Dr. Andrei Coronel Profile

Dr. Andrei Coronel

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Proceedings Article | 6 September 2017 Paper

Comparative analysis of tree classification models for detecting fusarium oxysporum f. sp cubense (TR4) based on multi soil sensor parameters

Maria Regina Justina Estuar, John Noel Victorino, Andrei Coronel, Jerelyn Co, Francis Tiausas, Chiara Veronica Señires

Proceedings Volume 10444, 1044404 (2017) https://doi.org/10.1117/12.2279126

KEYWORDS: Sensors, Remote sensing, Classification systems

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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.

Proceedings Article | 6 September 2017 Paper

Streamlining machine learning in mobile devices for remote sensing

Andrei Coronel, Ma. Regina Estuar, Kyle Kristopher Garcia, Bon Lemuel Dela Cruz, Jose Emmanuel Torrijos, Hadrian Paulo Lim, Patricia Angela Abu, John Noel Victorino

Proceedings Volume 10444, 1044407 (2017) https://doi.org/10.1117/12.2279061

KEYWORDS: Machine learning, Mobile devices, Remote sensing

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Mobile devices have been at the forefront of Intelligent Farming because of its ubiquitous nature. Applications on precision farming have been developed on smartphones to allow small farms to monitor environmental parameters surrounding crops. Mobile devices are used for most of these applications, collecting data to be sent to the cloud for storage, analysis, modeling and visualization. However, with the issue of weak and intermittent connectivity in geographically challenged areas of the Philippines, the solution is to provide analysis on the phone itself. Given this, the farmer gets a real time response after data submission. Though Machine Learning is promising, hardware constraints in mobile devices limit the computational capabilities, making model development on the phone restricted and challenging. This study discusses the development of a Machine Learning based mobile application using OpenCV libraries. The objective is to enable the detection of Fusarium oxysporum cubense (Foc) in juvenile and asymptomatic bananas using images of plant parts and microscopic samples as input. Image datasets of attached, unattached, dorsal, and ventral views of leaves were acquired through sampling protocols. Images of raw and stained specimens from soil surrounding the plant, and sap from the plant resulted to stained and unstained samples respectively. Segmentation and feature extraction techniques were applied to all images. Initial findings show no significant differences among the different feature extraction techniques. For differentiating infected from non-infected leaves, KNN yields highest average accuracy, as opposed to Naive Bayes and SVM. For microscopic images using MSER feature extraction, KNN has been tested as having a better accuracy than SVM or Naive-Bayes.

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