Search: id:"swepub:oai:DiVA.org:kau-80334" >
Optimizing Pretrain...
Optimizing Pretrained Convolutional Neural Networks for Tomato Leaf Disease Detection
-
- Ahmad, Iftikhar (author)
- University of Engineering and Technology, PAK
-
- Hamid, Muhammad (author)
- University of Engineering and Technology, PAK
-
- Yousaf, Suhail (author)
- University of Engineering and Technology, PAK
-
show more...
-
- Tanveer Shah, Syed (author)
- The University of Agriculture, PAK
-
- Ahmad, Muhammad Ovais (author)
- Karlstads universitet,Institutionen för matematik och datavetenskap (from 2013),SQUAD - SOFTWARE QUALITY AND DIGITAL MODERNISATION
-
show less...
-
(creator_code:org_t)
- Hindawi Publishing Corporation, 2020
- 2020
- English.
-
In: Complexity. - : Hindawi Publishing Corporation. - 1076-2787 .- 1099-0526. ; 2020
- Related links:
-
https://doi.org/10.1...
-
show more...
-
https://downloads.hi...
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
show less...
Abstract
Subject headings
Close
- Vegetable and fruit plants facilitate around 7.5 billion people around the globe, playing a crucial role in sustaining life on the planet. The rapid increase in the use of chemicals such as fungicides and bactericides to curtail plant diseases is causing negative effects on the agro-ecosystem. The high scale prevalence of diseases in crops affects the production quantity and quality. Solving the problem of early identification/diagnosis of diseases by exploiting a quick and consistent reliable method will benefit the farmers. In this context, our research work focuses on classification and identification of tomato leaf diseases using convolutional neural network (CNN) techniques. We consider four CNN architectures, namely, VGG-16, VGG-19, ResNet, and Inception V3, and use feature extraction and parameter-tuning to identify and classify tomato leaf diseases. We test the underlying models on two datasets, a laboratory-based dataset and self-collected data from the field. We observe that all architectures perform better on the laboratory-based dataset than on field-based data, with performance on various metrics showing variance in the range 10%–15%. Inception V3 is identified as the best performing algorithm on both datasets.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Software Engineering (hsv//eng)
Keyword
- Computer Science
- Datavetenskap
Publication and Content Type
- ref (subject category)
- art (subject category)
Find in a library
-
Complexity
(Search for host publication in LIBRIS)
To the university's database