This dissertation analyzed consumer primary motivations for attending farmers' markets, preferences for product features, and differentiated produce. We used consumer survey data collected at farmers' markets in Nevada and Utah during summers of 2008 and 2011, respectively. This dissertation consists of three essays. The first essay employed binary and multinomial logistic models to assess primary consumer motivations for attending farmers' markets. Results indicate that many consumers attend farmers' markets primarily to purchase fresh produce. Other motives such as social interaction, purchasing ready-to-eat food, and buying packaged foods, arts, and crafts were also analyzed. In this first essay, consumers who attended farmers' markets were clustered into three groups based on their similar characteristics. Results from this essay are useful to vendors at farmers' markets for they indicate primary motivations to attend. It also provides guidelines to farmers' markets managers in their efforts to meet attendees' expectations. The second essay used an ordered logistic model to analyze consumer preferences for eight fresh produce features. These features are product variety, quality, appearance, pricing, local, organic, freshness, and knowledge of local growers. Findings show that consumer preferences are strong for product quality, freshness, local and organic production. Policy makers can use results from this essay to provide necessary assistance to farmers to feature their products based on consumers' preferences. Health-related policy makers can use the results to implement programs aimed at increasing fresh produce consumption. The last essay used a multinomial logistic, conditional and ordinary least squares models to respectively investigate consumer preferences for differentiated fresh produce, willingness to pay, and stated demands for green peppers, cucumbers, and yellow squash. Comparison between preferences before and those after information about production and place of production was also done. Results demonstrate that consumer willingness to pay and the probability of purchasing each of the three products grown conventionally in Utah overweight those for either organically or conventionally grown of unknown origin. This essay provides information pertaining to produce differentiation through labels. The information has significant impact on preferences for conventionally grown local produce and negative effect on conventionally grown fresh produce of unknown origin. Green peppers, cucumbers, and yellow squash are ordinary goods with inelastic stated demands. Produce growers can use results from this essay to adopt production practices to meet consumer preferences. Results are useful to policy makers in enforcing local and organic certification regulations. They can also be used for pricing and marketing strategies.

Die Wandelbarkeit von Anlagen im Kontext der Industrie 4.0 führt dazu, dass zukünftig Fertigungsmodule unterschiedlicher Hersteller interagieren müssen – es entsteht eine heterogene Modullandschaft. Das erfordert Lösungen für die automatisierungstechnische Einbindung und den flexiblen Austausch von weiteren, herstellerfremden und bislang unbekannten Modulen. Es werden Lösungskonzepte zur Umsetzung eines strukturierten und herstellerunabhängigen Engineering-Konzepts für Plug & Produce in einer heterogenen Landschaft von Fertigungsmodulen benötigt. Im Rahmen dieser Arbeit stellen die Autoren eine Architektur vor, wie dieses Ziel erreicht werden kann. Der Fokus liegt dabei auf der Steuerungsarchitektur, der Informationsmodellierung und der Virtuellen Inbetriebnahme als Grundlage für das gemischt real-virtuelle Engineering. Das vorgestellte Konzept basiert auf einer Anforderungsanalyse und ist prototypisch umgesetzt.

In developed countries, approximately 222 million tons of food is wasted at the consumer level per year (FAO, 2011). These amounts of food waste have large social, economic, and environmental impacts. Studies have shown that one of the main causes of food waste in developed countries is consumers’ elevated expectations for appearances in fresh produce, causing imperfect produce to be wasted. In this study, we estimate consumer willingness to pay for sweet potatoes with five different skinning injury levels using a Vickrey 2nd price non- hypothetical auction. We test if consumer knowledge about (1) the percentage of blemishing, (2) the relationship between blemished produce and food waste, and (3) the environmental impacts of food waste influences willingness-to-pay for blemished produce. We find that consumer bids were affected by knowing the blemishing levels and after gaining knowledge about food waste and its environmental impacts.

