Tomato Flower Detection and Three-Dimensional Mapping for Precision Pollination

It is estimated that nearly 75% of major crops have some level of reliance on pollination. Humans are reliant on fruit and vegetable crops for many vital nutrients. With the intensification of agricultural production in response to human demand, native pollinator species are not able to provide sufficient pollination services, and managed bee colonies are in decline due to colony collapse disorder, among other issues. Previous work addresses a few of these issues by designing pollination systems for greenhouse operations or other controlled production systems but fails to address the larger need for development in other agricultural settings with less environmental control. In response to this crisis, this research aims to act as a vital first step towards the development of a more robust autonomous pollination system for agricultural crop production. The main objective of this research is to develop a flower detection and mapping system for a field crop setting. This research presents a method to detect and localize tomato flowers within a three-dimensional (3D) region. Tomato plants were grown in a raised-bed garden where images were collected of the overhead view of the plants. Images were then stitched together using a photogrammetry technique, accomplished by the Pix4Dmapper software, to form an orthomosaic and 3D representation of the raised-bed garden from a high spatial resolution aerial view. Various machine learning architectures were trained to detect tomato flowers from overhead images and were then tested on the orthomosaic images produced by the Pix4D software. The coordinates of the detected flowers in the orthomosaic were then compared to the 3D model representation to find approximate 3D coordinates for each of the flowers relative to a predefined origin. This research serves as a first step in autonomous pollination by presenting a way for machine vision and machine learning to be used to identify the presence and location of flowers on tomato crops. Future work will aim to expand flower detection to other crops varieties in varying field conditions

Towards Specifying And Evaluating The Trustworthiness Of An AI-Enabled System

Applied AI has shown promise in the data processing of key industries and government agencies to extract actionable information used to make important strategical decisions. One of the core features of AI-enabled systems is the trustworthiness of these systems which has an important implication for the robustness and full acceptance of these systems. In this paper, we explain what trustworthiness in AI-enabled systems means, and the key technical challenges of specifying, and verifying trustworthiness. Toward solving these technical challenges, we propose a method to specify and evaluate the trustworthiness of AI-based systems using quality-attribute scenarios and design tactics. Using our trustworthiness scenarios and design tactics, we can analyze the architectural design of AI-enabled systems to ensure that trustworthiness has been properly expressed and achieved.The contributions of the thesis include (i) the identification of the trustworthiness sub-attributes that affect the trustworthiness of AI systems (ii) the proposal of trustworthiness scenarios to specify trustworthiness in an AI system (iii) a design checklist to support the analysis of the trustworthiness of AI systems and (iv) the identification of design tactics that can be used to achieve trustworthiness in an AI system

Smart Manufacturing

This book is a collection of 11 articles that are published in the corresponding Machines Special Issue “Smart Manufacturing”. It represents the quality, breadth and depth of the most updated study in smart manufacturing (SM); in particular, digital technologies are deployed to enhance system smartness by (1) empowering physical resources in production, (2) utilizing virtual and dynamic assets over the Internet to expand system capabilities, (3) supporting data-driven decision-making activities at various domains and levels of businesses, or (4) reconfiguring systems to adapt to changes and uncertainties. System smartness can be evaluated by one or a combination of performance metrics such as degree of automation, cost-effectiveness, leanness, robustness, flexibility, adaptability, sustainability, and resilience. This book features, firstly, the concepts digital triad (DT-II) and Internet of digital triad things (IoDTT), proposed to deal with the complexity, dynamics, and scalability of complex systems simultaneously. This book also features a comprehensive survey of the applications of digital technologies in space instruments; a systematic literature search method is used to investigate the impact of product design and innovation on the development of space instruments. In addition, the survey provides important information and critical considerations for using cutting edge digital technologies in designing and manufacturing space instruments

Smart Farming Using Robots in IoT to Increase Agriculture Yields: A Systematic Literature Review

Robots are beneficial in everyday life, especially in helping food security in the agricultural industry. Smart farming alone is not enough because smart farming is only automated without mobile hardware. The existence of robots can minimize human involvement in agriculture so that humans can maximize activities outside of farms. This Study aims to review articles regarding robots in smart farming to increase agriclture yields. This article systematically uses the systematic literature review method utilizing the Preferred reporting items for systematic review and meta-analyses (PRISMA) by submitting 3 Research Questions (RQ). According to the authors of the 3 RQs, it is necessary to represent the function and purpose of robots in farms and to be used in the context of the importance of robots in agriculture because of the potential impact of increase agriculture yields. This Research contributes to finding and answering 3 RQ, which are the roots of the use of robots. The results taken, the authors get 116 articles that can be reviewed and answered RQ and achieve goals. RQ 1 was responded to with the article's country of origin, research criteria, and the year of the article. In RQ 2 the author answered that Research often carried out 6 schemes, then the most Research was (Challenge Robots, Ethics, and Opinions in Agriculture) and (Design, Planning, and Robotic Systems in Agriculture). Finally, in RQ 3, the author describes the research scheme based on understanding related Research. The author hopes this basic scheme can be a benchmark or a new direction for future researchers and related agricultural industries to improve agricultural quality

