NNU Engineering Student Mission Projects from Peru to PNG to Kenya

A substantial impact is being made around the world by Christian university student engineering teams collaborating with Christian mission organizations [1-3]. The application of engineering talents to meeting basic human physical and spiritual needs is perhaps the “highest calling” of engineers. These engineering projects meet basic human needs such as clean water, sanitation, agriculture, power generation, education, healthcare, housing, and disaster recovery. These efforts are complementary to secular humanitarian work done by organizations like Engineers Without Borders, EPICS, IEEE Humanitarian Technologies Board/SIGHT, and the Bill and Melinda Gates Foundation. Lifelong impact is made not only in the lives of the assisted community, but also in the lives of the participating engineering students and faculty. These impacts will be illustrated in this paper by three stories of student teams from NNU Engineering who have teamed with villagers, non-profits, companies, church mission agencies, and even the IEEE to design, fund, construct, train, and follow-up on engineering projects around the world

Fruit detection system and an end effector for robotic harvesting of Fuji apples

 The challenges in developing a fruit harvesting robot are recognizing the fruit in the foliage and detaching the fruit from the tree without damaging either the fruit or the tree.  The objectives of this study were to develop a real-time fruit detection system using machine vision and a laser ranging sensor and to develop an end effector capable of detaching the fruit in a way similar to manual pick.  The Fuji apple variety was used in this study. In the detection of the fruit, machine vision was combined with a laser ranging sensor.  The machine vision recognized the fruit and the laser ranging sensor determined the distance.  The system detected a single fruit with 100% accuracy in both front and back lighted scenes with ±3 mm accuracy in distance measurement.  To detach the fruit from the tree, an end effector was developed with a peduncle holder and a wrist; the peduncle holder pinches the peduncle of the fruit and the wrist rotates the peduncle holder to detach the fruit.  Field test results of the end effector showed more than 90% success rate in detaching the fruit with average time use of 7.1 seconds.Keywords: apple, end effector, image processing, machine vision, robotic harvesting, Japan Citation: Bulanon D. M., and T. Kataoka.  Fruit detection system and an end effector for robotic harvesting of Fuji apples.  Agric Eng Int: CIGR Journal, 2010, 12(1): 203-210.&nbsp

Developing a Dual Vision Harvesting Bot Using ROS

With increasing demand for labor in the orchard harvesting field and limited supply to meet that demand, creating a tool for farmers to meet this need is important. Northwest Nazarene University is developing a robot to help farmers by automating harvesting. This robot uses a dual vision system that allows the robot to detect apples on a tree and pick them using the attached arm. This system utilizes the Robotic Operating System, ROS, to allow each piece of the system to communicate to create a seamless package

A Machine Vision Algorithm Combining Adaptive Segmentation and Shape Analysis for Orange Fruit Detection

 Over the last several years there has been a renewed interest in the automation of harvesting of fruits and vegetables. The two major challenges in the automation of harvesting are the recognition of the fruit and its detachment from the tree. This paper deals with fruit recognition and it presents the development of a machine vision algorithm for the recognition of orange fruits. The algorithm consists of segmentation, region labeling, size filtering, perimeter extraction and perimeter-based detection. In the segmentation of the fruit, the orange was enhanced by using the red chromaticity coefficient which enabled adaptive segmentation under variable outdoor illumination. The algorithm also included detection of fruits which are in clusters by using shape analysis techniques. Evaluation of the algorithm included images taken inside the canopy (varying lighting condition) and on the canopy surface. Results showed that more than 90% of the fruits visually recognized in the images were detected in the 110 images tested with a false detection rate of 4%. The proposed segmentation was able to deal with varying lighting condition and the perimeter-based detection method proved to be effective in detecting fruits in clusters. The development of this algorithm with its capability of detecting fruits in varying lighting condition and occlusion would enhance the overall performance of robotic fruit harvesting

Citrus black spot detection using hyperspectral image analysis

A recently discovered fungal disease called citrus black spot, is threatening the Florida citrus industry.  The fungal disease, which causes cosmetic lesions on the rind of the fruit and can cause a tree to drop its fruit prematurely, could possibly lead to a ban on sales of fresh Florida citrus in other citrus-producing states.  The objective of this research is to develop a multispectral imaging algorithm to detect citrus black spots based on hyperspectral image data.  Hyperspectral images of citrus fruits (Valencias) were collected in the wavelength range of 480 nm to 950 nm.  Five surface conditions were examined, citrus black spot, greasy spot, melanose, wind scar, and normal one.  The first part of the image analysis determined the optimal wavelengths using correlation analysis based on the wavelength ratio (l1/l2) and wavelength difference (l1 - l2).  Four wavelengths were identified, 493 nm, 629 nm, 713 nm, and 781 nm.  In the second part, pattern recognition approaches namely linear discriminant classifier and artificial neural networks were developed using the four selected wavelengths as the input.  Both pattern recognition approaches had an overall accuracy of 92%.  The detection accuracy was improved to 96% by using the NDVI band ratio method of 713 nm and 781 nm.  The multispectral image algorithm developed in this study haspotential to be adopted by a real-time multispectral imaging system for citrus black spot detection.     Keywords: activation energy, effective diffusivity, foam-mat drying, foam characteristics, modeling, Shrim

