Tomato leaf disease identification based on attention and inception mechanism
Pages 557 - 563
Abstract
To address the problems of difficult extraction of tomato disease leaf features, scattered disease locations, and certain similarities that lead to model misclassification, this study proposes an improved ResNet50 model for tomato leaf disease identification with ten types of tomato leaves as research objects. ECA-ResNet is constructed to enhance the convolutional neural network feature map differentiation process and classification accuracy. After that, the inception module is modified and integrated to the backbone network to address the problem of misclassification caused on by similar diseases. The proposed parallel multi-branch structure of the inception module fuses the different scale features of three branches to enhance the recognition of subtle diseases such as small spots, thus improving the feature expression capability of the network. The results show that the classification with 99.24% accuracy, and 99.23% precision. The model is compared with several deep convolutional models, including AlexNet, VGG16, GoogLeNet, MobileNetV2, and other improved models. The results show that the method has better classification performance, offering new approaches for the detection of leaf diseases in tomato as well as the advancement of intelligent agriculture.
References
[1]
Atila Ü, Uçar M, Akyol K, Plant leaf disease classification using EfficientNet deep learning model[J]. Ecological Informatics, 2021, 61: 101182.
[2]
Rumpf T, Mahlein A K, Steiner U, Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance[J]. Computers and electronics in agriculture, 2010, 74(1): 91-99.
[3]
Mokhtar U, Ali M A S, Hassanien A E, Identifying two of tomatoes leaf viruses using support vector machine[M]//Information systems design and intelligent applications. Springer, New Delhi, 2015: 771-782.
[4]
Zhang S, Zhang S, Zhang C, Cucumber leaf disease identification with global pooling dilated convolutional neural network[J]. Computers and Electronics in Agriculture, 2019, 162: 422-430.
[5]
Thangaraj R, Anandamurugan S, Pandiyan P, Artificial intelligence in tomato leaf disease detection: a comprehensive review and discussion[J]. Journal of Plant Diseases and Protection, 2022, 129(3): 469-488.
[6]
Tm P, Pranathi A, SaiAshritha K, Tomato leaf disease detection using convolutional neural networks[C]//2018 eleventh international conference on contemporary computing (IC3). IEEE, 2018: 1-5.
[7]
Elhassouny A, Smarandache F. Smart mobile application to recognize tomato leaf diseases using Convolutional Neural Networks[C]//2019 International Conference of Computer Science and Renewable Energies (ICCSRE). IEEE, 2019: 1-4.
[8]
Mohanty S P, Hughes D P, Salathé M. Using deep learning for image-based plant disease detection[J]. Frontiers in plant science, 2016, 7: 1419.
[9]
Ferentinos K P. Deep learning models for plant disease detection and diagnosis[J]. Computers and electronics in agriculture, 2018, 145: 311-318.
[10]
Rahman C R, Arko P S, Ali M E, Identification and recognition of rice diseases and pests using convolutional neural networks[J]. Biosystems Engineering, 2020, 194: 112-120.
[11]
DeChant C, Wiesner-Hanks T, Chen S, Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning[J]. Phytopathology, 2017, 107(11): 1426-1432.
Recommendations
Designing self attention-based ResNet architecture for rice leaf disease classification
AbstractIn India, rice crops are very significant. Rice cultivation comprises several phases, and it is crucial to keep an eye on the crop's development to avoid any leaf diseases and to provide a good yield. To avoid yield loss, crop diseases need to be ...
Can Deep Learning Identify Tomato Leaf Disease?
This paper applies deep convolutional neural network (CNN) to identify tomato leaf disease by transfer learning. AlexNet, GoogLeNet, and ResNet were used as backbone of the CNN. The best combined model was utilized to change the structure, aiming at ...
Tomato Leaf Disease Detection Using CNN
AbstractOne of the structure of economic development is farming involved with niche skills and technologies. More than 25% of total tomato crop production is lost annually because of various plant diseases. The farmers who grow tomatoes face huge economic ...
Comments
Information & Contributors
Information
Published In
September 2023
1540 pages
ISBN:9798400707674
DOI:10.1145/3641584
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 14 June 2024
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
AIPR 2023
AIPR 2023: 2023 6th International Conference on Artificial Intelligence and Pattern Recognition
September 22 - 24, 2023
Xiamen, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 8Total Downloads
- Downloads (Last 12 months)8
- Downloads (Last 6 weeks)2
Reflects downloads up to 15 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format