Transfer learning based tomato leaf disease detection for mobile applications

Paarth Bir, Rajesh Kumar, Ghanshyam Singh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

31 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 languageEnglish
Title of host publication2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages34-39
Number of pages6
ISBN (Electronic)9781728150703
DOIs
Publication statusPublished - 2 Oct 2020
Externally publishedYes
Event2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020 - Greater Noida, India
Duration: 2 Oct 20204 Oct 2020

Publication series

Name2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020

Conference

Conference2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020
Country/TerritoryIndia
CityGreater Noida
Period2/10/204/10/20

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

Fingerprint

Dive into the research topics of 'Transfer learning based tomato leaf disease detection for mobile applications'. Together they form a unique fingerprint.

Cite this