Measuring the dynamic photosynthome

Background: Photosynthesis underpins plant productivity and yet is notoriously sensitive to small changes inenvironmental conditions, meaning that quantitation in nature across different time scales is not straightforward. The ‘dynamic’ changes in photosynthesis (i.e. the kinetics of the various reactions of photosynthesis in response to environmental shifts) are now known to be important in driving crop yield. Scope: It is known that photosynthesis does not respond in a timely manner, and even a small temporal “mismatch” between a change in the environment and the appropriate response of photosynthesis toward optimality can result in a fall in productivity. Yet the most commonly measured parameters are still made at steady state or a temporary steady state (including those for crop breeding purposes), meaning that new photosynthetic traits remain undiscovered. Conclusions: There is a great need to understand photosynthesis dynamics from a mechanistic and biological viewpoint especially when applied to the field of ‘phenomics’ which typically uses large genetically diverse populations of plants. Despite huge advances in measurement technology in recent years, it is still unclear whether we possess the capability of capturing and describing the physiologically relevant dynamic features of field photosynthesis in sufficient detail. Such traits are highly complex, hence we dub this the ‘photosynthome’. This review sets out the state of play and describes some approaches that could be made to address this challenge with reference to the relevant biological processes involved

Satisfaction of Graduating Students during Exit Interviews in University of Cebu, Philippines

The University Guidance Center conducts exit interviews of graduating students as means of assessment for the University's services, where the graduating students have one final self-assessment before they leave the portals of the university which is considered their home for almost four years. The study aimed to determine the overall student feedback on their learning experiences, importance, and relevance of the skills gained and student satisfaction rating of Student Personnel Services for the school year 2013-2014 and to identify the areas of strengths, and recommend measures for improvement. The study employed descriptive survey method using researcher made questionnaires. The respondents were the 713 graduating students from the different colleges of the University of Cebu Main Campus, Philippines, first semester of the school year 2013-2014. They were advised to answer the four-part questionnaire. The gathered data were treated using frequency, simple percentage, and rank. Findings revealed that majority of the respondents enroll in the course for their parents or relatives encouraged them; the discussion questions contribute to the professional development of the interviewees that affect an in-depth knowledge in their chosen profession. The study concluded that the graduating students are pleased with the services of the Student Personnel Department. However, some suggestions and comments need to be addressed for the improvement of the University in general

Proof-of-concept robot platform for exploring automated harvesting of sugar snap peas

Currently, sugar snap peas are harvested manually. In high-cost countries like Norway, such a labour-intensive practise implies particularly large costs for the farmer. Hence, automated alternatives are highly sought after. This project explored a concept for robotic autonomous identification and tracking of sugar snap pea pods. The approach was based on a combination of visible–near infrared reflection measurements and image analysis, along with visual servoing. A proof-of-concept harvesting platform was implemented by mounting a robotic arm with hand-mounted sensors on a mobile unit. The platform was tested under plastic greenhouse conditions on potted plants of the sugar snap pea variety Cascadia using LED-lights and a partial shade. The results showed that it was feasible to differentiate the pods from the surrounding foliage using the light reflection at the spectral range around 970 nm combined with elementary image segmentation and shape modelling methods. The proof-of-concept harvesting platform was tested on 48 representative agricultural environments comprising dense canopy, varying pod sizes, partial occlusions and different working distances. A set of 104 images were analysed during the teleoperation experiment. The true positive detection rate was 93 and 87% for images acquired at long distances and at close distances, respectively. The robot arm achieved a success rate of 54% for autonomous visual servoing to a pre-grasp pose around targeted pods on 22 untouched scenarios. This study shows the potential of developing a prototype robot for semi-automated sugar snap pea harvesting

A Machine Vision System for the Apple Harvesting Robot

Rosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is a Technical article from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 3 (2001): D.M. Bulanon, T. Kataoka, Y. Ota, and T. Hiroma. A Machine Vision System for the Apple Harvesting Robot. Vol. III, December 2001