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Proceedings Paper

Comparative analysis of tree classification models for detecting fusarium oxysporum f. sp cubense (TR4) based on multi soil sensor parameters
Author(s): Maria Regina Justina Estuar; John Noel Victorino; Andrei Coronel; Jerelyn Co; Francis Tiausas; Chiara Veronica Señires
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Paper Abstract

Use of wireless sensor networks and smartphone integration design to monitor environmental parameters surrounding plantations is made possible because of readily available and affordable sensors. Providing low cost monitoring devices would be beneficial, especially to small farm owners, in a developing country like the Philippines, where agriculture covers a significant amount of the labor market. This study discusses the integration of wireless soil sensor devices and smartphones to create an application that will use multidimensional analysis to detect the presence or absence of plant disease. Specifically, soil sensors are designed to collect soil quality parameters in a sink node from which the smartphone collects data from via Bluetooth. Given these, there is a need to develop a classification model on the mobile phone that will report infection status of a soil. Though tree classification is the most appropriate approach for continuous parameter-based datasets, there is a need to determine whether tree models will result to coherent results or not. Soil sensor data that resides on the phone is modeled using several variations of decision tree, namely: decision tree (DT), best-fit (BF) decision tree, functional tree (FT), Naive Bayes (NB) decision tree, J48, J48graft and LAD tree, where decision tree approaches the problem by considering all sensor nodes as one. Results show that there are significant differences among soil sensor parameters indicating that there are variances in scores between the infected and uninfected sites. Furthermore, analysis of variance in accuracy, recall, precision and F1 measure scores from tree classification models homogeneity among NBTree, J48graft and J48 tree classification models.

Paper Details

Date Published: 6 September 2017
PDF: 10 pages
Proc. SPIE 10444, Fifth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2017), 1044404 (6 September 2017); doi: 10.1117/12.2279126
Show Author Affiliations
Maria Regina Justina Estuar, Ateneo de Manila Univ. (Philippines)
John Noel Victorino, Ateneo de Manila Univ. (Philippines)
Andrei Coronel, Ateneo de Manila Univ. (Philippines)
Jerelyn Co, Ateneo de Manila Univ. (Philippines)
Francis Tiausas, Ateneo de Manila Univ. (Philippines)
Chiara Veronica Señires, Ateneo de Manila Univ. (Philippines)


Published in SPIE Proceedings Vol. 10444:
Fifth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2017)
Kyriacos Themistocleous; Silas Michaelides; Giorgos Papadavid; Vincent Ambrosia; Gunter Schreier; Diofantos G. Hadjimitsis, Editor(s)

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