Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Artificial Intelligence Review
Erschienen in: Artificial Intelligence Review 1/2021

28.05.2020

Image classifiers and image deep learning classifiers evolved in detection of Oryza sativa diseases: survey

verfasst von: N. V. Raja Reddy Goluguri, K. Suganya Devi, Nagesh Vadaparthi

Erschienen in: Artificial Intelligence Review | Ausgabe 1/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Growth in consumption of Oryza sativa (rice) has led the farmers across Asian countries to cultivate Oryza sativa, with an impact of 2.5 percent increase in the cultivation of the crop every year. Along with the growth in Oryza sativa cultivation, there are new challenges that are faced by the farmers in terms of diseases. The absence of information to recognize what sort of infection the plant is influenced with during the harvest cycle drives the farmers over the globe to lose 37 percent of the production. Involving technology to identify these diseases during the harvest cycle will help the farmers to get benefitted by attaining better yields. Deep learning being a latest technology playing a vital role in helping human in many aspects. A thorough review of the research papers on the various classifiers used in the identification of Oryza sativa diseases was carried out and the survey was tabulated and presented.
Vorheriger Artikel Non-goal oriented dialogue agents: state of the art, dataset, and evaluation
Nächster Artikel Improving coalition structure search with an imperfect algorithm: analysis and evaluation results

