Northwest Nazarene University

The Fruit Harvest Helper, a mobile application developed by Northwest Nazarene University’s (NNU) Robotics Vision Lab, aims to assist farmers in estimating fruit yield for apple orchards. Currently, farmers manually estimate the fruit yield for an orchard, which is a laborious task. The Fruit Harvest Helper seeks to simplify their process by detecting apples on images of apple trees. Once the number of apples is detected, a correlation can then be applied to this value to obtain a usable yield estimate for an apple tree. While prior research efforts at NNU concentrated on developing an iOS app for blossom detection, this current research aims to adapt that smart farming application for apple detection across multiple platforms, iOS and Android. Borrowing ideas from the former iOS app, the new application was designed with an intuitive user interface that is easy for farmers to use, allowing for quick image selection and processing. Unlike before, the adapted app uses a color ratio-based image-segmentation algorithm written in C++ to detect apples. This algorithm detects apples within apple tree images that farmers select for processing by using OpenCV functions and C++ code. The results of testing the algorithm on a dataset of images indicate an 8.52% Mean Absolute Percentage Error (MAPE) and a Pearson correlation coefficient of 0.6 between detected and actual apples on the trees. These findings were obtained by evaluating the images from both the east and west sides of the trees, which was the best method to reduce the error of this algorithm. The algorithm’s processing time was tested for Android and iOS, yielding an average performance of 1.16 s on Android and 0.14 s on iOS. Although the Fruit Harvest Helper shows promise, there are many opportunities for improvement. These opportunities include exploring alternative machine-learning approaches for apple detection, conducting real-world testing without any human assistance, and expanding the app to detect various types of fruit. The Fruit Harvest Helper mobile application is among the many mobile applications contributing to precision agriculture. The app is nearing readiness for farmers to use for the purpose of yield monitoring and farm management within Pink Lady apple orchards.

The Robotics Vision Lab of Northwest Nazarene University has developed the Orchard Robot (OrBot), which was designed for harvesting fruits. OrBot is composed of a machine vision system to locate fruits on the tree, a robotic manipulator to approach the target fruit, and a gripper to remove the target fruit. Field trials conducted at commercial orchards for apples and peaches during the harvesting season of 2021 yielded a harvesting success rate of about 85% and had an average harvesting cycle time of 12 s. Building upon this success, the goal of this study is to evaluate the performance of OrBot during nighttime harvesting. The idea is to have OrBot harvest at night, and then human pickers continue the harvesting operation during the day. This human and robot collaboration will leverage the labor shortage issue with a relatively slower robot working at night. The specific objectives are to determine the artificial lighting parameters suitable for nighttime harvesting and to evaluate the harvesting viability of OrBot during the night. LED lighting was selected as the source for artificial illumination with a color temperature of 5600 K and 10% intensity. This combination resulted in images with the lowest noise. OrBot was tested in a commercial orchard using twenty Pink Lady apple trees. Results showed an increased success rate during the night, with OrBot gaining 94% compared to 88% during the daytime operations.

Increased spatial resolution has been shown to be an important factor in enabling machine learning to map burn extent and severity with extremely high accuracy. Unfortunately, the acquisition of drone imagery is a labor-intensive endeavor, making the capture of drone imagery impractical for large catastrophic fires, which account for the majority of the area burned each year in the western US. To overcome this difficulty, satellites, such as PlanetScope, are now available which can produce imagery with remarkably high spatial resolution (approximately three meters). In addition to having higher spatial resolution, PlanetScope imagery contains up to eight bands in the visible and near-infrared spectra. This study examines the efficacy of each of the eight bands observed in PlanetScope imagery using a variety of feature selection methods, then uses these bands to map the burn extent and biomass consumption of three wildland fires. Several classifications are produced and compared based on the available bands, resulting in highly accurate maps with slight improvements as additional bands are utilized. The near-infrared band proved contribute most to increased mapping accuracy, while the green 1 and yellow bands contributed the least.

