MAPPING OF HIGH VALUE CROPS THROUGH AN OBJECT-BASED SVM MODEL USING LIDAR DATA AND ORTHOPHOTO IN AGUSAN DEL NORTE PHILIPPINES

This research describes the methods involved in the mapping of different high value crops in Agusan del Norte Philippines using LiDAR. This project is part of the Phil-LiDAR 2 Program which aims to conduct a nationwide resource assessment using LiDAR. Because of the high resolution data involved, the methodology described here utilizes object-based image analysis and the use of optimal features from LiDAR data and Orthophoto. Object-based classification was primarily done by developing rule-sets in eCognition. Several features from the LiDAR data and Orthophotos were used in the development of rule-sets for classification. Generally, classes of objects can't be separated by simple thresholds from different features making it difficult to develop a rule-set. To resolve this problem, the image-objects were subjected to Support Vector Machine learning. SVMs have gained popularity because of their ability to generalize well given a limited number of training samples. However, SVMs also suffer from parameter assignment issues that can significantly affect the classification results. More specifically, the regularization parameter C in linear SVM has to be optimized through cross validation to increase the overall accuracy. After performing the segmentation in eCognition, the optimization procedure as well as the extraction of the equations of the hyper-planes was done in Matlab. The learned hyper-planes separating one class from another in the multi-dimensional feature-space can be thought of as super-features which were then used in developing the classifier rule set in eCognition. In this study, we report an overall classification accuracy of greater than 90% in different areas

Knowledge and compliance of De La Salle Medical and Health Sciences Institute (DLSMHSI) students on ergonomic principles and factors during online learning of AY 2021-2022

Ergonomics has been a vital aspect to consider in the current online setting since distance learning has become the norm as it significantly affects comfort, productivity, and motivation. Therefore, this study aimed to determine and describe the knowledge and compliance regarding ergonomic principles of De La Salle Medical and Health Sciences Institute (DLSMHSI) students and the ergonomic factors that could affect the knowledge and compliance of DLSMHSI students in A.Y. 2021-2022. A descriptive quantitative research design was utilized in the form of disseminating survey forms for data gathering among different courses throughout the institution of DLSMHSI, Dasmarinas, Cavite, Philippines following purposive sampling technique – total enumeration. A total of 571 students from the different courses responded to the survey forms disseminated. The study revealed that the respondents are most knowledgeable regarding the ergonomic principle of maintaining neutral postures (77.82%) and are most compliant with the principle of environmental modifications (4.15 using a 5-point Likert Scale). In addition, respondents are most knowledgeable regarding psychosocial factors (88.51%) and are most compliant with environmental factors (3.86 using a 5-point Likert Scale). The results of the study could provide a basis to promote awareness regarding the ergonomics of students’ workstations as well as providing information to develop interventions that would be able to address potential issues in the utilization of ergonomics by the students

Crop height monitoring using a consumer-grade camera and UAV Technology

Recent advances in the ability to capture high spatial resolution images by unmanned aerial vehicles (UAVs) have shown the potential of this technology for a wide range of application including exploring the effects of different external stimuli when monitoring environmental and structural variables. In this paper, we show the application of UAV technology for crop height monitoring and modelling to provide quantitative crop growth data and demonstrate the remote sensing and photogrammetric capabilities of the technology to the farming industry. This study was carried out in a field trial involving a combination of six wheat varieties and three different fungicide treatments. The UAV imagery of the field trial site was captured on five occasions throughout crop development. These were used to create digital surface models from which crop surface models (CSMs) were extracted for the cropped areas. Crop heights are estimated from the photogrammetric derived CSMs and are compared against the reference heights captured using Real-Time Kinematic Global Navigation Satellite System (GNSS) to validate the CSMs. Furthermore, crop growth differences among varieties are analysed; and crop height correlations with grain yield as well as with independently estimated vegetation indices are evaluated. These evaluations show that the technology is suitable (with average bias range 2–10 cm depending on wind conditions relative to GNSS height) and has potential for quantitative and qualitative monitoring of canopy and/or crop height and growth