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
Abu Bakar MN, Abdullah AH, Abdul Rahim N, Yazid H, Misman SN, Masnan MJ (2018) Rice leaf blast disease detection using multi-level colour image thresholding. J Telecommun Electron Comput Eng (JTEC) 10:1–15
Alex Sherstinsky (2020) Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena 401
Archana KS, Arun S (2018) Automatic rice leaf disease segmentation using image processing techniques. Int J Eng Technol (UAE)
Bhagyashri SG, Gajanan KB (2017) Computer vision based approach to detect rice leaf diseases using texture and color descriptors. In: International conference on inventive computing and informatics (ICICI)
Chowdhury RR, Preetom SA, Eunus Ali M, Mohammad AIK, Sajid HA, Farzana N, Abu W (2019) Identification and recognition of rice diseases and pests using deep convolutional neural networks
Dipalee G, Siddhartha C (2015) Discrete wavelet transform for image processing. Int J Emerg Technol Adv Eng 4(3)
Divya S, Rajesh W, Manasi G (2015) A review: color feature extraction methods for content based image retrieval. IJCEM Int J Comput Eng Manag 18(3):9–14
Dorothy R, Joany R, Joseph RR, Santhana SP, Rajendran S (2015) Image enhancement by Histogram equalization. Int Nano Corrosion Sci Eng 2(4):21–30
Duncan AV, Bao-Rong L, Norihiko T (2008) The evolving story of rice evolution. Plant Sci 174(4):394–408CrossRef
Fatemeh H, Mohammad KS (2017) A survey on classification techniques for opinion mining and sentiment analysis. Artif Intell Rev 52:1495–1545
Gayathridevi T, Neelamegam P (2018) Image processing based rice plant leaves diseases in Thanjavur, Tamilnadu. Cluster Comput 1–14
Ghassan Mahmoud HA, Ahmed Mohamed A (2015) Edge detection methods. In: 2nd World symposium on Web applications and networking (WSWAN), Sousse
Gonzalez Fernandez I, Iglesias-otero MA, Esteki M, Moldes OA, Mejuto JC, Simal Gandara J (2019) A critical review on the use of artificial neural networks in olive oil production, characterization and authentication. Crit Rev Food Sci Nutr 59(12):104
Humeau H, Anne (2019) Texture feature extraction methods: a survey. IEEE Access 7:8975–9001CrossRef
Jiang J (1999) Image compression with neural networks—A survey. Sig Process Image Commun 14(9):737–760CrossRef
Jurgen S (2014) Deep learning in neural networks: an overview. Neural Networks 61:85–117
Kamlesh L, Murarka PD, Narendra SC (2015) Color space transformation for visual enhancement of noisy color image. Int J ICT Manag 3(2)
Komal B, Mehwish B, Mariam R (2019) Detection and classification of rice diseases: an automated approach using textural features. Mehran Univ Res J Eng Technol 38:239–250CrossRef
Konstantinides K, Bhaskaran V, Beretta G (1999) Image sharpening in the JPEG domain. IEEE Trans Image Process 8(6):874–878CrossRef
Libo L, Guomin Z (2009) Extraction of the rice leaf disease image based on BP neural network. In: International conference on computational intelligence and software engineering
Lu Y, Yi S, Zeng N, Liu Y, Zhang Y (2017) Identification of rice diseases using deep convolutional neural networks. Neurocomputing 267
Maohua X, You M, Zhixiang F, Ziang D, Shishuang H, Lei S (2018) Rice blast recognition based on principal component analysis and neural network. Comput Electron Agric 154:482–490CrossRef
Mehwish R, Muhammad S, Mudassar R (2014) Image compression: a survey. Res J Appl Sci Eng Technol 7(4):656–672CrossRef
Narmadha RP, Arulvadivu G (2017) Disease symptoms identification in paddy leaf using image processing. Adv Comput Sci Technol 10:2213–2223
Naveen T, Shruti K, Manoj Kumar P (2016) Comparison of digital image segmentation techniques- a research review. Int J Comput Sci Mobile Comput 5(5):215–220
Neha M, Priyanka BR, Soumya GH, Pooja R (2019) Paddy leaf disease detection using image processing and machine learning. Int J Innov Res Electr Electron Instrum Control Eng 7(2)
Ning C, Wenjing L, Ruizhen B, An C (2017) Application of computational intelligence technologies in emergency management: a literature review. Artif Intell Rev 52(3):2131–2168
Orillo JW, Dela Cruz J, Agapito L, Satimbre PJ, Valenzuela I (2014) Identification of diseases in rice plant (Oryza sativa) using back propagation artificial neural network. In: IEEE international conference humanoid, nanotechnology, information technology communication and control, environment and management (HNICEM), Palawan, Philippines
Pankaj Kumar (2014) Image enhancement using histogram equalization and histogram specification on different color spaces, Rourkela
Phadikar S, Sil J (2008) Rice disease identification using pattern recognition techniques. In: International conference on computer and information technology
Prajapati HB, Shah J, Dabhi V (2017) Detection and classification of rice plant diseases. Intell Decis Technol 11:357–373CrossRef
Ramesh S, Vydeki D (2018) Rice blast disease detection and classification using machine learning algorithm. In: International conference on micro-electronics and telecommunication engineering
Rana A, Rawat AS, Bijalwan A, Bahuguna H (2018) Application of multi layer (perceptron) artificial neural network in the diagnosis system: a systematic review. In: International conference on research in intelligent and computing in engineering (RICE), San Salvador
Sano Y, Sano R, Morishima H (1984) Neighbour effects between two co-occurring rice species, Oryza sativa and O. glaberrima. J Appl Ecol 21(1):245–254CrossRef
Sanyal P, Patel SC (2008) Pattern recognition method to detect two diseases in rice plants. Imag Sci J 56:319–325CrossRef
Shakeel A, Abdul H, Khushal K, Isfahan T, Mehboob A, Faisal S (2019) Evaluation of synergistic effect of nanoparticles with antibiotics against enteric pathogens. Appl Nanosci
Shakeel A, Isfahan T, Kashif SH, Khushal K, Muhammad S, Mehboob A, Faisal S (2019b) Synthesis of silver nanoparticles using leaves of Catharanthus roseus and their antimicrobial activity. Appl Nanosci, DEC
Suganya Devi K, Vaishnave MP, Srinivasan P, Arulperumjothi G (2019) Detection and classification of groundnut leaf diseases using KNN classifier. In: 2019 IEEE international conference on system, computation, automation and networking (ICSCAN)
Sujung K, Wook-joong K, Seong-Dae K (2008) Automatic white balance based on adaptive feature selection with standard illuminants. In: 5th IEEE international conference on image processing, San Diego
Takuya K, Yutaka H (2018) Development of classification system of rice disease using artificial intelligence. In: IEEE international conference on systems, man, and cybernetics (SMC)
Taohodul I, Manish S, Sudipto B, Rudra R (2018) A faster technique on rice disease detectionusing image processing of affected area in agro-field. In: International conference on inventive communication and computational technologies (ICICCT 2018)
Thao N, EUn Ae P, Jiho H, Dong Chul P, Soo Young M (2013) Object detection using scale invarinat feature transform. In: Genetic and evolutionary computing, Prague, Springer, pp 65–72
Wan-Jie L, Hong Z, Gu-feng Z, Hong-XIn C (2019) Rice blast disease recognition using a deep convolutional neural network. Sci Rep 9
Wu J, Zhiming C, Victor SS, Pengpeng Z, Dongliang S, Hengrong GS (2013) A comparative study of SIFT and its variants. Measure Sci Rev 13(3):122–132CrossRef
Xiao L, Ying-lan M, Li-zhuang M, RUi-Ling Z (2014) A survey for image resizing. J Zhejiang Univ Sci C 697–716
Yao Q, Guan Z, Zhou Y, Tang J, Hu Y, Yang B (2009) Application of support vector machine for detecting rice diseases using shape and color texture features. In: International conference on engineering computation
Zahid I, Muhammad AK, Muhammad S, Jamal HS, Muhammad HU, Kashif J (2018) An automated detection and classification of citrus plant diseases using image processing techniques: a review. Comput Electron Agric 12–32
ZIegler RS, Leong AS, Teng PS (1994) Rice Blast Disease, CAB International
Metadaten
Titel
Image classifiers and image deep learning classifiers evolved in detection of Oryza sativa diseases: survey
verfasst von
N. V. Raja Reddy Goluguri
K. Suganya Devi
Nagesh Vadaparthi
Publikationsdatum
28.05.2020
Verlag
Springer Netherlands
Erschienen in
Artificial Intelligence Review / Ausgabe 1/2021
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI
https://doi.org/10.1007/s10462-020-09849-y

Weitere Artikel der Ausgabe 1/2021

Artificial Intelligence Review 1/2021 Zur Ausgabe

OriginalPaper

Survey on evaluation methods for dialogue systems

OriginalPaper

Projection wavelet weighted twin support vector regression for OFDM system channel estimation

OriginalPaper

A review on machine learning in 3D printing: applications, potential, and challenges

OriginalPaper

CovidSens: a vision on reliable social sensing for COVID-19

OriginalPaper

Metaheuristic-based adaptive curriculum sequencing approaches: a systematic review and mapping of the literature

OriginalPaper

Non-goal oriented dialogue agents: state of the art, dataset, and evaluation

Premium Partner

    Bildnachweise