Without an increase in cropland, agricultural efficiency must be tripled in the next 50 years to sustain the increased demand for food. Controlled environment agriculture (CEA) systems are likely to play an important role in the increase of agricultural efficiency. CEA systems, however, require constant observation because decisions must be quickly made when plants show signs of stress. A visual inspection system that uses a robotic camera system would permit visual access to inaccessible plants in a large hydroponics operation or allows an observer to remotely inspect plants for multiple small or remote CEA operations, whereas a dedicated CEA specialist would be beneficial but impractical under present conditions. This thesis presents a theoretical design for a plant inspection robot. The design parameters, design process, and the system specification necessary to satisfy the design constraints were examined for this system. The design analysis revealed that the major components of the plant inspection robot must be designed sequentially, starting with the imaging system. The imaging system design revealed that the system parameters were governed by illumination, shape and size of the object, and the desired detail. The motion system design was governed by velocity, acceleration, work area, and accuracy. An example design for a system used for visual inspection of 289 romaine lettuce plants was presented. This design was shown to be feasible from the theoretical perspective and could be built from commercially-available components, reducing development time and cost.

Controlled environment agriculture allows the production of fresh food indoors from global locations and contexts where it would not otherwise be possible. Growers in extreme climates and urban areas produce food locally indoors, saving thousands of food import miles and capitalizing upon the demand for fresh, tasty, and nutritious food. However, the growing of food, both indoors and outdoors, consumes huge quantities of water - as much as 70-80% of global fresh water supplies. The utilization of liquid desiccants in a closed indoor agriculture cycle provides the possibility of capturing plant-transpired water vapor. The regeneration/desalination of these liquid desiccants offers the potential to recover fresh water for irrigation and also to re-concentrate the desiccants for continued dehumidification. Through the utilization of solar thermal energy, the process can be completed with a very small to zero grid-energy footprint. The primary research in this dissertation focused on two areas: the dehumidification of indoor environments utilizing liquid desiccants inside membrane contactors and the regeneration of these desiccants using membrane distillation. Triple-bore PVDF hollow fiber membranes yielded dehumidification permeance rates around 0.25-0.31 g m-2 h-1 Pa-1 in lab-scale trials. A vacuum membrane distillation unit utilizing PVDF fibers yielded a flux of 2.8-7.0 kg m-2 hr-1. When the membrane contactor dehumidification system was applied in a bench scale controlled environment agriculture setup, the relative humidity levels responded dynamically to both plant transpiration and dehumidification rates, reaching dynamic equilibrium levels during day and night cycles. In addition, recovered fresh water from distillation was successfully applied for irrigation of crops and concentrated desiccants were successfully reused for dehumidification. If applied in practice, the liquid desiccant system for controlled environment agriculture offers the potential to reduce water use in controlled environment agriculture by as much as ~99%.

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Biostimulants offer an innovative approach to potentially improve crop yield and quality under abiotic stresses. Particularly, plant-derived protein hydrolysates (PH), a mixture of amino acids and soluble peptides from enzymatic or chemical hydrolysis of agricultural waste, are gaining global interest due to their sustainability and positive effects on crops. However, a functional role of the PH in crop yield and quality remains uncertain and is proposed to be associated with its phytohormone-like activities or serve as an additional nitrogen (N) source. Besides, the effects of PH on crop yield and quality are limited in intensive production systems such as greenhouse facilities. The purposes of this research are to examine the effects and mechanisms of PH on crops and to assess the potential of PH application to reduce fertilizer use in crop production. The specific objectives were to; 1) elucidate the hormone-like activities of PH in the adventitious rooting formation of cuttings, 2) evaluate the effects of different PH application methods on greenhouse crop yield and quality under different N levels when plants are grown with a commercial growing medium, and 3) examine the effects of PH application methods on yield and quality of hydroponically grown lettuce under different N levels and forms. Three conclusions were that 1) <a>the hormonal effects of PH are attributed to brassinosteroid-mediated processes, and PH has overlapping functions with auxin during adventitious rooting of cuttings in a plant species-specific manner</a>, 2) root application of PH (PH-R) effectively improves nutrient uptake compared to foliar spray of PH (PH-F), subsequently, increases the lettuce and tomato yield and quality regardless of N levels while PH-R did not change the chemical properties of growing media, and 3) PH-R effectively increases root growth, and subsequently, improving shoot yield and quality with significant PH × N levels and PH × NO₃:NH₄ratios interactions. Also, PH-R counteracted the negative effects of low NO₃:NH₄ratios on lettuce yield. The outcomes provide the optimization of PH and N fertilization in modern sustainable greenhouse production and the development of a new strategy for producing high-quality greenhouse crops with improved nutrient use efficiency.

