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 <i>Fusarium oxysporum cubense (Foc)</i> 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.
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.