fuzzy C-means classificationWorld Appl Sci J201323912071211 Zeytin ve Zeytin Yağı Raporu (2018). T.C. Gümrük ve Ticaret Bakanlığı Kooperatifçilik Genel Müdürlüğü AsrafHMNooritawatiMRizamMSA comparative study in kernel-based support vector machine of oil palm leaves nutrient diseaseProc Eng2012411353135910.1016/j.proeng.2012.07.321 AshqarBAAbu-NaserSSImage-based tomato leaves diseases detection using deep learningInt J Acad Eng Res20182121016 ZhangKXuZDongSCenCWuQIdentification of peach leaf disease infected by Xanthomonas campestris with deep learningEng Agric Environ Food201912438839610.1016/j.eaef.2019.05.001 LuJHuJZhaoGMeiFZhangCAn in-field automatic wheat disease diagnosis systemComput Electron Agric201714236937910.1016/j.compag.2017.09.012 ÇetinHAlaogluOMut (Mersin) ilcesindeki zeytin agaclarnda bulunan eriophyid akar turleri ve zarar sekilleriTurk Entomol Derg2006304303315 SubramanianVDeep learning with PyTorch: a practical approach to building neural network models using PyTorch2018BirminghamPackt Publishing Ltd GregoryPJJohnsonSNNewtonACIngramJSIntegrating pests and pathogens into the climate change/food security debateJ Exp Bot200960102827283810.1093/jxb/erp080 KaurSPandeySGoelSPlants disease identification and classification through leaf images: a surveyArch Comput Methods Eng201926250753010.1007/s11831-018-9255-6 Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556 OdegardIVan der VoetEThe future of food—scenarios and the effect on natural resource use in agriculture in 2050Ecol Econ201497515910.1016/j.ecolecon.2013.10.005 Council IO (2018) World oil production. https://www.internationaloliveoil.org/what-we-do/economic-affairs-promotion-unit/. Accessed 13 Dec 2019 TooECYujianLNjukiSYingchunLA comparative study of fine-tuning deep learning models for plant disease identificationComput Electron Agric201916127227910.1016/j.compag.2018.03.032 KamilarisAPrenafeta-BoldúFXDeep learning in agriculture: a surveyComput Electron Agric2018147709010.1016/j.compag.2018.02.016 KhanSRahmaniHShahSAABennamounMA guide to convolutional neural networks for computer visionSynth Lect Comput Vis2018811207 BorhanMPanigrahiSSatterMGuHEvaluation of computer imaging technique for predicting the SPAD readings in potato leavesInform Process Agric201744275282 KayaAKeceliASCatalCYalicHYTemucinHTekinerdoganBAnalysis of transfer learning for deep neural network based plant classification modelsComput Electron Agric2019158202910.1016/j.compag.2019.01.041 LiuBZhangYHeDLiYIdentification of apple leaf diseases based on deep convolutional neural networksSymmetry20181011110.3390/sym10010011 SelvarajMGVergaraARuizHSafariNElayabalanSOcimatiWBlommeGAI-powered banana diseases and pest detectionPlant Methods20191519210.1186/s13007-019-0475-z V Subramanian (5235_CR46) 2018 HM Asraf (5235_CR17) 2012; 41 A Kamilaris (5235_CR40) 2018; 147 J Lu (5235_CR26) 2017; 142 H Çetin (5235_CR10) 2006; 30 MS Al-Tarawneh (5235_CR14) 2013; 23 A Fuentes (5235_CR25) 2017; 17 B Liu (5235_CR27) 2018; 10 I Odegard (5235_CR2) 2014; 97 BA Ashqar (5235_CR12) 2018; 2 FO Obanor (5235_CR11) 2008; 27 I Vasilev (5235_CR47) 2019 G Hu (5235_CR28) 2019; 24 KP Ferentinos (5235_CR32) 2018; 145 S Kaur (5235_CR7) 2019; 26 PJ Gregory (5235_CR3) 2009; 60 H Dong (5235_CR5) 2006; 25 D Ashourloo (5235_CR15) 2016; 9 5235_CR1 N Srivastava (5235_CR44) 2014; 15 AC Cruz (5235_CR34) 2017; 8 5235_CR38 SP Mohanty (5235_CR4) 2016; 7 5235_CR39 G Geetharamani (5235_CR30) 2019; 76 5235_CR36 5235_CR8 S Khan (5235_CR43) 2018; 8 5235_CR9 5235_CR41 5235_CR42 M Borhan (5235_CR18) 2017; 4 JGA Barbedo (5235_CR29) 2019; 180 M Braunack (5235_CR6) 2012; 120 A Adeel (5235_CR13) 2019; 24 S Ji-Yong (5235_CR19) 2012; 138 EC Too (5235_CR31) 2019; 161 C Lisu (5235_CR16) 2017; 10 MG Selvaraj (5235_CR20) 2019; 15 A Kaya (5235_CR48) 2019; 158 M Ji (5235_CR23) 2019 RA Priyadharshini (5235_CR22) 2019; 31 M Alruwaili (5235_CR33) 2019; 10 S Uğuz (5235_CR37) 2019 Y LeCun (5235_CR35) 1998; 86 5235_CR45 K Zhang (5235_CR21) 2019; 12 5235_CR24 |
