IoTs and Mobile Application Using Deep Learning Analytics for Leaf Disease Tracking in Bush Tomatoes

IoTs and Mobile Application Using Deep Learning Analytics for Leaf Disease Tracking in Bush Tomatoes

  IJETT-book-cover           
  
© 2024 by IJETT Journal
Volume-72 Issue-5
Year of Publication : 2024
Author : Wongpanya S. Nuankaew, Veerapat Thongdee, Weerachai Kojakung, Pratya Nuankaew
DOI : 10.14445/22315381/IJETT-V72I5P115

How to Cite?

Wongpanya S. Nuankaew, Veerapat Thongdee, Weerachai Kojakung, Pratya Nuankaew, " IoTs and Mobile Application Using Deep Learning Analytics for Leaf Disease Tracking in Bush Tomatoes," International Journal of Engineering Trends and Technology, vol. 72, no. 5, pp. 141-148, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I5P115

Abstract
The research objectives consist of two goals. The first objective is to study, design, and develop a low-bush tomato greenhouse control system. The second objective is to construct a mobile application using deep learning analytics for tracking leaf disease in bush tomatoes. The research data was a study of 400 diseased and normal tomato leaves. Information on tomato leaf diseases studied consisted of six diseases: Early blight, Leaf spot, Leaf blight, Late blight, Leaf mold, and Powdery mildew. The results showed that the developed system can respond effectively to detecting tomato leaf diseases. In addition, the application can perform treatment spraying tasks automatically and manually. From the research results, this research is beneficial and deserves further promotion and development.

Keywords
Leaf disease tracking, Internet of Things, Mobile application for Leaf disease, Deep learning, Treatment.

References
[1] Peter Rosset, Robert Rice, and Michael Watts, “Thailand and the World Tomato: Globalization, New Agricultural Countries (NACS) and the Agrarian Question,” The International Journal of Sociology of Agriculture and Food, vol. 8, pp. 71-94, 1999.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Tamotsu Murai et al., “Damage to Tomato by Ceratothripoides Claratris (Shumsher) (Thysanoptera: Thripidae) in Central Thailand and a Note on its Parasitoid, Goetheana Shakespearei Girault (Hymenoptera: Eulophidae),” Applied Entomology and Zoology, vol. 35, no. 4, pp. 505-507, 2000.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Nina Isabella Moeller et al., “Measuring Agroecology: Introducing a Methodological Framework and a Community of Practice Approach,” Elementa: Science of the Anthropocene, vol. 11, no. 1, pp. 1-14, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Zahid Ullah et al., “EffiMob-Net: A Deep Learning-Based Hybrid Model for Detection and Identification of Tomato Diseases Using Leaf Images,” Agriculture, vol. 13, no. 3, pp. 1-13, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Sami Ur Rahman et al., “Image Processing Based System for the Detection, Identification and Treatment of Tomato Leaf Diseases,” Multimedia Tools and Applications, vol. 82, no. 6, pp. 9431-9445, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Omneya Attallah, “Tomato Leaf Disease Classification via Compact Convolutional Neural Networks with Transfer Learning and Feature Selection,” Horticulturae, vol. 9, no. 2, pp. 1-19, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Antonio Guerrero-Ibañez, and Angelica Reyes-Muñoz, “Monitoring Tomato Leaf Disease through Convolutional Neural Networks,” Electronics, vol. 12, no. 1, pp. 1-15, 2023.
[CrossRef] [Google Scholar] [Publisher Link]