Transfer learning based tomato leaf disease detection for mobile applications

Paarth Bir; Rajesh Kumar; Ghanshyam Singh

doi:10.1109/GUCON48875.2020.9231174

Transfer learning based tomato leaf disease detection for mobile applications

Paarth Bir
, Rajesh Kumar
, Ghanshyam Singh

Malaviya National Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

36 Citations (Scopus)

Abstract

An estimated 15-25% of potential crop production in India is lost to pests, diseases and weeds. Advanced technologies for the early detection of crop diseases are required to achieve food security. Convolutional Neural Networks have found large success in vision problems such as classification and object detection. They have been used extensively in a plethora of fields in the recent years including robotics, healthcare and agriculture. However such deep learning approaches are computationally expensive and have large memory and power requirements. The paper aims to use transfer learning to obtain effective results for use on mobile devices at reduced costs using pre-trained EfficientNetB0, MobileNetV2 and VGG 19 models as feature extractors. 15, 000 images from 9 types of diseases and one healthy class from the tomato plant are used to show the potential use of such approaches in agricultural applications.

Original language	English
Title of host publication	2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	34-39
Number of pages	6
ISBN (Electronic)	9781728150703
DOIs	https://doi.org/10.1109/GUCON48875.2020.9231174
Publication status	Published - 2 Oct 2020
Externally published	Yes
Event	2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020 - Greater Noida, India Duration: 2 Oct 2020 → 4 Oct 2020

Publication series

Name	2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020

Conference

Conference	2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020
Country/Territory	India
City	Greater Noida
Period	2/10/20 → 4/10/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 2 Zero Hunger

Keywords

Deep Learning
Deep Learning in Agriculture
Image Classification
Transfer Learning

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition
Energy Engineering and Power Technology
Electrical and Electronic Engineering
Safety, Risk, Reliability and Quality
Control and Optimization
Instrumentation

Access to Document

10.1109/GUCON48875.2020.9231174

Cite this

Bir, P., Kumar, R., & Singh, G. (2020). Transfer learning based tomato leaf disease detection for mobile applications. In 2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020 (pp. 34-39). Article 9231174 (2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GUCON48875.2020.9231174

Bir, Paarth ; Kumar, Rajesh ; Singh, Ghanshyam. / Transfer learning based tomato leaf disease detection for mobile applications. 2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 34-39 (2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020).

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title = "Transfer learning based tomato leaf disease detection for mobile applications",

abstract = "An estimated 15-25\% of potential crop production in India is lost to pests, diseases and weeds. Advanced technologies for the early detection of crop diseases are required to achieve food security. Convolutional Neural Networks have found large success in vision problems such as classification and object detection. They have been used extensively in a plethora of fields in the recent years including robotics, healthcare and agriculture. However such deep learning approaches are computationally expensive and have large memory and power requirements. The paper aims to use transfer learning to obtain effective results for use on mobile devices at reduced costs using pre-trained EfficientNetB0, MobileNetV2 and VGG 19 models as feature extractors. 15, 000 images from 9 types of diseases and one healthy class from the tomato plant are used to show the potential use of such approaches in agricultural applications.",

keywords = "Deep Learning, Deep Learning in Agriculture, Image Classification, Transfer Learning",

author = "Paarth Bir and Rajesh Kumar and Ghanshyam Singh",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020 ; Conference date: 02-10-2020 Through 04-10-2020",

year = "2020",

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doi = "10.1109/GUCON48875.2020.9231174",

language = "English",

series = "2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Bir, P, Kumar, R & Singh, G 2020, Transfer learning based tomato leaf disease detection for mobile applications. in 2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020., 9231174, 2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020, Institute of Electrical and Electronics Engineers Inc., pp. 34-39, 2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020, Greater Noida, India, 2/10/20. https://doi.org/10.1109/GUCON48875.2020.9231174

Transfer learning based tomato leaf disease detection for mobile applications. / Bir, Paarth; Kumar, Rajesh; Singh, Ghanshyam.
2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 34-39 9231174 (2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Transfer learning based tomato leaf disease detection for mobile applications

AU - Bir, Paarth

AU - Kumar, Rajesh

AU - Singh, Ghanshyam

PY - 2020/10/2

Y1 - 2020/10/2

N2 - An estimated 15-25% of potential crop production in India is lost to pests, diseases and weeds. Advanced technologies for the early detection of crop diseases are required to achieve food security. Convolutional Neural Networks have found large success in vision problems such as classification and object detection. They have been used extensively in a plethora of fields in the recent years including robotics, healthcare and agriculture. However such deep learning approaches are computationally expensive and have large memory and power requirements. The paper aims to use transfer learning to obtain effective results for use on mobile devices at reduced costs using pre-trained EfficientNetB0, MobileNetV2 and VGG 19 models as feature extractors. 15, 000 images from 9 types of diseases and one healthy class from the tomato plant are used to show the potential use of such approaches in agricultural applications.

AB - An estimated 15-25% of potential crop production in India is lost to pests, diseases and weeds. Advanced technologies for the early detection of crop diseases are required to achieve food security. Convolutional Neural Networks have found large success in vision problems such as classification and object detection. They have been used extensively in a plethora of fields in the recent years including robotics, healthcare and agriculture. However such deep learning approaches are computationally expensive and have large memory and power requirements. The paper aims to use transfer learning to obtain effective results for use on mobile devices at reduced costs using pre-trained EfficientNetB0, MobileNetV2 and VGG 19 models as feature extractors. 15, 000 images from 9 types of diseases and one healthy class from the tomato plant are used to show the potential use of such approaches in agricultural applications.

KW - Deep Learning

KW - Deep Learning in Agriculture

KW - Image Classification

KW - Transfer Learning

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U2 - 10.1109/GUCON48875.2020.9231174

DO - 10.1109/GUCON48875.2020.9231174

M3 - Conference contribution

AN - SCOPUS:85096514035

T3 - 2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020

SP - 34

EP - 39

BT - 2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020

Y2 - 2 October 2020 through 4 October 2020

ER -

Bir P, Kumar R, Singh G. Transfer learning based tomato leaf disease detection for mobile applications. In 2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 34-39. 9231174. (2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020). doi: 10.1109/GUCON48875.2020.9231174

Transfer learning based tomato leaf disease detection for mobile applications

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

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