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Research on Disease Detection Systems in Tomato Plants using Artificial Intelligence

by Nguyen Le Dung, Tran Hau Van Toan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 36
Year of Publication: 2020
Authors: Nguyen Le Dung, Tran Hau Van Toan
10.5120/ijca2020920924

Nguyen Le Dung, Tran Hau Van Toan . Research on Disease Detection Systems in Tomato Plants using Artificial Intelligence. International Journal of Computer Applications. 175, 36 ( Dec 2020), 24-29. DOI=10.5120/ijca2020920924

@article{ 10.5120/ijca2020920924,
author = { Nguyen Le Dung, Tran Hau Van Toan },
title = { Research on Disease Detection Systems in Tomato Plants using Artificial Intelligence },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2020 },
volume = { 175 },
number = { 36 },
month = { Dec },
year = { 2020 },
issn = { 0975-8887 },
pages = { 24-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number36/31685-2020920924/ },
doi = { 10.5120/ijca2020920924 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:40:26.321085+05:30
%A Nguyen Le Dung
%A Tran Hau Van Toan
%T Research on Disease Detection Systems in Tomato Plants using Artificial Intelligence
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 36
%P 24-29
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This working personally discusses how to approach the Raspberry Pi 3B + Board in combination with the 8MP Raspberry Pi V2 camera, and at the same time about algorithm knowledge with the Raspberry Pi 3B + board, and also be able to communicate with reduction motors. V1 speed, RC servo motor, H bridge circuit. During the implementation of the individual use object detection method to perform identification and detection of tomato disease. Combine with a self-driving car with a camera to make it possible to sample tomato leaf disease in the most realistic way.

References
  1. Q. H. Cap, K. Suwa, E. Fujita, S. Kagiwada, H. Uga, H. Iyatomi, An End-To-End Practical Plant Disease Diagnosis System For Wide-Angle Cucumber Images, International Journal of Engineering & Technology, 2018, 106-111.
  2. Marko Arsenovic, Mirjana Karanovic, Srdjan Sladojevic, Darko Stefanovic, Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection, 2019.
  3. Amanda Ramcharan, Peter McCloskey, Kelsee Baranowski, Neema Mbilinyi, Latifa Mrisho, Mathias Ndalahwa, James Legg, David P. Hughes, A Mobile-Based Deep Learning Model for Cassava Disease Diagnosis, 2019.
  4. Lalitpur, Dhapakhel, TOMATO PLANT DISEASES DETECTION SYSTEM USING IMAGE PROCESSING, 2018.
  5. Alexander Driaba, Volgograd, Recognition of Various Objects from a Certain Categorical Set in Real Time Using Deep Convolutional Neural Networks, 2019.
  6. Manhnh, HOW TO SELECT RIGHT DEEP LEARNING MODEL FOR OBJECT DETECTION APPLICATIONS, 2019.
  7. Chengwei, How to train an object detection model easy for free, 2019.
  8. Https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_model_zoo.md
  9. Https://www.kaggle.com/emmarex/plantdisease
Index Terms

Computer Science
Information Sciences

Keywords

Raspberry Pi 3 Yolo

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