Progress 07/01/24 to 02/28/25

Outputs
Target Audience:The customers that would be utilizing this monitoring platform can be divided between three different VF sizes: small (converted shipping containers), medium (between four to six tiers in a warehouse-style setting), and large scale VFs (massive complexes with greater than six tiers in a single growth rack). During this work, an industry conference, Indoor AgCon 2025, was attended to expand customer discovery further. The intended audience for the initial MMS offering would be medium to larger scale VFs as these farms have the most crop coverage needed. These initial customers of the Mobile Monitoring System (MMS) are expected to be VFs growing lettuce varieties. Lettuce is the most commonly grown crop in these sorts of facilities and has, thus far, been the plant variety used during the development of the MMS technology. As the technology matures, other varieties of crops can be included to expand the customer base. Reaching these clients will occur both directly to currently operating VFs as well as farm manufacturers. By working with farms under construction, we can immediately integrate this innovative monitoring system in their farms while greatly reducing their crop monitoring investment costs. The MMS can instantly provide workers with 24/7 monitoring activity and recommend specific tasks to be completed or problem areas to address (e.g., pest removal or nutrient adjustments). Additionally, because of the ease of scalability and complete farm coverage, workers can remotely access the status of crops in difficult to see areas which is especially useful for farms above five tiers. In preparation for the Phase II program, Acreage Farms established a second office at the University of South Florida to enable plant science students to assist with various growth studies with different stress conditions imposed on the crops to develop image datasets for the vertical farm environment. These datasets will be crucial for advancing our future machine learning algorithms and provide a better product to our intended clients. Through this partnership, a matching grants program can be obtained from the Florida High Tech Corridor to expand student involvement as well as the intended project scope. Another effort that was made possible from this work was an internship for an undergraduate from Embry-Riddle Aeronautical University to manage crops from seed to harvest. Separate from this specific project, but in the same period of time, Acreage Farms utilized knowledge learned in hydroponics to enable ~20 high school students to build their own vertical growing units via a partnership with a local chapter of UnitedWay. Lastly, using the progress from this Phase I opportunity, we intend to incorporate local farms to pilot the prototype through the Phase II portion. Changes/Problems:Several notable changes were made to the project approach over the course of the award period to optimize system performance and reduce design limitations. Early on, the initial implementation of a Jetson Orin Nano was substituted with a Raspberry Pi Zero 2W. This change was due to the Jetson's larger size and limited compatibility with required environmental and imaging sensors. Additionally, the GPIO pins to power and control devices did not function as intended, which impacted our developmental approach. The adjustment away from the Jetson enabled the development of a more compact sensor suite, better aligned with the intended application while still enabling the comparison between higher (Raspberry Pi 4) and lower cost hardware systems. Another change in design to speed up development work was the use of cable carriers in place of a battery-powered electrical system utilizing wireless charging. The selection of cable carriers was originally adopted to simplify the early development of the movement system and increase focus on the sensor suites, thus deferring battery integration until after achieving stable and consistent motion. This choice also provided the team additional time to devise alternative power strategies to simplify the MMS design. The cable carriers were ultimately removed and replaced with a lightweight and innovative solution to provide power along each aluminum extrusion member, eliminating the need for batteries or docking locations for charging while providing continuous and stable wall power. Specifically, copper strips were embedded along the tracks and custom-designed sliding contacts were added to the sensor suite housing to provide continuous power delivery to the sensor platform while in motion. This significantly reduced the weight associated with cable carriers and batteries while offering a more scalable alternative. Additional adjustments in our approach included new hardware (rotary encoders and limit switches) to better define the global and local reference frame as the sensor suite moves around. The devices enabled us to relay a confirmation of the successful execution of a movement command sent to different microcontrollers. What opportunities for training and professional development has the project provided?Over the course of the project, numerous opportunities for training and professional development were provided to team members. The interdisciplinary nature of the work required cross-training in software development (e.g., Python, Next.js), hardware integration, circuit design, mobile-based system architecture design and computer-aided design (CAD) for prototyping and assembling the mobile monitoring system. These hands-on experiences significantly enhanced the team's technical capabilities and problem-solving skills. Beyond the initial team, an undergraduate intern was hired and trained in