| References_xml | – reference: Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556 – reference: Ji-YongSXiao-BoZJie-WenZKai-LiangWZheng-WeiCXiao-WeiHDe-TaoZHolmesMNondestructive diagnostics of nitrogen deficiency by cucumber leaf chlorophyll distribution map based on near infrared hyperspectral imagingSci Hortic201213819019710.1016/j.scienta.2012.02.024 – reference: Wong SC, Gatt A, Stamatescu V, McDonnell MD (2016) Understanding data augmentation for classification: when to warp? In: 2016 international conference on digital image computing: techniques and applications (DICTA), 2016. IEEE, pp 1–6 – reference: Zeytin ve Zeytin Yağı Raporu (2018). T.C. Gümrük ve Ticaret Bakanlığı Kooperatifçilik Genel Müdürlüğü – reference: AlruwailiMAlanaziSEl-GhanySAShehabAAn efficient deep learning model for olive diseases detectionInt J Adv Comput Sci Appl2019108486492 – reference: HuGYangXZhangYWanMIdentification of tea leaf diseases by using an improved deep convolutional neural networkSustain Comput Info Syst201924100353 – reference: AdeelAKhanMASharifMAzamFShahJHUmerTWanSDiagnosis and recognition of grape leaf diseases: an automated system based on a novel saliency approach and canonical correlation analysis based multiple features fusionSustain Comput Inform Syst201924100349 – reference: LiuBZhangYHeDLiYIdentification of apple leaf diseases based on deep convolutional neural networksSymmetry20181011110.3390/sym10010011 – reference: JiMZhangLWuQAutomatic grape leaf diseases identification via UnitedModel based on multiple convolutional neural networksInf Process Agric201910.1016/j.inpa.2019.10.003 – reference: BraunackMGarsideAMagareyRReduced tillage planting and the long-term effect on soil-borne disease and yield of sugarcane (Saccharum inter-specific hybrid) in Queensland, AustraliaSoil Tillage Res2012120859110.1016/j.still.2011.11.002 – reference: PriyadharshiniRAArivazhaganSArunMMirnaliniAMaize leaf disease classification using deep convolutional neural networksNeural Comput Appl201931128887889510.1007/s00521-019-04228-3 – reference: Chollet F (2015) Keras. https://github.com/fchollet/keras – reference: FerentinosKPDeep learning models for plant disease detection and diagnosisComput Electron Agric201814531131810.1016/j.compag.2018.01.009 – reference: DongHZhangXChoenYZhouYLiWLiZDry mycelium of Penicillium chrysogenum protects cotton plants against wilt diseases and increases yield under field conditionsCrop Prot200625432433010.1016/j.cropro.2005.05.003 – reference: AshourlooDAghighiHMatkanAAMobasheriMRRadAMAn investigation into machine learning regression techniques for the leaf rust disease detection using hyperspectral measurementIEEE J Sel Top Appl Earth Observ Remote Sens2016994344435110.1109/JSTARS.2016.2575360 – reference: Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:150203167 – reference: ObanorFOJaspersMVJonesEEWalterMGreenhouse and field evaluation of fungicides for control of olive leaf spot in New ZealandCrop Prot200827101335134210.1016/j.cropro.2008.04.007 – reference: SelvarajMGVergaraARuizHSafariNElayabalanSOcimatiWBlommeGAI-powered banana diseases and pest detectionPlant Methods20191519210.1186/s13007-019-0475-z – reference: KhanSRahmaniHShahSAABennamounMA guide to convolutional neural networks for computer visionSynth Lect Comput Vis2018811207 – reference: CruzACLuvisiADe BellisLAmpatzidisYX-FIDO: an effective application for detecting olive quick decline syndrome with deep learning and data fusionFront Plant Sci20178174110.3389/fpls.2017.01741 – reference: LeCunYBottouLBengioYHaffnerPGradient-based learning applied to document recognitionProc IEEE199886112278232410.1109/5.726791 – reference: KamilarisAPrenafeta-BoldúFXDeep learning in agriculture: a surveyComput Electron Agric2018147709010.1016/j.compag.2018.02.016 – reference: LisuCYuanyuanSKeWRapid diagnosis of nitrogen nutrition status in rice based on static scanning and extraction of leaf and sheath characteristicsInt J Agric Biol Eng2017103158164 – reference: AshqarBAAbu-NaserSSImage-based tomato leaves diseases detection using deep learningInt J Acad Eng Res20182121016 – reference: MohantySPHughesDPSalathéMUsing deep learning for image-based plant disease detectionFront Plant Sci20167141910.3389/fpls.2016.01419 – reference: BarbedoJGAPlant disease identification from individual lesions and spots using deep learningBiosys Eng20191809610710.1016/j.biosystemseng.2019.02.002 – reference: Francis M, Deisy C (2019) Disease detection and classification in agricultural plants using convolutional neural networks—a visual understanding. In: 2019 6th ınternational conference on signal processing and ıntegrated networks (SPIN). IEEE, pp 1063–1068 – reference: AsrafHMNooritawatiMRizamMSA comparative study in kernel-based support vector machine of oil palm leaves nutrient diseaseProc Eng2012411353135910.1016/j.proeng.2012.07.321 – reference: LuJHuJZhaoGMeiFZhangCAn in-field automatic wheat disease diagnosis systemComput Electron Agric201714236937910.1016/j.compag.2017.09.012 – reference: GregoryPJJohnsonSNNewtonACIngramJSIntegrating pests and pathogens into the climate change/food security debateJ Exp Bot200960102827283810.1093/jxb/erp080 – reference: Wallelign S, Polceanu M, Buche C (2018) Soybean plant disease ıdentification using convolutional neural network. In: The thirty-first ınternational flairs conference, 2018 – reference: FuentesAYoonSKimSParkDA robust deep-learning-based detector for real-time tomato plant diseases and pests recognitionSensors2017179202210.3390/s17092022 – reference: ÇetinHAlaogluOMut (Mersin) ilcesindeki zeytin agaclarnda bulunan eriophyid akar turleri ve zarar sekilleriTurk Entomol Derg2006304303315 – reference: United Nations DoE, Social Affairs PD (2019) World population prospects 2019: highlights – reference: VasilevISlaterDSpacagnaGRoelantsPZoccaVPython deep learning: exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow2019BirminghamPack Publishing – reference: BorhanMPanigrahiSSatterMGuHEvaluation of computer imaging technique for predicting the SPAD readings in potato leavesInform Process Agric201744275282 – reference: Council IO (2018) World oil production. https://www.internationaloliveoil.org/what-we-do/economic-affairs-promotion-unit/. Accessed 13 Dec 2019 – reference: Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, 2012. pp 1097–1105 – reference: GeetharamaniGPandianAIdentification of plant leaf diseases using a nine-layer deep convolutional neural networkComput Electr Eng20197632333810.1016/j.compeleceng.2019.04.011 – reference: UğuzSMakine öğrenmesi-Teorik yönleri ve Python uygulamaları ile bir yapay zeka ekolü2019TurkeyNobel Academic Publish(in Turkish) – reference: ZhangKXuZDongSCenCWuQIdentification of peach leaf disease infected by