A method for automatic segmentation and splitting of hyperspectral images of raspberry plants collected in field conditions

Abstract Hyperspectral imaging is a technology that can be used to monitor plant responses to stress. Hyperspectral images have a full spectrum for each pixel in the image, 400–2500 nm in this case, giving detailed information about the spectral reflectance of the plant. Although this technology has been used in laboratory-based controlled lighting conditions for early detection of plant disease, the transfer of such technology to imaging plants in field conditions presents a number of challenges. These include problems caused by varying light levels and difficulties of separating the target plant from its background. Here we present an automated method that has been developed to segment raspberry plants from the background using a selected spectral ratio combined with edge detection. Graph theory was used to minimise a cost function to detect the continuous boundary between uninteresting plants and the area of interest. The method includes automatic detection of a known reflectance tile which was kept constantly within the field of view for all image scans. A method to split images containing rows of multiple raspberry plants into individual plants was also developed. Validation was carried out by comparison of plant height and density measurements with manually scored values. A reasonable correlation was found between these manual scores and measurements taken from the images (r2 = 0.75 for plant height). These preliminary steps are an essential requirement before detailed spectral analysis of the plants can be achieved

Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress

This review explores how imaging techniques are being developed with a focus on deployment for crop monitoring methods. Imaging applications are discussed in relation to both field and glasshouse-based plants, and techniques are sectioned into ‘healthy and diseased plant classification’ with an emphasis on classification accuracy, early detection of stress, and disease severity. A central focus of the review is the use of hyperspectral imaging and how this is being utilised to find additional information about plant health, and the ability to predict onset of disease. A summary of techniques used to detect biotic and abiotic stress in plants is presented, including the level of accuracy associated with each method

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject

Power Play - NPS' defense energy program educates tomorrow's energy-intelligent officers

The Navy’s fleet has been powered by a broad spectrum of sources over the past two centuries, from wind and coal, to diesel and nuclear. The service has set a determined course for energy independence, a true cultural change in how the Navy, Marine Corps view energy, but to achieve that change, they will need a new breed of energy-intelligent officer

NPS Operations Research Professor Wins Two Prestigious INFORMS Awards

Faculty Showcase Archive ArticleNaval Postgraduate School (NPS) Distinguished Professor of Operations Research (OR) Dr. Gerald G. Brown accomplished something no other OR practitioner ever has ... He was awarded both the 2016 INFORMS President’s Award, and the 2016 INFORMS Steinhardt Prize, during the organization’s annual meeting, November 15. Both awards recognize Brown’s long career of honored and impactful contributions to the practice of operations research and beyond.Approved for public release; distribution is unlimited

Alumnus Rear Adm. Jan Tighe takes the helm of Naval Postgraduate School

Rear Adm. Jan Tighe addressed Naval Postgraduate faculty and staff for the first time during a gathering in King Auditorium, Nov. 28, following the annnouncement of the university's change in senior leadership by Secretary of the Navy Ray Mabus. Tighe was introduced to the istitution's community by Under Secretary of the Navy Robert O. Work

NPS Adds Another Astronaut Alumnus With NASA’s Newest Class

Article taken from the NPS website: http://www.nps.edu/About/News/NPS-Adds-Another-Astronaut-Alumnus-With-NASAs-Newest-Class.htmlWhen NASA Administrator Charles Bolden announced the latest class of NASA’s eight astronaut candidates, June 17, the Naval Postgraduate School (NPS) was able to add yet another space-traveling alumnus to its ranks, now totaling 41 and counting. Lt. Cmdr. Victor Glover, an F/A-18 combat pilot currently serving as a Legislative Fellow in the office of Senator John McCain, was selected from more than 6,100 applicants to begin training at Johnson Space Center in August for potential space flight. Glover graduated from the Naval Postgraduate School in 2009 through the Master’s of Systems Engineering Management – Product Development 21st Century (SEM-PD21) program, in addition to receiving a space systems academic certificate in 2005, both via distance learning