Show simple item record

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases

dc.contributor.authorVijaypal Singh Dhaka
dc.contributor.authorSangeeta Vaibhav Meena
dc.contributor.authorGeeta Rani
dc.contributor.authorDeepak Sinwar
dc.contributor.authorKavita
dc.contributor.authorMuhammad Fazal Ijaz
dc.contributor.authorMarcin Woźniak
dc.contributor.otherDepartment of Computer and Communication Engineering, Manipal University Jaipur, Dehmi Kalan, Jaipur, Rajasthan 303007, India
dc.contributor.otherDepartment of Computer and Communication Engineering, Manipal University Jaipur, Dehmi Kalan, Jaipur, Rajasthan 303007, India
dc.contributor.otherDepartment of Computer and Communication Engineering, Manipal University Jaipur, Dehmi Kalan, Jaipur, Rajasthan 303007, India
dc.contributor.otherDepartment of Computer and Communication Engineering, Manipal University Jaipur, Dehmi Kalan, Jaipur, Rajasthan 303007, India
dc.contributor.otherDepartment of Computer Science and Engineering, Chandigarh University, Mohali, Punjab 140413, India
dc.contributor.otherDepartment of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea
dc.contributor.otherFaculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
dc.date.accessioned2025-10-09T04:55:20Z
dc.date.available2025-10-09T04:55:20Z
dc.date.issued01-07-2021
dc.identifier.urihttps://www.mdpi.com/1424-8220/21/14/4749
dc.identifier.urihttp://digilib.fisipol.ugm.ac.id/repo/handle/15717717/40841
dc.description.abstractIn the modern era, deep learning techniques have emerged as powerful tools in image recognition. Convolutional Neural Networks, one of the deep learning tools, have attained an impressive outcome in this area. Applications such as identifying objects, faces, bones, handwritten digits, and traffic signs signify the importance of Convolutional Neural Networks in the real world. The effectiveness of Convolutional Neural Networks in image recognition motivates the researchers to extend its applications in the field of agriculture for recognition of plant species, yield management, weed detection, soil, and water management, fruit counting, diseases, and pest detection, evaluating the nutrient status of plants, and much more. The availability of voluminous research works in applying deep learning models in agriculture leads to difficulty in selecting a suitable model according to the type of dataset and experimental environment. In this manuscript, the authors present a survey of the existing literature in applying deep Convolutional Neural Networks to predict plant diseases from leaf images. This manuscript presents an exemplary comparison of the pre-processing techniques, Convolutional Neural Network models, frameworks, and optimization techniques applied to detect and classify plant diseases using leaf images as a data set. This manuscript also presents a survey of the datasets and performance metrics used to evaluate the efficacy of models. The manuscript highlights the advantages and disadvantages of different techniques and models proposed in the existing literature. This survey will ease the task of researchers working in the field of applying deep learning techniques for the identification and classification of plant leaf diseases.
dc.language.isoEN
dc.publisherMDPI AG
dc.subject.lccChemical technology
dc.titleA Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases
dc.typeArticle
dc.description.keywordsconvolutional neural networks
dc.description.keywordsdeep learning
dc.description.keywordsagriculture
dc.description.keywordsleaf
dc.description.keywordsdisease
dc.description.keywordssurvey
dc.description.doi10.3390/s21144749
dc.title.journalSensors
dc.identifier.e-issn1424-8220
dc.identifier.oaioai:doaj.org/journal:2fb1ad52a1e844b5b849f3c5f2f36f31
dc.journal.infoVolume 21, Issue 14


This item appears in the following Collection(s)

Show simple item record