Optimized convolutional neural network for identification of maize leaf diseases with adaptive ageist spider monkey optimization model
In recent years, the number of maize disease species has increased, which obviously increases the level of damages in leaves. The reason for maize leaf disease is due to variations in agriculture systems, the variants of pathogen, and it also occurs due to the scarcity of plant conservation measures...
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| Vydáno v: | International journal of information technology (Singapore. Online) Ročník 15; číslo 2; s. 877 - 891 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Singapore
Springer Nature Singapore
01.02.2023
Springer Nature B.V |
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| ISSN: | 2511-2104, 2511-2112 |
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| Abstract | In recent years, the number of maize disease species has increased, which obviously increases the level of damages in leaves. The reason for maize leaf disease is due to variations in agriculture systems, the variants of pathogen, and it also occurs due to the scarcity of plant conservation measures. The disease in maize leaves can be exhibited by varied symptoms; however, it might be complex for farmers to identify the disease in naked eye. Therefore, this paper intends to present a new automatic system for identifying and diagnosing maize leaf diseases. The proposed model includes two major phases: Proposed Feature Extraction and Classification. The first phase is feature extraction, where the proposed 4D-Local Binary Pattern (4D-LBP) based texture features will be extracted. More particularly, Dimension 1 insists pixel intensity, dimension 2 insists angle, dimension 3 insists local frequency from intensity patch and dimension 4 insists global frequency as well. Once the features get extracted, they are subjected for classification process, where the optimized Convolutional Neural Network (CNN) is used, where the count of convolutional layers is optimally tuned. For this optimal selection, a new Adaptive Opposition based Spider Monkey optimization (AOSMO), which is the enhanced version of SMO algorithm. At last, the performance of proposed work is evaluated over other traditional models with respect to accuracy. |
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| AbstractList | In recent years, the number of maize disease species has increased, which obviously increases the level of damages in leaves. The reason for maize leaf disease is due to variations in agriculture systems, the variants of pathogen, and it also occurs due to the scarcity of plant conservation measures. The disease in maize leaves can be exhibited by varied symptoms; however, it might be complex for farmers to identify the disease in naked eye. Therefore, this paper intends to present a new automatic system for identifying and diagnosing maize leaf diseases. The proposed model includes two major phases: Proposed Feature Extraction and Classification. The first phase is feature extraction, where the proposed 4D-Local Binary Pattern (4D-LBP) based texture features will be extracted. More particularly, Dimension 1 insists pixel intensity, dimension 2 insists angle, dimension 3 insists local frequency from intensity patch and dimension 4 insists global frequency as well. Once the features get extracted, they are subjected for classification process, where the optimized Convolutional Neural Network (CNN) is used, where the count of convolutional layers is optimally tuned. For this optimal selection, a new Adaptive Opposition based Spider Monkey optimization (AOSMO), which is the enhanced version of SMO algorithm. At last, the performance of proposed work is evaluated over other traditional models with respect to accuracy. |
| Author | Arjunagi, Shravankumar Patil, Nagaraj B. |
| Author_xml | – sequence: 1 givenname: Shravankumar surname: Arjunagi fullname: Arjunagi, Shravankumar email: sarjunagi@gmail.com organization: APPA Institute of Engineering and Technology – sequence: 2 givenname: Nagaraj B. surname: Patil fullname: Patil, Nagaraj B. organization: Govt College of Engineering |
