Author:
Xiong Yonghua,Yu Shuangqing, ,
Abstract
A novel growth evaluation system for tobacco planting (GESTP) based on a B/S architecture is introduced in this paper. It mainly consists of three parts: a mobile application (mobile app), a browser terminal and a server terminal. The GESTP system is used to evaluate the growth of tobacco and give farmers planting guidance instead them having to rely on personal judgment. Once the photos of the tobacco leaf and plant are uploaded to the web server via the mobile app or the browser terminal, the application program of the server terminal is called to process the tobacco images with image processing algorithms. The results including the grade of the tobacco growth and planting guidance will be provided to the client within a 2-second timeframe, which greatly help farmers understand the growth of tobacco and take planting measures. The running result indicates that the GESTP system provides an effective and straightforward way to evaluate the growth of tobacco and provides cultivation guidance to tobacco farmers.
Publisher
Fuji Technology Press Ltd.
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Human-Computer Interaction
Reference16 articles.
1. D. Makoka, J. Drope, A. Appau et al., “Costs, revenues and profits: an economic analysis of smallholder tobacco farmer livelihoods in Malawi,” Tobacco Control, Vol.26, No.6. pp. 634-640, 2017.
2. J. de J. Rubio, “A method with neural networks for the classification of fruits and vegetables,” Soft Computing, Vol.21, No.23, pp. 7207-7220, 2017.
3. N. M. H. Hassan and A. A. Nashat, “New effective techniques for automatic detection and classification of external olive fruits defects based on image processing techniques,” Multidimensional Systems and Signal Processing, Vol.30, No.2, pp. 571-589, 2019.
4. J. Bin, F. F. Ai, W. Fan et al., “A modified random forest approach to improve multi-class classification performance of tobacco leaf grades coupled with NIR spectroscopy,” RSC Advances, Vol.6, No.36, pp. 30353-30361, 2016.
5. A. Shibata, F. Dong, and K. Hirota, “Neural Network Structure Analysis Based on Hierarchical Force-Directed Graph Drawing for Multi-Task Learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.19, No.2, pp. 225-231, 2015.
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