Lateral.systems LLC is an agricultural technology startup company

Lateralsystems LLC received a $181,650 Small Business Innovation Research Phase I Project Grant from the United States Department of Agriculture National Institute of Food and Agriculture to develop a smart farming platform for indoor agriculture. Specifically, the company will test and evaluate an open-source software framework to determine its feasibility for commercializing a product and services that integrate relevant sensors and fuse disparate datasets to aid farmers in improving indoor growing operations, such as aquaponics. The goal of this 6-month feasibility study funded through the SBIR program is to advance critical enabling features of an on-premise smart service for farming applications.

Our mission is to enable generations of farmersby accelerating the intersection of promising modern technologiesto advance sustainable production of food. Our primary focus is developing a compute platform for indoor agriculture. A platform consists of hardware, data storage devices, software applications, and networks that support the processing and exchange of electronic information. In this case, we are developing a platform used for monitoring and maintaining biological, chemical, and physical elements within indoor growing environments such as greenhouses, repurposed warehouses, dry docks, manufacturing facilities, barns, and shipping containers. Our initial target with this industry is aquaponics, one of the fastest-growing industries in the U.S. (mordor intelligence, 2021).a key motivation for making aquaponics our beachhead is the global water crisis. The world bank (2021) estimates that future demand on fresh water resources by all sectors will require as much as 25-40% of water to be reallocated from lower to higher productivity, particularly in water stressed regions. Given that agriculture accounts for approximately 70% of freshwater consumptive withdrawals globally (goddek, ET AL., 2019), supporting the growth and success of aquaponics represents a highly promising approach to sustainably feeding the world. Aquaponics is organic agriculture that pairs fish and plant cultivation into a mutually beneficial growing system using an average of 5% of the water typically used to grow the same plants in soil (kelly, 2021). Well-designed aquaponics produces an average of 30-40% higher yields than plants grown in soil using a fraction of the energy and other costly external inputs (fernadez-cabanas, ET AL., 2020) while producing little pollution (mallick ET AL., 2021).as promising as this novel farming approach is, aquaponics comes with risks and requires deep knowledge to be successful (turn & scaron; EK ET AL., 2019). However, a worldwide survey of commercial aquaponic growers found that only 59% of respondents had some relevant prior knowledge during the start-up phase; 41% claimed to have insufficient knowledge of both fish and plants in their first year. Approximately 1/3 of aquaponic businesses are started by entrepreneurs with no prior training or experience in growing fish or plants (greenfield, 2020). High initial financial outlays on aquaponics systems and a steep learning curve pressured by tight profit margins often result in failure or at best, an average of 7-to-12 years to pay off investments (baganz ET AL., 2020). Our smart indoor farm monitoring and management tools will advance adoption and success of aquaponics farming by significantly reducing pain points and risks.existing indoor agricultural technologies such as digital probes for collecting data suffer from an inability to compile and interpret information due to a number of technical challenges. Currently, a non-proprietary modular plug and play platform approach tuned for indoor agriculture to integrate relevant sensors and the data they produce is non-existent. The fusion of relevant and disparate datasets is required to aide farmers to improve their respective growing operations (open AG data alliance, 2021). Our technology will advance USDA strategic goal 2 (maximizing the ability of american agricultural producers to prosper) and nifa priorities by accelerating controlled agricultural growing systems by enabling precision controls for indoor growing that conserve water and energy while producing healthy food for growing populations. The goal of this USDA nifa sbir phase i feasibility study is to advance critical enabling features of an on-premise smart service for farming applications. The scope of this project is to test and evaluate a well-established open-source compute framework to determine if this software and hardware technical approach is an effective solution to commercialize our product and services. This open-source software framework is designed to facilitate interoperability between multiple devices (such as sensors) and software applications. We will know that this particular framework is a feasible solution if we are able to validate that we can use the software stack to activate data flow (i.e., movement of data from various sensors in the fish tanks and plant beds to software), data fusion (e.g., overlaying multiple PH and temperature readings over time), and control logic (i.e., a key part of a software program that controls the operations of the program. The control logic responds to commands from the user and IT also acts on its own to perform automated tasks structured into the program).the development and testing steps are crucial to ensure that we select a solid software stack that will enable developers to code better and faster with a well-documented codebase. Testing to see how easy IT is to run tests on the platform now will helps US to make informed decisions about how easy IT will be to maintain the system, detect and fix common bugs or performance issues and tweak features without eating up too much time. Determining if the platform has a well-defined scalability matrix that works well for vertical scalability (adding features) and horizontal scenarios (handling increased volume of users and transactions on the platform) while maintaining security both on the client and server side will ensure that we avoid choosing the wrong stack.if the open framework we test proves to be suitable, this will advance our development towards beta testing our product and services within one year in preparation of a soft launch shortly thereafter. Revenue generated from the soft launch will fund further development of cloud-based features that will enable more advanced data analysis, data modeling, and predictive analytics that have the potential to support farmers' long term business management and increase their productivity and profits. Choosing the wrong stack could lead to additional time to adapt and rebuild from the ground up or potentially create extra work down the road with tasks such as tracking frequent update cycles that require constant changes to keep software applications running with the latest codebase, which may become unsustainable.