vertical farming operations, hydroponic system management, and plant care, gaining practical experience in crop production and agricultural technology. This provided them with a foundational understanding of controlled environment agriculture (CEA) and introduced them to applied research in precision agriculture. Beyond these, this project has provided the team the ability to expand their business network and growth opportunities by connecting with local CEA farms and universities for future collaborations. In particular, we have met with a professor at the University of South Florida to plan out and work together through the Phase II portion of the work. The team also benefited from professional development through participation in industry events. Members attended Indoor AgCon, a premier conference for indoor and vertical farming, where they networked with industry leaders and learned about emerging technologies as well as industry trends. Additionally, the team was invited by the Florida High Tech Corridor/ Cenfluence to table at Synapse, a major innovation and entrepreneurship conference in Tampa, Florida. This opportunity allowed them to showcase the project to a broader technology community, connect with fellow startups and researchers, and gain visibility among potential collaborators and investors. As a direct result of this event, the team was referred to two local vertical farms, opening the door to potential partnerships for pilot testing and feedback. How have the results been disseminated to communities of interest?Progress and results of this Phase I work were shared with some previously interviewed persons as well as around Indoor AgCon to continue expanding customer discovery and generate interest for potential pilot studies. This was completed purely through one-on-one discussions. Additionally, the team presented our technology at Synapse in Tampa, FL which enabled us to expand our network and potential client pool and garner feedback. What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? The project successfully progressed through all four stated objectives, with additional refinements that further increased its feasibility and commercial potential. Objective A: A functional hydroponic testbed was built to support four tiers of 45 crops each, simulating a controlled vertical farming environment. Three distinct LED lighting configurations (white, pinkish, and red-blue) were installed to mimic real-world variability in lighting strategies. Mylar barriers were added to prevent inter-tier light contamination, and fans were included at each level to control airflow. A central reservoir enabled continuous water circulation. A separate grow tent was used for seed germination before transplanting into the main system. Multiple successful seed-to-harvest grow cycles (~6 weeks per cycle) were completed, with produce donated to local nonprofits and community members. Crop maintenance methods, such as substrate selection, cleaning routines, and water quality management, were iteratively improved throughout the program. Objective B: The project's core technology development, the MMS, was installed on each of the largest sides to develop a higher and lower fidelity data capture solution. This gantry-style system enables 3D (XYZ) motion of a sensor suite, along with a 4th degree of freedom to rotate the system in and out of a tier, to enable simultaneous environmental and crop image data over multiple tiers. The higher-fidelity MMS included more precise environmental sensors and a thermal camera, while the standard version offers a more limited and affordable configuration. Both sensor suites were housed in custom-designed, 3D-printed enclosures and capture measurements for temperature, relative humidity, CO?, airflow, and light intensity. Data transfer was handled via MQTT protocols between single-board computers and microcontrollers, with all data in a MongoDB database hosted on a local network-attached storage device. Significant hardware refinements were made following initial development. A custom counterweight and a turnbuckle system were integrated to stabilize the vertical movement arm and prevent sagging when entering the plant canopy to ensure consistent, level, and straight motion across tiers. Copper strips were embedded along the extrusion tracks to enable continuous power delivery while reducing mechanical complexity, weight, and cost associated with cable carrier or battery-based solutions. This innovation allowed for a slimmer design that mounts directly onto the top and bottom shelves of existing shelving units in farms. Through these design characteristics, the MMS can plug-and-play into current vertical farms without obstructing worker access and can be easily moved aside when needed. Objective C: Environmental and image data were collected through the MMS units and stored for later analysis. Initially, renders were created to demonstrate concepts of the data analysis dashboard and a template was programmed for a functional navigation bar, and user authentication, while being mobile friendly. Following these, a modern web application using Next.js was developed to provide intuitive visualization of plant-specific and environmental data. Users can view time-series trends and inspect individual plant zones for detailed conditions based on the desired environmental variable. Feedback from early customer discovery interviewees validated the design's clarity and usability. Objective D: To assess economic potential, a modular bill of materials was made to be able to create pricing determined by combining cost-plus and value-add methods. To identify the hardware cost, the facility size (in terms of rack height, and width), the number