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ABSTRACT Forgiveness is a foundational tenet across all religions that has consistently been shown to be related to positive health outcomes. Although specific theoretical dimensions of “forgiveness” have been proposed (e.g., decisional, emotional, dispositional), there is limited evidence to suggest that they are statistically distinct constructs. However, a recent factor analysis (Yoon et al., 2022) indicated that a frequently used measure of forgiveness (i.e., Trait Forgiveness Scale) is composed of statistically distinct behavioral (i.e., willingness to forgive) and affective dimensions (i.e., resentment). Given this finding, the current study determined how these empirically distinct constructs interact to influence health. Based on 322 primarily Christian undergraduate students the results indicated: 1) willingness to forgive and resentment are significantly negatively correlated but distinct constructs; 2) religiosity is related to forgiveness but not resentment; 3) forgiveness, resentment, and their interaction are all significantly related to mental (but not physical) health; and 4) resentment partially negates the positive effects of willingness to forgive on mental health. Overall, the results suggest the need to differentiate behavioral and affective dimensions of forgiveness, that religious beliefs may promote forgiveness but not reduce resentment, and that both willingness to forgive and reducing resentment are important in promoting positive health.

To mitigate a malware threat it is important to understand the malware’s behavior. The MITRE ATT&ACK ontology specifies an enumeration of tactics, techniques, and procedures (TTP) that characterize malware. However, absent are automated procedures that would characterize, given the malware executable, which part of the execution flow is connected with a specific TTP. This paper provides an automation methodology to locate TTP in a sub-part of the control flow graph that describes the execution flow of a malware executable. This methodology merges graph representation learning and tools for machine learning explanation.

The 21st century business environment is full of dangers and risk. At the same time, it is ripe with opportunities and rewards. As organizations continue to pursue opportunities and rewards with vigor, while minimizing unnecessary exposure to risk, leaders find themselves with one of two perspectives on risk: risk-seeking or risk averse. Leaders who are risk-seeking are not moved by the inherent dangers of risk, and understand risk to be a necessary part of the business landscape – and therefore engage risk head-on. Alternatively, leaders who are risk averse understand the implications of risk on the enterprise, and work to minimize, or even avoid risk altogether, or at least to the furthest extent possible. This paper examines risk from a leadership perspective. There are various types of risk that business leaders face, and those will be identified and described herein, along with a discussion of various approaches for addressing risk and how risk is manifested in the business environment. Finally, this paper will assist leaders in determining their own attitudes toward risk using various self-assessment tools.

Wildfires burn 4–10 million acres annually across the United States and wildland fire related damages and suppression costs have exceeded $13 billion for a single year. High-intensity wildfires contribute to post-fire erosion, degraded wildlife habitat, and loss of timber resources. Accurate and temporally adequate assessment of the effects of wildland fire on the environment is critical to improving the of wildland fire as a tool for restoring ecosystem resilience. Sensor miniaturization and small unmanned aircraft systems (sUAS) provide affordable, on-demand monitoring of wildland fire effects at a much finer spatial resolution than is possible with satellite imagery. The use of sUAS would allow researchers to obtain data with more detail at a much lower initial cost. Unfortunately, current regulatory and technical constraints prohibit the acquisition of imagery using sUAS for the entire extent of large fires. This research examined the use of sUAS imagery to train and validate burn severity and extent mapping of large wildland fires from various satellite images. Despite the lower resolution of the satellite image, the research utilized the advantages of satellite imagery such as global coverage, low cost, temporal stability, and spectral extent while leveraging the higher resolution of hyperspatial sUAS imagery for training and validating the mapping analytics.

In As a City on a Hill, distinguished historian Daniel Rodgers dismantles one of Americans’ favorite origin stories. Found in virtually every history textbook, that story recounts how the English Puritan John Winthrop penned and preached a sermon entitled “A Model of Christian Charity” while traversing the Atlantic aboard the Arbella in 1630. Since the mid-twentieth-century, scholars have interpreted the text’s covenantal themes, providential character, and especially its “city upon a hill” phrase as casting a nationalist vision. The confidence, exceptionalism, and sense of purpose that drive American civic culture are said to have originated in Winthrop’s Model. All that America would become was held in embryo in that single text. Yet, as Rodgers contends in this deeply researched and persuasively argued monograph, this narrative is “at least half wrong” (p. 1). But “half wrong” is an understatement. Rodgers disputes even the most basic details. The Model was likely...