Route Planning for Long-Term Robotics Missions

Many future robotic applications such as the operation in large uncertain environment depend on a more autonomous robot. The robotics long term autonomy presents challenges on how to plan and schedule goal locations across multiple days of mission duration. This is an NP-hard problem that is infeasible to solve for an optimal solution due to the large number of vertices to visit. In some cases the robot hardware constraints also adds the requirement to return to a charging station multiple times in a long term mission. The uncertainties in the robot model and environment require the robot planner to account for them beforehand or to adapt and improve its plan during runtime. The problem to be solved in this work is how to plan multiple day routes for a robot where all predefined locations must be visited only a single time and at each route the robot must start and return to the same initial position while respecting the daily maximum operation time constraint. The proposed solution uses problem definitions from the delivery industry and compares various metaheuristic based techniques for planning and scheduling the multiple day routes for a robotic mission. Therefore the problem of planning multiple day routes for a robot is modeled as a time constrained Vehicle Routing Problem where the robot daily plan is limited by how long the robot with a full charge can operate. The costs are modeled as the time a robot takes to move among locations considering robot and environment characteristics. The solution for this method is obtained in a two step process where a greedy initial solution is generated and then a local search is performed using meta-heuristic based methods. A custom time window formulation with respect to the theoretical maximum daily route is presented to add human expert input, priorities or expiration time to the planned routes allowing the planner to be flexible to various robotic applications. This thesis also proposes an intermediary mission control layer, that connects the daily route plan to the robot navigation layer. The goal of the Mission Control is to monitor the robot operation, continuously improve its route and adapt to unexpected events by dropping waypoints according to some defined penalties. This is an iterative process where optimization is performed locally in real time as the robot traverse its goals and offline at the end of each day with the remaining vertices. The performance of the various meta-heuristic and how optimization improves over time are analysed in several robotic route planning and scheduling scenarios. Two robotic simulation environments were built to demonstrate practical application of these methods. An unmanned ground vehicle operated fully autonomously using the presented methods in a simulated underground stone mine environment where the goal is to inspect the pillars for structural failures and a farm environment where the goal is to pollinate flowers with an attached robotic arm. All the optimization methods tested presented significant improvement in the total route costs compared to the initial Path-Cheapest-Arc solution. However the Guided Local Search presented a smaller standard deviation among the methods in most situations. The time-windows allowed for a seamless integration with an expert human input and the mission control layer, forced the robot to operate within the mission constraints by dynamically choosing the routes and the necessity of dropping some of the vertices

Improving Robotic Decision-Making in Unmodeled Situations

Existing methods of autonomous robotic decision-making are often fragile when faced with inaccurate or incompletely modeled distributions of uncertainty, also known as ambiguity. While decision-making under ambiguity is a field of study that has been gaining interest, many existing methods tend to be computationally challenging, require many assumptions about the nature of the problem, and often require much prior knowledge. Therefore, they do not scale well to complex real-world problems where fulfilling all of these requirements is often impractical if not impossible. The research described in this dissertation investigates novel approaches to robotic decision-making strategies which are resilient to ambiguity that are not subject to as many of these requirements as most existing methods. The novel frameworks described in this research incorporate physical feedback, diversity, and swarm local interactions, three factors that are hypothesized to be key in creating resilience to ambiguity. These three factors are inspired by examples of robots which demonstrate resilience to ambiguity, ranging from simple vibrobots to decentralized robotic swarms. The proposed decision-making methods, based around a proposed framework known as Ambiguity Trial and Error (AT&E), are tested for both single robots and robotic swarms in several simulated robotic foraging case studies, and a real-world robotic foraging experiment. A novel method for transferring swarm resilience properties back to single agent decision-making is also explored. The results from the case studies show that the proposed methods demonstrate resilience to varying types of ambiguities, both stationary and non-stationary, while not requiring accurate modeling and assumptions, large amounts of prior training data, or computationally expensive decision-making policy solvers. Conclusions about these novel methods are then drawn from the simulation and experiment results and the future research directions leveraging the lessons learned from this research are discussed

Development of New Cotton Defoliation Sprayer Using Unmanned Ground Vehicle and Pulse Width Modulation Technology