Xanthomonas campestris with deep learningEng Agric Environ Food201912438839610.1016/j.eaef.2019.05.001 – reference: KayaAKeceliASCatalCYalicHYTemucinHTekinerdoganBAnalysis of transfer learning for deep neural network based plant classification modelsComput Electron Agric2019158202910.1016/j.compag.2019.01.041 – reference: OdegardIVan der VoetEThe future of food—scenarios and the effect on natural resource use in agriculture in 2050Ecol Econ201497515910.1016/j.ecolecon.2013.10.005 – reference: SubramanianVDeep learning with PyTorch: a practical approach to building neural network models using PyTorch2018BirminghamPackt Publishing Ltd – reference: KaurSPandeySGoelSPlants disease identification and classification through leaf images: a surveyArch Comput Methods Eng201926250753010.1007/s11831-018-9255-6 – reference: Al-TarawnehMSAn empirical investigation of olive leave spot disease using auto-cropping segmentation and fuzzy C-means classificationWorld Appl Sci J201323912071211 – reference: SrivastavaNHintonGKrizhevskyASutskeverISalakhutdinovRDropout: a simple way to prevent neural networks from overfittingJ Mach Learn Res20141511929195832315921318.68153 – reference: TooECYujianLNjukiSYingchunLA comparative study of fine-tuning deep learning models for plant disease identificationComput Electron Agric201916127227910.1016/j.compag.2018.03.032 – volume: 30 start-page: 303 issue: 4 year: 2006 ident: 5235_CR10 publication-title: Turk Entomol Derg – volume: 8 start-page: 1741 year: 2017 ident: 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5235_CR15 publication-title: IEEE J Sel Top Appl Earth Observ Remote Sens doi: 10.1109/JSTARS.2016.2575360 – volume: 138 start-page: 190 year: 2012 ident: 5235_CR19 publication-title: Sci Hortic doi: 10.1016/j.scienta.2012.02.024 – volume: 60 start-page: 2827 issue: 10 year: 2009 ident: 5235_CR3 publication-title: J Exp Bot doi: 10.1093/jxb/erp080 – volume: 17 start-page: 2022 issue: 9 year: 2017 ident: 5235_CR25 publication-title: Sensors doi: 10.3390/s17092022 – ident: 5235_CR1 – volume: 2 start-page: 10 issue: 12 year: 2018 ident: 5235_CR12 publication-title: Int J Acad Eng Res – volume: 10 start-page: 158 issue: 3 year: 2017 ident: 5235_CR16 publication-title: Int J Agric Biol Eng – volume: 41 start-page: 1353 year: 2012 ident: 5235_CR17 publication-title: Proc Eng doi: 10.1016/j.proeng.2012.07.321 – volume: 4 start-page: 275 issue: 4 year: 2017 ident: 5235_CR18 publication-title: Inform Process Agric – volume: 12 start-page: 388 issue: 4 year: 2019 ident: 5235_CR21 publication-title: Eng Agric Environ Food doi: 10.1016/j.eaef.2019.05.001 – volume: 86 start-page: 2278 issue: 11 year: 1998 ident: 5235_CR35 publication-title: Proc IEEE doi: 10.1109/5.726791 – volume: 180 start-page: 96 year: 2019 ident: 5235_CR29 publication-title: Biosys Eng doi: 10.1016/j.biosystemseng.2019.02.002 – volume: 25 start-page: 324 issue: 4 year: 2006 ident: 5235_CR5 publication-title: Crop Prot doi: 10.1016/j.cropro.2005.05.003 – ident: 5235_CR9 – volume: 8 start-page: 1 issue: 1 year: 2018 ident: 5235_CR43 publication-title: Synth Lect Comput Vis doi: 10.1007/978-3-031-01821-3 – ident: 5235_CR45 doi: 10.1109/SPIN.2019.8711701 – ident: 5235_CR38 doi: 10.1109/DICTA.2016.7797091 – ident: 5235_CR42 – volume-title: Makine öğrenmesi-Teorik yönleri ve Python uygulamaları ile bir yapay zeka ekolü year: 2019 ident: 5235_CR37 – volume: 7 start-page: 1419 year: 2016 ident: 5235_CR4 publication-title: Front Plant Sci doi: 10.3389/fpls.2016.01419 – volume: 145 start-page: 311 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