of MMS (generally one per rack within reason), and fidelity of the sensors and computational power in the MMS needed must be determined to calculate the cost-plus portion of the work. Based on these values, labor cost and a profit margin will then be included. An installation fee will be charged based on the size of the facility and distance required to transport the system. All of these data points with a standard tax will inform the total price. Larger farms will require more sensor suites and track but with the size also comes the economy of scale. As an example, if we plan on integrating into a standard shipping container farm (320 sq ft) with a high fidelity sensor suite the pricing breakdown for two units installed would be ~$37,000. Next, a value-add component will be calculated and bundled into the pricing of the software to capture value from potential cost savings and profits for a facility such as estimated crop yield increases or labor saved. Additionally, the software license will have annual or monthly renewals for access to the dashboard and analytics. We will continue market research with vertical farms during Phase II and conduct beta testing to validate the pricing model and quantify the value added. Once we onboard our first client, we anticipate adding 6 additional farms within a year period which allows for us to break into the market and develop quality relationships. As we establish ourselves and provide benefits to these early clients, we are planning to onboard a new farm per month, of the approximately 2000 VFs in the US. From our market research we are anticipating a profit margin between 20-25% on hardware sales for smaller and prefabricated farms. For larger commercial scale farms we anticipate a profit margin of around 35% for hardware sales. We can absorb less profit from smaller farms to allow for higher saturation in the owner operated market which is where we believe we can make a higher impact and generate more sales. We expect the profit margin from our software offering to be very high (> 75%) and to have a high adoption rate of the software. Having low margin hardware allows more farms to acquire the technology and enables the use of our higher margin software suite. Most of the recurring revenue will be made in software sales. In the future we intend to sell anonymized data to researchers, agronomists, and developers. The way we will engage repeat sales will be through high customer satisfaction and retention. In doing so, any future facility expansions will incorporate our technology. Additionally, we will have subscription based software enabling recurring revenue which will require that the software provides critical insights and high value to the vertical farm operators. The scalable MMS hardware and software platform lays the groundwork for applying machine learning models in Phase II to assess plant health, detect abnormalities, and provide actionable recommendations to improve yields.

Publications

Progress 07/01/24 to 02/28/25

Outputs
Target Audience:The customers that would be utilizing this monitoring platform can be divided between three different vertical farm (VF) sizes: small (converted shipping containers), medium (between four to six tiers in a warehouse-style setting), and large scale VFs (massive complexes with greater than six tiers). After expanding customer discovery efforts, the intended audience for the initial MMS offering would be medium to larger scale VFs as these farms have the most crop coverage needed. Reaching these clients will occur both directly to currently operating VFs as well as farm manufacturers. By working with farms under construction, we can immediately integrate this innovative monitoring system in their farms while greatly reducing their crop monitoring investment costs. Through integrating the Mobile Monitoring System (MMS) into these facilities, it can instantly provide workers with 24/7 monitoring activity and recommend specific tasks to be completed or problem areas to address (e.g., pest removal or nutrient adjustments). Additionally, because of the ease of scalability and complete farm coverage, workers can remotely access the status of crops in difficult to see areas which is especially useful for farms above five tiers. Through the proof of concept being created as part of the current Phase I work, Acreage Farms has been developing relationships with local Florida farms and universities for future collaborations as part of the Phase II process. In particular, the team is currently communicating with the University of South Florida to explore obtaining an office space to carry out various growth studies with different stress conditions imposed on the crops to develop image datasets for the vertical farm environment. These datasets will be crucial for advancing our future machine learning algorithms and provide a better product to our intended clients. Changes/Problems:Two major changes have occurred during this work. First was the substitution for a Jetson Orin Nano for a Raspberry Pi Zero 2W. The reason for this change was due to the Jetson's physical size being too large for this application and it having many compatibility issues with various environmental and imaging sensors. The change in single board computers enabled the project to have a smaller sensor suite option which increased the potential for the technology to function as intended. The second major change was the use of cable carriers rather than battery power with wireless charging. The reason for this change was to allow for the continuous and dedicated development of the movement system without the additional complexities and challenges. The inclusion of batteries and charging capabilities