This thesis presents an innovative agricultural automation technique which focuses on addressing the significant perception challenges posed by occlusion within environments such as farms and greenhouses. Automated systems tasked with duties like pruning face considerable difficulties due to occlusion, complicating the accurate identification of plant features. To tackle these challenges, this work introduces a novel approach utilizing a LiDAR camera mounted on a robot arm, enhancing the system's ability to scan plants and dynamically adjust the arm's trajectory based on machine learning-derived segmentation. This adjustment significantly increases the detection area of plant features, improving identification accuracy and efficiency. Building on foreground isolation and instance segmentation, the thesis then presents an automated method for identifying optimal pruning points using best pose view images of indeterminate tomato plants. By integrating advanced image processing techniques, the proposed method ensures the pruning process by targeting branches with the highest leaf load. Experimental validation of the proposed method was conducted in a simulated environment, where it demonstrated substantially enhanced performance. In terms of pruning point identification, the method achieved impressive results with 94% precision, 90% recall, and 92% F1 score for foreground isolation. Furthermore, the segmentation of isolated images significantly outperformed non-isolated ones, with improvements exceeding 30%, 27%, and 30% in precision, recall, and F1 metrics, respectively. This validation also confirmed the method's effectiveness in accurately identifying pruning points, achieving a 67% accuracy rate when compared against manually identified pruning points. These results underscore the robustness and reliability of the approach in automating pruning processes in agricultural settings. / Master of Science / This thesis explores new methods for improving automated farming systems, particularly focusing on enhancing tasks like pruning where visibility of plant features can be significantly obstructed by overlapping leaves and branches. Central to this study is the development of an innovative approach using a special camera mounted on a robotic arm, which scans plants to determine the best vantage points for precise interactions. This setup not only identifies the optimal positions for viewing but also adjusts the robot's movements in real-time to ensure it can accurately perform pruning task. The innovative approach employed here leverages advanced technology to dynamically adjust the trajectory of the robotic arm based on real-time imaging. This enables the robot to better detect essential features of plants, which is crucial to make informed decision of where to prune the plant. By improving the robot's ability to clearly see and interact with plants, the system facilitates more precise and efficient operations. Tests conducted in simulated environments have demonstrated that this method significantly enhances the robot's capability to isolate and identify plant features accurately. These improvements make it possible for the robot to subsequently identify pruning points, potentially reducing the time and labor typically required in traditional manual operations. Overall, this research indicates that integrating advanced sensing and machine learning technologies into agricultural automation can revolutionize farming practices, making them more efficient and less dependent on human labor, especially in environments where traditional methods are less effective.

The world is facing a food shortage as the world’s population increases and arable land decreases. Despite this, the food industry is wasteful, and 30% - 40% of all produced food is lost before reaching the end consumer. Emerging technologies aim to increase the amount of food that can be grown per m2 or allow the growing of food in climates or on lands previously impossible. Four main farming techniques utilising these emerging technologies are Controlled Environment Agriculture, Hydroponic Farming, Urban Farming and Vertical farming. When used together, these techniques form the basis for what can be called a Plant Factory. Despite the positive effects these technologies have on the production rate, few Plant Factories have managed to achieve profitability. By creating support for developing the post-harvesting system for a plant factory, this thesis aims to aid in the development of profitable plant factories. The thesis uses Design Research Methodology to achieve this aim in three parts. The first part identifies the underlying factors of the post-harvesting system affecting plant factory profitability. The second presents a set of support components that will aid the developers to improve key factors affecting profitability. The third part is a case study where the support components applicability at targeting the key factors are evaluated, and suggestions for further improvements and testing of the support is suggested.  Further, using Design Research Methodology, the methods used to develop support in this thesis are presented to easily be replicated by other researchers to aid them in developing support for other industries and circumstances. The suitability of the developed support was tested using the principles of an initial DS-II. The developed support proved very useful for the investigated case, and with its conditions, the application evaluation was considered a partial success. Two key factors were successfully improved and indicated that the intended support is ready for a comprehensive DS-II. A third support component needs more work to provide the intended support fully. Therefore a second  PS iteration is recommended before a comprehensive DS-II is done to increase its value.