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Produce is responsible for 46% of all foodborne illnesses in the USA. Salmonella enterica causes 19,000 hospitalizations each year, and has been associated with produce. Presently, chlorine based sanitizers are most often used, however organic matter reduces its antimicrobial activity. Bacteriophage treatments are an all-natural, alternative method for pathogen inactivation. The objective of this study was to determine the efficacy of a five-strain bacteriophage treatment against a S. enterica cocktail in simulated wash waters at different temperatures. Bacteriophage and S. enterica were enumerated in simulated wash water solutions. One set of experiments studied bacteriophage and S. enterica growth in TSB+vegetable solutions. Bacteriophage behavior was not statistically different (p < 0.05) in spinach, romaine, or iceberg lettuce across different concentrations of organic matter. S. enterica reduction was approximately 2 log over 135 minutes for vegetable solutions and for the TSB control. S. enterica reduction was only 0.5 log in water solutions. The next set of experiments studied bacteriophage and S. enterica growth in vegetable solutions. Spinach wash water and tryptone soy broth solutions (TSB) at 20 °C and 37 °C. S. enterica was not reduced in spinach solution studies at 20 °C and 37 °C or at broth solutions at 20 °C. However, S. enterica was effectively reduced 4 log in broth solutions at 37 °C up to 7.5 hours, but grew to high levels after 24 hours. These results indicate that bacteriophage could not effectively control bacteria levels in produce wash water, and may need to be optimized.

The Food Safety Modernization Act (FSMA), specifically the Produce Safety Rule (PSR), requires growers to clean and sanitize food-contact surfaces to protect against produce contamination. The ATP monitoring device is a potential sanitation tool to monitor the efficacy of an on-farm cleaning and sanitation program that could help growers meet regulatory expectations mandated by PSR. The ATP device uses bioluminescence to detect all ATP (found in bacteria and produce matter cells) from a swabbed surface. Little work has been done to test the efficacy of these tools under postharvest conditions. The present study evaluated ATP measurement for postharvest surface cleanliness evaluation. Concentrations of leafy greens (spinach, romaine, red cabbage) (with/without L. innocua) were used as organic matter inocula onto stainless steel, HDPE plastic, and bamboo wood coupons to represent postharvest surfaces. The ATP levels on the coupons were measured using swabs and ATP monitoring device. Results showed that the concentration of L. innocua and leafy greens on a surface had a highly significant effect on the ATP device reading (PL. innocua at 4.5 log CFU/coupon where the ATP device could no longer detect ATP from L. innocua. The type of leafy green on a food-contact surface did not affect the ATP reading (P=0.88). Leafy greens with L. innocua had a higher ATP reading when compared to saline and L. innocua, demonstrating the presence of leafy green matter contributes to ATP reading when combined with L. innocua. The different food-contact surfaces had different ATP readings (P=0.03) and the ATP device did not detect bacterial or leafy green ATP from bamboo wood surfaces (P=0.16). Based upon our results, ATP measurement is an appropriate tool to measure produce or bacterial contamination on stainless steel or HDPE plastic surfaces, however it is not recommended for wood surfaces.

The fresh-cut produce industry has seen expansive growth in recent years, to meet consumer demand ready-to-eat (RTE) salads have included the use of non-traditional ingredients. Uncommon ingredients include beet greens, kale, broccoli stalk, and Brussels sprouts, since these ingredients have not historically been consumed raw, potential food safety issues should be reassessed. Current processing technologies include produce washes that can reduce microbial levels but do not eradicate all populations. The lack of a kill step in produce processing emphasizes the need to minimize pathogen contamination during production and growth during a product’s shelf life. Listeria monocytogenes, a leading cause of foodborne illness related deaths, continues to challenge the industry with recent outbreaks and recalls of fresh-cut produce. These events present the need to better understand L. monocytogenes growth potential in RTE produce during storage and distribution. Traditional salad greens have been researched extensively, however, limited knowledge is available on new inclusions. While temperature is known to strongly influence microbial growth, the effects of physical abuse during storage and distribution are unknown. The purpose of this study was to characterize L. innocua’s, a surrogate for L. monocytogenes, growth behavior in processed beet greens, kale, broccoli stalk, and Brussels sprouts when exposed to simulated physical and thermal abuses during storage and distribution. To evaluate L. innocua growth during storage and distribution produce samples were obtained from a local processor in retail packaging and surface inoculated. The samples were conditioned at 4℃ for 18h prior to being exposed to a series of physical abuses (compression, drop, and vibration) typical of storage and distribution. After abuse, produce was incubated at 4 or 8°C and sampled post-abuse through 16 and 11 days, respectively. Samples were enumerated for L. innocua, aerobic and psychrotrophic microorganisms, and lactic acid bacteria. To monitor growing conditions in each vegetable, product pH, water activity, and headspace (gas analysis), were observed at each time pull. The study found physical abuse had no significant effect on L. innocua, or microbiota growth regardless of vegetable or incubation temperature (P > 0.05). Vegetable intrinsic factors (pH, Aw, and headspace) did not seem to interfere in L. innocua or background microbiota growth during incubation. All vegetables supported L. innocua growth under 8℃. Growth of L. innocua was greatest in beet greens, followed by kale, broccoli stalk, and Brussels sprouts in descending order. Significant growth of L. innocua at 4 and 8ᵒC was seen on day 6 and 4 in beet greens, 11 and 6 in Brussels sprouts, 16 and 4 in kale, and 16 and 6 in broccoli stalk (P < 0.05). Overall, these results show the studied RTE vegetables can support L. monocytogenes growth during storage and distribution, especially under abusive temperatures, demonstrating the importance of prevention strategies during processing and refrigeration throughout RTE produce shelf life.