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| Cites_doi | 10.1016/j.pmpp.2016.07.001 10.1016/j.aoas.2018.05.006 10.1016/j.agee.2016.06.029 10.1080/10106049.2017.1343391 10.1016/j.pmpp.2019.101441 10.1016/S2095-3119(18)62087-8 10.1016/j.suscom.2019.100353 10.1016/j.fcr.2011.09.023 10.1016/j.compag.2019.03.017 10.1007/s10658-017-1229-2 10.1109/ACCESS.2018.2844405 10.1016/j.molp.2017.02.004 10.1016/S2095-3119(19)62603-1 10.1007/s10658-018-1456-1 10.1109/ISCAS.2010.5537907 10.1016/j.cropro.2015.10.009 10.1016/j.knosys.2019.105088 10.1016/j.agsy.2018.01.016 10.1016/j.biosystemseng.2019.05.002 10.1016/S1671-2927(08)60002-4 10.1109/TIP.2014.2321495 10.1016/j.aninu.2017.07.003 |
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| Copyright | Bharati Vidyapeeth's Institute of Computer Applications and Management 2021 Bharati Vidyapeeth's Institute of Computer Applications and Management 2021. |
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| Keywords | AOSMO model CNN Performance measures Maize leaf detection 4D-LBP features |
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| References | Gensheng H, Xiaowei Y, Yan Z, Mingzhu W (2019) Identification of tea leaf diseases by using an improved deep convolutional neural network. Sustain Comput Inform Syst 24 Shao-qingWANGJiaMAMengWANGXin-huaWANGChenJGCombined application of Trichoderma harzianum SH2303 and difenoconazole-propiconazolein controlling Southern corn leaf blight disease caused by Cochliobolus heterostrophus in maizeJ Integr Agric20191892063207110.1016/S2095-3119(19)62603-1 ZhangXQiaoYMengFFanCZhangMIdentification of maize leaf diseases using improved deep convolutional neural networksIEEE Access20186303703037710.1109/ACCESS.2018.2844405 TillessenAMenkhausJJoseph-AlexanderVDevelopment of specific PCR primers for diagnosis and quantitative detection of the fungal maize pathogen Kabatiella zeaeEur J Plant Pathol2018152250350610.1007/s10658-018-1456-1 LeCun Y, Kavukvuoglu K, Farabet C (2010) Convolutional networks and applications in vision. In: Circuits and Systems, International Symposium on, 253–256 PaswelPMOlafEBoddupalliPDanMYosephBMaize lethal necrosis disease: evaluating agronomic and genetic control strategies for Ethiopia and KenyaAgric Syst201816222022810.1016/j.agsy.2018.01.016 Malusi S, Mbuyu S (2019) A computational procedure for the recognition and classification of maize leaf diseases out of healthy leaves using convolutional neural networks. AgriEngineering MubeenSRafiqueMMunisMFHChaudharyHJStudy of southern corn leaf blight (SCLB) on maize genotypes and its effect on yield”J Saudi Soc Agric Sci2017163210217 Janse van RensburgBMc LarenNWSchoemanAFlettBCThe effects of cultivar and prophylactic fungicide spray for leaf diseases on colonisation of maize ears by fumonisin producing Fusarium spp. and fumonisin synthesis in South AfricaCrop Protect201679566310.1016/j.cropro.2015.10.009 Avinash S, Akshay S, Panigrahi BK, Deep K, Rajesh K (2015) Ageist spider monkey optimization algorithm. Swarm Evol Comput FentahunMFeyissaTAbrahamAHaeRKDetection and characterization of Maize chlorotic mottle virus and Sugarcanemosaic virus associated with maize lethal necrosis disease in Ethiopia: an emerging threat to maize production in the regionEur J Plant Pathol201714941011101710.1007/s10658-017-1229-2 Cueto-GinzoASerranoLSinERodríguezRAchonMAExogenous salicylic acid treatment delays initial infection and counteracts alterations induced by Maize dwarf mosaic virus in the maize proteomePhysiol Mol Plant Pathol201696475910.1016/j.pmpp.2016.07.001 Ramar AP, Selvaraj A, Madakannu A, Annamalai M (2019) Maize leaf disease classification using deep convolutional neural networks. Neural Comput Appl, 1–9, 17 May 2019 FanK-CHungT-YA novel local pattern descriptor—local vector pattern in high-order derivative space for face recognitionIEEE Trans Image Process201423728772889322646810.1109/TIP.2014.23214951374.94102 QinYPeterB-KMingliangXQuantitative disease resistance: dissection and adoption in maizeMol Plant201710340241310.1016/j.molp.2017.02.004 Li-yuSHIXin-haiLIZhuan-fangHAOChuan-xiaoXIEGuang-tangPANComparative QTL mapping of resistance to gray leaf spot in maize based on bioinformaticsAgric Sci China20076121411141910.1016/S1671-2927(08)60002-4 NishaSSamirRNishchalKSRicardaMEInfluence of feeding crimped kernel maize silage