Optimizing Pretrained Convolutional Neural Networks for Tomato Leaf Disease Detection

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 h...

Full description

Saved in:
Bibliographic Details
Published in:Complexity (New York, N.Y.) N.Y.), 2020, Vol.2020 (2020), p.1-6
Main Authors: Shah, Syed Tanveer, Yousaf, Suhail, Hamid, Muhammad, Ahmad, Iftikhar, Ahmad, Muhammad Ovais
Format: Article
Language:English
Subjects:
Accuracy
Agricultural ecology
Algorithms
Analysis
Artificial neural networks
Automation
Bactericides
Business metrics
Classification
Computer Science
Crop production
Data collection
Datasets
Datavetenskap
Deep learning
Feature extraction
Fungicides
Neural networks
Parameter identification
Plant diseases
Tomatoes
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1076-2787
1099-0526
1099-0526
DOI:10.1155/2020/8812019