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Deep Learning application for plant diseases detection

Published: 07 January 2020 Publication History

Abstract

The use of deep learning for automatic detection of plant diseases is an innovative solution for agricultural application. In this paper we present a Convolutional neural network model based on VGGnet16 architecture for the recognition of sick and healthy leaves, Several optimizer are tested to examine accuracy and model stability, the best results are obtained with Adadelta and SGD optimizer. Those models are tested on a computer and on a Raspberry pi model B

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J.G.A. Barbedo, Digital image processing techniques for detecting, quantifying and classifying plant diseases, Springer Plus 2 (1) (2013) 660--672.
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Sharada P. Mohanty, David P. Hughes, Marcel Salathé, Using Deep Learning for image-based plant disease detection, Front.Plant Sci. 7:1419.
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Guan Wang, Yu Sun, Jianxin Wang, Automatic Image Based Plant Disease Severity Estimation Using Deep Learning, Computational Intelligence and Neuroscience, volume 2017, Article ID 2917536, 8 pages.
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Bin Liu, Yun Zhang, DongJian He, Yuxiang Li, Identification of apple Leaf Diseases Based on Deep Convolutional Neural Networks. Symmetry 2018, 10, 11
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Srdjan Sladojevic, Marco Arsenovic, Andras Andrela, Dubravko Culibrk, Darko Steanovic, Deep Neural Networks Based Recognition Of Plant Diseases by Leaf Image Classification, Computational intelligence and neuroscience, volume 2016, 3289801, 11 pages
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Cited By

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  • (2024)Comparative Investigation of Deep Convolutional Networks in Detection of Plant DiseasesTürk Doğa ve Fen Dergisi10.46810/tdfd.147747613:3(37-49)Online publication date: 26-Sep-2024
  • (2024)LWSDNet: A Lightweight Wheat Scab Detection Network Based on UAV Remote Sensing ImagesRemote Sensing10.3390/rs1615282016:15(2820)Online publication date: 31-Jul-2024
  • (2024)Performance evaluation and optimization of convolutional neural network architectures for Tomato plant disease eleven classes based on augmented leaf images datasetNeural Computing and Applications10.1007/s00521-024-09670-636:20(11919-11943)Online publication date: 18-Apr-2024
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Published In

cover image ACM Other conferences
BDIoT '19: Proceedings of the 4th International Conference on Big Data and Internet of Things
October 2019
476 pages
ISBN:9781450372404
DOI:10.1145/3372938
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 ACM 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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 January 2020

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Author Tags

  1. Convolutional neural network
  2. Deep learning
  3. optimization
  4. plant diseases detection

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BDIoT'19

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BDIoT '19 Paper Acceptance Rate 75 of 136 submissions, 55%;
Overall Acceptance Rate 75 of 136 submissions, 55%

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Cited By

View all
  • (2024)Comparative Investigation of Deep Convolutional Networks in Detection of Plant DiseasesTürk Doğa ve Fen Dergisi10.46810/tdfd.147747613:3(37-49)Online publication date: 26-Sep-2024
  • (2024)LWSDNet: A Lightweight Wheat Scab Detection Network Based on UAV Remote Sensing ImagesRemote Sensing10.3390/rs1615282016:15(2820)Online publication date: 31-Jul-2024
  • (2024)Performance evaluation and optimization of convolutional neural network architectures for Tomato plant disease eleven classes based on augmented leaf images datasetNeural Computing and Applications10.1007/s00521-024-09670-636:20(11919-11943)Online publication date: 18-Apr-2024
  • (2023)Automatic Early Diagnosis of Dome Galls in Cordia Dichotoma G. Forst. Using Deep Transfer LearningIEEE Access10.1109/ACCESS.2023.328356811(59511-59523)Online publication date: 2023
  • (2023)Apple foliar leaf disease detection through improved capsule neural network architectureMultimedia Tools and Applications10.1007/s11042-023-17463-783:16(48585-48605)Online publication date: 3-Nov-2023
  • (2023)Tomato Plant Leaf Disease Detection Using Inception V3Intelligent Systems and Applications10.1007/978-981-19-6581-4_5(49-60)Online publication date: 1-Jan-2023
  • (2023)Crop Disease Detection Accelerated by GPUArtificial Intelligence Applications and Reconfigurable Architectures10.1002/9781119857891.ch8(151-166)Online publication date: 10-Feb-2023
  • (2022)Performance Analysis of AI-based Learning Models on Leaf Disease Prediction2022 4th International Conference on Circuits, Control, Communication and Computing (I4C)10.1109/I4C57141.2022.10057794(85-88)Online publication date: 21-Dec-2022
  • (2022)Comprehensive Review on Machine Learning for Plant Disease Identification and Classification with Image ProcessingProceedings of International Conference on Intelligent Cyber-Physical Systems10.1007/978-981-16-7136-4_20(247-262)Online publication date: 24-Jan-2022
  • (2021)Spatiotemporal Data of Vegetation Images for Convolutional Neural Network: Okra Case Study2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)10.1109/UEMCON53757.2021.9666664(0504-0508)Online publication date: 1-Dec-2021
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