on the course of subclinical necrotic enteritis in a broiler disease modelAnimal Nutr20173439239810.1016/j.aninu.2017.07.003 QuanLFengHLvYWangQiZongyangYMaize seedling detection under different growth stages and complex field environments based on an improved Faster R-CNNBiosys Eng201918412310.1016/j.biosystemseng.2019.05.002 WangLWangPLiangSQiXLianxiangXMonitoring maize growth conditions by training a BP neural network with remotely sensed vegetation temperature condition index and leaf area indexComput Electron Agric2019160829010.1016/j.compag.2019.03.017 InosDElhadiAOnisimoMKwabenaAElfatihMA-RJohnOMhosisiMTesting the capability of spectral resolution of the new multispectral sensors on detecting the severity of grey leaf spot disease in maize cropGeocarto Int201833111223123610.1080/10106049.2017.1343391 Pashupat V, Kamaljit K, Pannu PPS, Gurjit K, Harleen K (2019) Unraveling the metabolite signatures of maize genotypes showing differential response towards southern corn leaf blight by 1H-NMR and FTIR spectroscopy. Physiol Mol Plant Pathol 108 Li-liZXiang-liZYeFJunFHuaQPost-silking nitrogen accumulation and remobilization are associated with green leaf persistence and plant density in maizeJ Integr Agric20191881882189210.1016/S2095-3119(18)62087-8 RamKUshaMRobinGArtiBHaritRCEffect of elevated temperature and carbon dioxide levels on maydis leaf blight disease tolerance attributes in maizeAgric Ecosyst Environ20162319810410.1016/j.agee.2016.06.029 Yiwei P, Zhibin P, Yikun Wg, Wei W (2019) A new fast search algorithm for exact k-nearest neighbors based on optimal triangle-inequality-based check strategy. Knowl-Based Syst, Available online 9 October 2019, Article 105088 (in press) LyimoHJFPrattRCMnyukuRSOWComposted cattle and poultry manures provide excellent fertility and improved management of gray leaf spot in maizeField Crops Res20121269710310.1016/j.fcr.2011.09.023 ArjunagiSPatilNBTexture based leaf disease classification using machine learning techniquesInt J Eng Adv Technol (IJEAT)20199122498958 ShravankumarANagarajBPComputing amount of disease in crop using artificial intelligenceInt J Innov Technol Explor Eng (IJITEE)201981222783075 LadejobiOSalaudeenMTLava KumarPMenkirAGedilMMapping of QTLs associated with recovery resistance to streak virus disease in maizeAnn Agric Sci201863111512110.1016/j.aoas.2018.05.006 Zhi-YongZLinYShu-FengZHan-GuangWFeng-LingFImprovement of resistance to maize dwarf mosaic virus mediated by transgenic RNA interferenceJ Biotechnol20111533–4181187 Enquhone A (2017) Maize leaf diseases recognition and classification based on imaging and machine learning techniques. Int J Innov Res Comput Commun Eng 5(12) Ramesh S, Vydeki D (2019) Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm. Inf Process Agric, Available online 6 September 2019 (in press) K-C Fan (657_CR28) 2014; 23 D Inos (657_CR8) 2018; 33 S Nisha (657_CR11) 2017; 3 657_CR3 657_CR4 B Janse van Rensburg (657_CR20) 2016; 79 657_CR31 657_CR1 O Ladejobi (657_CR9) 2018; 63 A Cueto-Ginzo (657_CR13) 2016; 96 M Fentahun (657_CR5) 2017; 149 Y Qin (657_CR12) 2017; 10 SHI Li-yu (657_CR16) 2007; 6 X Zhang (657_CR2) 2018; 6 PM Paswel (657_CR10) 2018; 162 S Mubeen (657_CR18) 2017; 16 657_CR26 657_CR27 L Wang (657_CR24) 2019; 160 657_CR25 657_CR23 Z Zhi-Yong (657_CR14) 2011; 153 WANG Shao-qing (657_CR19) 2019; 18 Z Li-li (657_CR21) 2019; 18 657_CR6 A Tillessen (657_CR7) 2018; 152 HJF Lyimo (657_CR15) 2012; 126 A Shravankumar (657_CR29) 2019; 8 K Ram (657_CR17) 2016; 231 L Quan (657_CR22) 2019; 184 S Arjunagi (657_CR30) 2019; 9 |
| References_xml | – reference: Malusi S, Mbuyu S (2019) A computational procedure for the recognition and classification of maize leaf diseases out of healthy leaves using convolutional neural networks. AgriEngineering – reference: TillessenAMenkhausJJoseph-AlexanderVDevelopment of specific PCR primers for diagnosis and quantitative detection of the fungal maize pathogen Kabatiella zeaeEur J Plant Pathol2018152250350610.1007/s10658-018-1456-1 – reference: LeCun Y, Kavukvuoglu K, Farabet C (2010) Convolutional networks and applications in vision. In: Circuits and Systems, International Symposium on, 253–256 – reference: Zhi-YongZLinYShu-FengZHan-GuangWFeng-LingFImprovement of resistance to maize dwarf