Knowing rainforest environments is rendered challenging by distance, vegetation intensity, and coverage; however, knowing the complexity and sustainability of these landscapes is important for ecologists and conservationists. The airborne light detection and ranging (LiDAR) system has made dramatic improvements to forest data collection and management especially on the forest inventory aspect. LiDAR can reliably calculate tree-level characteristics such as crown scale and tree height as well as derived measures such as breast height diameter (DBH). To do this, an exact tree extraction method is needed inside LiDAR data. Within LiDAR data, tree extraction often starts by locating the treetops via local maxima (LM). Wide-ranging efforts have been developed to extract individual trees from LiDAR data by starting to localize treetops through LM within LiDAR data. Throughout this research, a demonstration of a new tree extraction framework inside LiDAR Point Cloud by incorporating a new tree extraction method using the bounding-box coordinates provided by deep learning-based object detection. Tree extraction inside the LiDAR point cloud using the bounding-box coordinates was successful and feasible.

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The current system of checking and grading egg quality in the Philippines was done manually one by one using the traditional way where graders exert great effort that resulted in graders' visual stress. To address the problem identified the researchers proposed a scientific way of checking and grading the egg quality by using image processing based non-destructive and cost-effective technique to detect various cracks, dirt, and defect in eggs. Upon testing, the system obtained a total of 91.33% as high-quality eggs and the presence of either crack or dirt while 8.66% were inspected as low quality. For the internal part of each egg, the system achieved 100% detection of the yolk. The main results achieved have been quite promising; the researchers are encouraged to continue the labor of improving the generation of internal and external egg detection.

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