Computer vision is becoming a ubiquitous technology in many industries on account of its speed, accuracy, and long-term cost efficacy. The ability of a computer vision system to quickly and efficiently make quality decisions has made computer vision a popular technology on inspection lines. However, few companies in the agriculture industry use computer vision because of the non-uniformity of sellable produce. The small number of agriculture companies that do utilize computer vision use it to extract features for size sorting or for a binary grading system: if the piece of fruit has a certain color, certain shape, and certain size, then it passes and is sold. If any of the above criteria are not met, then the fruit is discarded. This is a highly wasteful and relatively subjective process. This thesis proposes a process to undergo to use computer vision techniques to extract features of fruit and build a model to predict shelf-life based on the extracted features. Fundamentally, the existing agricultural processes that do use computer vision base their distribution decisions on current produce characteristics. The process proposed in this thesis uses current characteristics to predict future characteristics, which leads to more informed distribution decisions. By modeling future characteristics, the process proposed will allow fruit characterized as “unfit to sell” by existing standards to still be utilized (i.e. if the fruit is too ripe to ship across the country, it can still be sold locally) which decreases food waste and increases profit. The process described also removes the subjectivity present in current fruit grading systems. Further, better informed distribution decisions will save money in storage costs and excess inventory. The proposed process consists of discrete steps to follow. The first step is to choose a fruit of interest to model. Then, the first of two experiments is performed. Sugar content of a large sample of fruit are destructively measured (using a refractometer) to correlate sugar content to a color range. This step is necessary to determine the end-point of data collection because stages of ripeness are fundamentally subjective. The literature is consulted to determine “ripe” sugar content of the fruit and the first experiment is undertaken to correlate a color range that corresponds to the “ripe” sugar content. This feature range serves as the end-point of the second experiment. The second experiment is large-scale data collection of the fruit of interest, with features being recorded every day, until the fruit reaches end-of-life as determined by the first experiment. Then, computer vision is used to perform feature extraction and features are recorded over each sample fruit’s lifetime. The recorded data is then analyzed with regression and other techniques to build a model of the fruit’s shelf-life. The model is finally validated. This thesis uses bananas as a proof of concept of the proposed process.

This thesis introduces a Human-Machine Interface (HMI) developed to enhance safety and efficiency in Configurable Multiagent Systems (CMAS) operating in Plug-and-Produce robot cells. The HMI addresses challenges related to flexible CMAS configurations, specifically addressing collision detection difficulties. Through runtime Configuration and coding of CMAS, the HMI identifies safer robot paths to prevent collisions during real-world CMAS operations. The experimental phase involves a virtual environment, demonstrating the HMI's effectiveness in collision prevention during CMAS operations. This research represents a notable advancement in collision-free motion planning for flexible CMAS configurations, offering a valuable tool for operators to operate CMAS in dynamic production settings, fostering safer and more efficient robotic automation across industries

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