will take place once the movement system is completed and reliable. Additionally, a secondary prototype station will first have this added benefit before converting the current testbed, allowing for a smooth transition of development. What opportunities for training and professional development has the project provided?Thus far, the project has provided numerous opportunities for both training and professional development. In particular, team members have had to cross-learn many skills such as different software programs (e.g., Python and Next.js) to operate and maneuver the sensor suite across the crops. Other skill development has been related to hardware integration, circuity, and computer aided design. Additionally, an undergraduate student intern was hired to learn about vertical farming and plant science while managing the crops growing throughout this work. Beyond these, this project has provided the team the ability to expand their business network and growth opportunities by connecting with local CEA farms and universities for future collaborations. How have the results been disseminated to communities of interest?Current results have not been released to communities of interest. The team will be attending Indoor AgCon in March '25 to complete further customer discovery as well as discuss our progress to gain interest from potential clients. What do you plan to do during the next reporting period to accomplish the goals?To complete the goals set out at the beginning of this project, the team will be focusing on two main directions. First, the program currently operating each sensor system needs to be upgraded to autonomously move throughout a tier, exit, move to the next tier, enter, and complete a scan. With this change in operation, the system will achieve the main goal of collecting environmental and crop image data using a single sensor suite over multiple tiers. While this data is being recorded, the analytics dashboard needs to intuitively display the results for a user. Once these two directions are accomplished, demos can be shown to previous interviewees who were interested in seeing the team's progress. Doing so will help improve the core technology for its intended users and can aid in determining economic and technical viability. Beyond these main directions, the commercial feasibility of the technology will be further assessed through market research, exploring pricing options, and customer discovery.

Impacts
What was accomplished under these goals? The goals for this project are split between four overall objectives. First, the hydroponic testbed was constructed to grow four tiers of 45 crops. To account for various vertical farming environments, three LED lighting options were included (i.e., white, pinkish, and blue-red) along with mylar layers placed beneath each tier's hydroponic channels to prevent the lights from bleeding through into the next tier. This testbest also had fans on each tier installed and a central water tank for water to circulate out and then drain back. A separate grow tent was utilized to house a seed starting station which allows the seeds to germinate and grow for two weeks before being transferred to the testbed. With all of the systems in place to grow crops, multiple cycles have been completed from seed-to-harvest which has been donated to nonprofits, friends, and family. Continuous improvements are being incorporated to our growing methodology (e.g., water condition tracking, cleaning, substrate usage). With the vertical farm testbed operational, the project's core technology (the mobile monitoring system) was installed on each of the largest sides to develop a higher and lower fidelity data capture solution. This gantry-style system enables 3D motion of a sensor suite taking environmental and crop image data. Two electronics bays were wired on each side to transfer power as the system moves around. To mitigate slippage from the stepper motor enabling vertical motion, a counter weight was added which equalizes the force on each side of the motor. Additionally, a hinge and a linear actuator were used to allow the sensor suite to enter or exit from a tier. Concurrently, the next objective required each sensor suite to be soldered together, encompassing a collection of environmental (i.e., temperature, relative humidity, CO2 concentration, wind speed, and light intensity) and imaging sensors, and inserted into custom 3D printed cases. The two sensor systems varied from one another due to higher quality environmental sensors and the inclusion of a thermal camera in the higher fidelity model. Moving the system around was made possible through communicating between single-board computers to microcontrollers via MQTT. Data collected is transferred to a network attached storage device which will later be used for training machine learning models. To visualize the measurements, a web-based dashboard is currently in development. At the midpoint of this work, initial renders have been created to demonstrate concepts of the application and a template has been programmed consisting of a functional navigation bar, user authentication, and is mobile friendly. The last objective for this work is based on determining the economic viability for this technology. One method for exploring this possibility has been through customer discovery to meet and learn from those working in the controlled environment agriculture field. Additionally, a modular bill of materials is being created to explore system costs based on various sizes of vertical farms. Another aspect to improve the viability of this work is a commercialization plan which is being drafted and then reviewed by an expert in the crop monitoring space

Publications