<p>This thesis aims to evaluate a Modular Manufacturing (MM) technical approach to Controlled Environment Agriculture (CEA) for cultivating plant food crops in a repurposed urban space. The specific approach was to fit a modular hydroponic CEA system into an insulated cooler box with environmental control to act as a micro plant factory. The feasibility of the approach was evaluated and a benchmark comparison between repurposed urban space and controlled lab environments was produced.</p><p>Possessing accessibility and affordability to desired quantitatively and nutritious food is a pillar for a healthy lifestyle, yet food insecurity is a growing problem worldwide, in industrial as well as industrializing nations. Food insecurity is defined as “lacking the ability to meet nutritional needs at one or multiple times during the year.” [1] Though Developing countries tend to score poorly on the Food Security Index [2], the issue is common in developed countries as well, where countries like the U.S. Possess a household food insecurity rate of above 10% [1]. Especially, subgroups of the urban population and university students in developed countries are represented at a higher rate concerning food insecurity [3], due to food insecurity’s dependence on socioeconomic factors such as purchasing power and local accessibility.</p><p>Bringing production close to the consumers or to the Point-of-Need (PoN) would be a valuable tool for supplementing traditional food crop production and increasing access to high-quality food for groups exposed to food insecurity. This is especially attractive in densely populated areas and college campuses, where real estate is prime. Bringing production to the PoN does however carry certain challenges, such as severe resource restrictions, which are not present in traditional agricultural production in rural areas where there is vast access to land, water, and plenty of sunlight. Pushing the boundaries of CEA research, technology, and application areas will be crucial for the utilization of nontraditional agricultural land, agricultural resource optimization, and food security improvements in difficult-to-farm environments to facilitate delivery to PoN.</p><p><br></p><p><b>Salient outcomes:</b> The salient outcomes of this research were that a MM platform was proven to be feasible for CEA cultivation of food crops in a repurposed urban space as well as a controlled location. Specimens cultivated in a repurposed urban space were shown to have a lower growth rate compared to a controlled location, but the important comparison is to the currently nonexistent productivity in such spaces.</p><p><b>Intellectual merit:</b> The MM CEA platform was designed, prototyped, and tested using components-of-the-shelf (COTS) as recommended by frugal engineering methodology [4]. This manufacturing platform was engineered for a case study for repurposing unused “garage space” on the college campus at Purdue University. The platform was further used for a set of studies to evaluate the feasibility of the MM platform and the production efficiency of the platform not only in a repurposed urban space but also across harsh environments across winter-spring seasons. Romaine lettuce cultivars were used as a sample plant for winter and spring studies due to their property as a popular consumable, nutritious, and relatively short growth time for better productivity. The following research issues were addressed by this research: (1) design of a modular manufacturing module; (2) testing of the module in the indoor controlled lab environment; (3) advancing design based on findings in no.2; (4) CEA testing of the integration of multiple modules (two and water supply) in the Purdue University garage (living lab) and the indoor lab environment.</p><p><b>Broader Impact:</b> The results from this research could serve as a proof-of-concept to validate the feasibility of functional modules and their integration in scaled-up urban food crop production using repurposed space. This case study especially could open opportunities for college campuses across the US (and the world), to repurpose multi-storied garage spaces for healthy food production at PoN, for example, accessible to students’ dorms and cafeterias. This MM model could further be extended to other forms of urban areas for food security and production in communities in the vicinity of garages and similar spaces in form. Utilizing unrecognized space resources in an otherwise resource-restricted environment could be the supplemental production needed to fight food desertification and insecurity in urban locations. Bringing food production to the PoN would increase the accessibility of high-quality and nutritious fresh produce, improving conditions for localized food insecurity problems.</p>

<p dir="ltr">Controlled environment agriculture (CEA) is a practice of food production under optimized conditions to intensify production yield, and thus has potential for addressing food security for a growing population. Aquaculture and aquaponics are two types of CEA that can produce aquatic animals along with plants using non-arable lands and lower inputs of water and nutrients. However, their operations have high energy consumption and generate considerable nutrient-rich sludge and wastewater, making their environmental performance an emerging research focus. This thesis quantitively analyzed the environmental sustainability of aquaponics and aquaculture production using life cycle assessment (LCA).</p><p dir="ltr">The LCA on aquaponics evaluated a marine aquaponics production system that grew shrimp, red orache, minutina and okahajiki, and analyzed the effect of salinity, C/N ratio, and shrimp-to-plant stocking density. The grow-out stage accounted for over 90% of total environmental impacts with electricity use as the predominant contributor. The marine aquaponic production exhibited best environmental performance when operated at low salinity (10 ppt), and high C/N ratio (15) and stocking density (5:1), which can be further improved by 95–99% via the use of wind power as electricity source. Additionally, variation in the prices of aquaponic products was found to improve the system’s environmental impacts by up to 8%.</p><p dir="ltr">The aquaculture LCA focused on shrimp recirculating aquaculture systems (RAS) and evaluated the environmental feasibility of microalgae-based wastewater treatment. Microalgae treatment effectively removed 74% of phosphate in RAS wastewater and thus reduced the freshwater eutrophication potential by 55%. However, its remediation performance was inferior to activated sludge treatment due to different operation scales. Electricity was the principal hotspot of microalgae treatment and made up over 99% of all the environmental impacts, which can be considerably decreased by reducing coal use in the electricity supply. Three utilization pathways for algal biomass (feed ingredient, biodiesel and biogas) were investigated; however, only biogas production was found to show environmental benefits to marine eutrophication remediation owing to the low biomass quantity produced.</p><p dir="ltr">While <a href="" target="_blank">aquaculture and aquaponics</a> play important roles in meeting the globally growing demand for seafood, this thesis provides valuable life cycle inventory data for these fields. Moreover, the LCA models developed in this thesis are useful decision-making tools for aquaculture and aquaponic producers to adapt farming practices with lower environmental footprint.</p>

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