A CNN-based image detector for plant leaf diseases classification. (October 2022)
- Record Type:
- Journal Article
- Title:
- A CNN-based image detector for plant leaf diseases classification. (October 2022)
- Main Title:
- A CNN-based image detector for plant leaf diseases classification
- Authors:
- Falaschetti, Laura
Manoni, Lorenzo
Di Leo, Denis
Pau, Danilo
Tomaselli, Valeria
Turchetti, Claudio - Abstract:
- Graphical abstract: Our article proposes an image detector embedding a resource constrained convolutional neural network (CNN) implemented in a low cost, low power platform, named OpenMV Cam H7 Plus, to perform a real-time classification of plant disease. The CNN network so obtained has been trained on two specific datasets for plant diseases detection, the ESCA-dataset and the PlantVillage-augmented dataset, and implemented in a low-power, low-cost Python programmable machine vision camera for real-time classification. Abstract: Identifying diseases from images of plant leaves is one of the most important research areas in precision agriculture. The aim of this paper is to propose an image detector embedding a resource constrained convolutional neural network (CNN) implemented in a low cost, low power platform, named OpenMV Cam H7 Plus, to perform a real-time classification of plant disease. The CNN network so obtained has been trained on two specific datasets for plant diseases detection, the ESCA-dataset and the PlantVillage-augmented dataset, and implemented in a low-power, low-cost Python programmable machine vision camera for real-time image acquisition and classification, equipped with a LCD display showing to the user the classification response in real-time. Experimental results show that this CNN-based image detector can be effectively implemented on the chosen constrained-resource system, achieving an accuracy of about 98.10%/95.24% with a very low memory costGraphical abstract: Our article proposes an image detector embedding a resource constrained convolutional neural network (CNN) implemented in a low cost, low power platform, named OpenMV Cam H7 Plus, to perform a real-time classification of plant disease. The CNN network so obtained has been trained on two specific datasets for plant diseases detection, the ESCA-dataset and the PlantVillage-augmented dataset, and implemented in a low-power, low-cost Python programmable machine vision camera for real-time classification. Abstract: Identifying diseases from images of plant leaves is one of the most important research areas in precision agriculture. The aim of this paper is to propose an image detector embedding a resource constrained convolutional neural network (CNN) implemented in a low cost, low power platform, named OpenMV Cam H7 Plus, to perform a real-time classification of plant disease. The CNN network so obtained has been trained on two specific datasets for plant diseases detection, the ESCA-dataset and the PlantVillage-augmented dataset, and implemented in a low-power, low-cost Python programmable machine vision camera for real-time image acquisition and classification, equipped with a LCD display showing to the user the classification response in real-time. Experimental results show that this CNN-based image detector can be effectively implemented on the chosen constrained-resource system, achieving an accuracy of about 98.10%/95.24% with a very low memory cost (718.961 KB/735.727 KB) and inference time (122.969 ms/125.630 ms) tested on board for the ESCA and the PlantVillage-augmented datasets respectively, allowing the design of a portable embedded system for plant leaf diseases classification. Source files are available at https://doi.org/10.17605/OSF.IO/UCM8D . … (more)
- Is Part Of:
- HardwareX. Volume 12(2022)
- Journal:
- HardwareX
- Issue:
- Volume 12(2022)
- Issue Display:
- Volume 12, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 12
- Issue:
- 2022
- Issue Sort Value:
- 2022-0012-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-10
- Subjects:
- Image detector -- Esca disease -- convolutional neural network -- embedded systems -- plant diseases recognition
Scientific apparatus and instruments -- Periodicals
Laboratories -- Equipment and supplies -- Periodicals
Laboratories -- Equipment and supplies
Scientific apparatus and instruments
Periodicals
Electronic journals
681 - Journal URLs:
- https://www.journals.elsevier.com/hardwarex ↗
http://www.sciencedirect.com/science/journal/24680672 ↗
http://www.sciencedirect.com/ ↗ - DOI:
- 10.1016/j.ohx.2022.e00363 ↗
- Languages:
- English
- ISSNs:
- 2468-0672
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 24657.xml