Chemical spraying is one of the most important and frequently performed intercultural agriculture operations. It is imperative to utilize appropriate spraying technology as a selection of ineffective one leads to waste of agrochemicals to the non‐target area. Several precision technologies have been developed in the past few decades, such as image processing based on real‐time variable‐rate chemical spraying systems, autonomous chemical sprayers using machine vision and nozzle control, and use of unmanned aerial and ground vehicles. Cotton (Gossypium hirsutum L.) is an important industrial crop. It is a perennial crop with indeterminate growth habit; however, in most parts of the United States, it is grown as an annual crop and managed using growth regulators. Cotton defoliation is a natural physiological phenomenon, but untimely and/or inadequate defoliation by natural processes necessitates the application of chemical defoliants for efficient harvest. Defoliation is a major production practice influencing harvester efficiency, fiber trash content, cotton yield, and fiber quality. Currently, defoliant spraying is done by conventional ground driven boom sprayer or aerial applicator and both systems spray chemical vertically downwards into the canopy, which results in less chemical reaching the bottom of the canopy. Thus, a new autonomous ground sprayer was developed using robotics and pulse width modulation, which travels between two rows covering the whole canopy of the plant. Field research was conducted to evaluate the (i) effect of duty cycles (20%,40%, and 60%) on droplet characteristic (droplet distribution, deposition, and drift potential), defoliation cotton fiber and (ii) effect of duty cycles on cotton yield and II fiber quality. Droplet characteristics (droplet distribution, density, and potential droplet drift) were non-significant across the treatments and results from the water‐sensitive paper field test showed adequate penetration with low flow rates. Therefore, a 20% duty cycle was sufficient to defoliate based on the result of the field experiment. Likewise, the defoliants could be applied safely at the duty cycles tested without influencing fiber quality except for nep/gm, length (Ln), L (5%), short fiber content (SFCn), trash content in field 1 and micronaire, nep size, length (Ln), span length (5%), SFC, and fiber fineness in field 2 which were significant. However, the 20% duty cycle significantly reduced the amount of defoliant and would be a good choice for the autonomous cotton defoliation. This is a significant development as there is a huge potential to save on the cost of applying defoliant chemicals and the environment

PROGRAM and PROCEEDINGS THE NEBRASKA ACADEMY OF SCIENCES 1880-2017 Including the Nebraska Association of Teachers of Science (NATS) Division Nebraska Junior Academy of Sciences (NJAS) Affiliate and Affiliated Societies

FRIDAY, APRIL 21, 2017 7:30 a.m. REGISTRATION FOR ACADEMY, Lobby of Lecture wing, Olin Hall 8:00 Aeronautics and Space Science, Session A, Olin 249 Aeronautics and Space Science, Session B, Olin 224 Chemistry and Physics, Section A, Chemistry, Olin A Collegiate Academy, Biology, Session A, Olin B Collegiate Academy, Biology, Session B, Olin 112 Collegiate Academy, Chemistry and Physics, Session A, Olin 324 8:30 Biological and Medical Sciences, Session A, Smith Callen Conference Center 9:10 Aeronautics and Space Science, Poster Session, Olin 249 9:40 Applied Science and Technology, Olin 325 10:00 Chemistry and Physics, Physics, Section B, Planetarium 10:30 Aeronautics and Space Science, Poster Session, Olin 249 11:00 MAIBEN MEMORIAL LECTURE, OLIN B – Scholarship and Friend of Science Recipients also announced. 12:00 LUNCH, PATIO ROOM, STORY STUDENT CENTER Aeronautics Group, Sunflower Room 1:00 p.m. Anthropology, Olin 111 Biological and Medical Sciences, Session B, Smith Callen Conference Center Collegiate Academy, Biology, Session A, Olin B Collegiate Academy, Biology, Session B, Olin 112 Collegiate Academy, Chemistry and Physics, Session B, Olin 324 Earth Science, Olin 249 1:05 Applied Science and Technology, Olin 325 1:15 Teaching of Science and Math, Olin 224 Chemistry and Physics, Section A, Chemistry, Olin A 2:45 Environmental Sciences, Olin 249 4:30 BUSINESS MEETING, OLIN B Abstracts of papers 2016-2017 EXECUTIVE COMMITTEE 2016-2017 PROGRAM COMMITTEE 2016-2017 POLICY COMMITTEE FRIENDS OF THE ACADEMY FRIEND OF SCIENCE AWARD WINNERS FRIEND OF SCIENCE AWARD TO KACIE BAUM FRIEND OF SCIENCE AWARD TO TODD YOUNG Author Index 141 p

Frontiers in Ultra-Precision Machining

Ultra-precision machining is a multi-disciplinary research area that is an important branch of manufacturing technology. It targets achieving ultra-precision form or surface roughness accuracy, forming the backbone and support of today’s innovative technology industries in aerospace, semiconductors, optics, telecommunications, energy, etc. The increasing demand for components with ultra-precision accuracy has stimulated the development of ultra-precision machining technology in recent decades. Accordingly, this Special Issue includes reviews and regular research papers on the frontiers of ultra-precision machining and will serve as a platform for the communication of the latest development and innovations of ultra-precision machining technologies