mosaic virus mediated by transgenic RNA interferenceJ Biotechnol20111533–4181187 – reference: FentahunMFeyissaTAbrahamAHaeRKDetection and characterization of Maize chlorotic mottle virus and Sugarcanemosaic virus associated with maize lethal necrosis disease in Ethiopia: an emerging threat to maize production in the regionEur J Plant Pathol201714941011101710.1007/s10658-017-1229-2 – reference: Cueto-GinzoASerranoLSinERodríguezRAchonMAExogenous salicylic acid treatment delays initial infection and counteracts alterations induced by Maize dwarf mosaic virus in the maize proteomePhysiol Mol Plant Pathol201696475910.1016/j.pmpp.2016.07.001 – reference: QinYPeterB-KMingliangXQuantitative disease resistance: dissection and adoption in maizeMol Plant201710340241310.1016/j.molp.2017.02.004 – reference: ShravankumarANagarajBPComputing amount of disease in crop using artificial intelligenceInt J Innov Technol Explor Eng (IJITEE)201981222783075 – reference: Avinash S, Akshay S, Panigrahi BK, Deep K, Rajesh K (2015) Ageist spider monkey optimization algorithm. Swarm Evol Comput – reference: InosDElhadiAOnisimoMKwabenaAElfatihMA-RJohnOMhosisiMTesting the capability of spectral resolution of the new multispectral sensors on detecting the severity of grey leaf spot disease in maize cropGeocarto Int201833111223123610.1080/10106049.2017.1343391 – reference: Janse van RensburgBMc LarenNWSchoemanAFlettBCThe effects of cultivar and prophylactic fungicide spray for leaf diseases on colonisation of maize ears by fumonisin producing Fusarium spp. and fumonisin synthesis in South AfricaCrop Protect201679566310.1016/j.cropro.2015.10.009 – reference: LyimoHJFPrattRCMnyukuRSOWComposted cattle and poultry manures provide excellent fertility and improved management of gray leaf spot in maizeField Crops Res20121269710310.1016/j.fcr.2011.09.023 – reference: RamKUshaMRobinGArtiBHaritRCEffect of elevated temperature and carbon dioxide levels on maydis leaf blight disease tolerance attributes in maizeAgric Ecosyst Environ20162319810410.1016/j.agee.2016.06.029 – reference: MubeenSRafiqueMMunisMFHChaudharyHJStudy of southern corn leaf blight (SCLB) on maize genotypes and its effect on yield”J Saudi Soc Agric Sci2017163210217 – reference: Yiwei P, Zhibin P, Yikun Wg, Wei W (2019) A new fast search algorithm for exact k-nearest neighbors based on optimal triangle-inequality-based check strategy. Knowl-Based Syst, Available online 9 October 2019, Article 105088 (in press) – reference: Shao-qingWANGJiaMAMengWANGXin-huaWANGChenJGCombined application of Trichoderma harzianum SH2303 and difenoconazole-propiconazolein controlling Southern corn leaf blight disease caused by Cochliobolus heterostrophus in maizeJ Integr Agric20191892063207110.1016/S2095-3119(19)62603-1 – reference: Ramar AP, Selvaraj A, Madakannu A, Annamalai M (2019) Maize leaf disease classification using deep convolutional neural networks. Neural Comput Appl, 1–9, 17 May 2019 – reference: QuanLFengHLvYWangQiZongyangYMaize seedling detection under different growth stages and complex field environments based on an improved Faster R-CNNBiosys Eng201918412310.1016/j.biosystemseng.2019.05.002 – reference: WangLWangPLiangSQiXLianxiangXMonitoring maize growth conditions by training a BP neural network with remotely sensed vegetation temperature condition index and leaf area indexComput Electron Agric2019160829010.1016/j.compag.2019.03.017 – reference: Ramesh S, Vydeki D (2019) Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm. 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| SubjectTerms | Accuracy Agricultural production Algorithms Artificial Intelligence Artificial neural networks Classification Computer Imaging Computer Science Corn Crop diseases Crops Deep learning Feature extraction Identification Image Processing and Computer Vision Leaves Machine Learning Monkeys Optimization Optimization models Original Research Pathogens Pattern Recognition and Graphics Plant diseases Software Engineering Spectrum analysis Vision |
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| Title | Optimized convolutional neural network for identification of maize leaf diseases with adaptive ageist spider monkey optimization model |
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