**AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** IN THE PROPOSED EFFORT, VERDANT ROBOTICS AND PROFESSOR AARON SMITH WILL DEVELOP AND TEST A NOVEL BIO-ECONOMIC FARM MODEL (BEFM) TO PROVIDE A MECHANISTIC TOOL FOR MODELING THE ECONOMIC AND AGROECOLOGICAL VALUE CREATED THROUGH ADOPTION OF NEXT GENERATION PRECISION AGRICULTURE TECHNOLOGIES (NG-PATS) (E.G. AUTONOMOUS FARM MANAGEMENT ROBOTICS) AND HOW THE FARM-LEVEL VALUE AND ADOPTION CHANGES AS A FUNCTION OF POLICY AND MARKET VARIABLES SUCH AS FARM SCALE, PROFIT MARGINS, AND LABOR AVAILABILITY. THE MODEL WILL BE TESTED THROUGH A COMPARISON OF FIELD DATA COLLECTED FROM NG-PAT PROVIDERS AND INTERVIEWS WITH FARMERS. TESTING WILL BE ACCOMPLISHED BY DATA COLLECTION ON FARM DEPLOYMENTS OF AUTONOMOUS AGRICULTURAL ROBOTICS BY VERDANT ROBOTICS; A TYPICAL EARLY DEPLOYMENT IS PERFORMED ON A SUBSET OF THE OVERALL FARM ACREAGE ALLOWING FOR A 1:1 COMPARISON OF OUTCOMES ON ACREAGE UTILIZING NG-PATS AND ACREAGE UTILIZING TRADITIONAL METHODS. ADDITIONAL DATA WILL BE COLLECTED VIA SURVEYS OF FARMERS.FOR THE PROPOSED EFFORT, THE SCOPE OF END-USE STUDY WILL BE RESTRICTED TO CERTAIN HIGH VALUE HORTICULTURAL APPLICATIONS SUCH AS CARROTS OR APPLES AND THE MODEL IS EXPECTED TO BE REFINABLE AND EXTENDABLE TO OTHER AGRICULTURAL AREAS SUCH AS OIL AND GRAIN CROPS, FIBER CROPS, AND OTHER ROOT OR TUBER CROPS, THOUGH SUCH AN EXTENSION IS BEYOND THE SCOPE OF THE CURRENT EFFORT. THE PRIMARY FOCUS OF THE PROPOSED EFFORT WILL BE TO PROVIDE A TOOL TO UNDERSTAND THE EFFECTS OF PRECISION AGRICULTURAL TECHNOLOGIES AT THE FARM AND COMMUNITY LEVEL. LARGER MACROECONOMIC EFFECTS CAN POTENTIALLY BE MODELED AS POPULATION OUTCOMES USING STATISTICAL OR MACHINE LEARNING MODELS, SUCH AS MONTE CARLO SIMULATIONS, FOR EXAMINING THE GLOBAL IMPACTS OF TECHNOLOGY POLICY ON FARM ECONOMICS.THROUGHOUT HISTORY, FARM LABOR SHORTAGES HAVE SPURRED TECHNICAL INNOVATION IN AGRICULTURE. IN A 2019 CALIFORNIA FARM BUREAU FEDERATION (CFBF) SURVEY, 56% OF PARTICIPATING FARMERS INDICATED THAT AT SOME POINT IN THE PREVIOUS 5 YEARS, THEY HAD DIFFICULTY HIRING THE NUMBER OF EMPLOYEES THEY NEEDED FOR PRODUCTION OF THEIR MAIN CROP (CFBF 2019). THIS PERCENTAGE WAS VIRTUALLY UNCHANGED FROM A 2017 CFBF SURVEY. AS IN THE PAST, THE CURRENT LABOR SHORTAGE IS FORCING SOME FARMERS TO LOOK TOWARDS INCREASED MECHANIZATION TO MAINTAIN THEIR PRODUCTIVITY. OF THE RESPONDENTS, 56% STATED THEY HAD USED A LABOR-SAVING TECHNOLOGY WITHIN THE PREVIOUS 5 YEARS, WITH BOTH RISING LABOR COSTS AND NOT ENOUGH WORKERS CITED AS REASONS BY THE MAJORITY OF RESPONDENTS (CFBF 2019).BIG DATA USE IN AGRICULTURE HAS BEEN SHOWN TO IMPROVE THE PRODUCTIVITY OF FARMS. IN FRANCE, SMAG HAS POOLED 30 YEARS OF WEATHER DATA HISTORY, SATELLITE AND DRONE IMAGES, AND SOIL TYPES TO DEVELOP AN ALGORITHM THAT SUCCESSFULLY PREDICTS CROP YIELDS. WEED AND DISEASE IDENTIFYING APPS THAT COMPARE PHOTOS OF PLANTS ON THE FARM TO A LARGE DATABASE OF IMAGES UTILIZE MACHINE LEARNING TO CONSTANTLY IMPROVE THE SOURCE DATABASE, WHICH IN TURN HELPS FARMERS IDENTIFY AND TREAT THE WEEDS OR DISEASE FASTER, ULTIMATELY INCREASING CROP YIELD. VERDANT'S PROPOSED TECHNOLOGY BUILDS ON THIS CONCEPT BY ALSO BEING ABLE TO TREAT THE WEEDS OR DISEASE WITH A ROBOT. THE ROBOT WOULD IDENTIFY THE DISEASE OR WEED ON ITS TRIPS THROUGH THE ROWS OF CROPS, REMEMBER THE LOCATION, AND LATER RETURN WITH A TREATMENT TARGETED SPECIFICALLY FOR THAT DISEASE OR WOOD. THE ROBOT WOULD ALSO BE ABLE TO MONITOR THE RESULTS OF THE TREATMENT OVER TIME, TO DETERMINE THE EFFICACY. THE LABOR COSTS ASSOCIATED WITH THE TREATMENT WOULD BE MINIMAL, AS NO PERSON WOULD BE REQUIRED TO PHYSICALLY RETURN TO THE LOCATION OF THE IDENTIFIED WEED.THE USE OF BIG DATA TO IMPROVE FARM PRODUCTIVITY AND ULTIMATELY, PROFITABILITY, ALLOWS ALL FARMERS ACCESS TO DATA THAT PREVIOUSLY WOULD HAVE BEEN LIMITED TO LARGE, CORPORATE FARMS. GIVEN THE MANY RISKS TO THE FOOD SUPPLY CHAIN, INCLUDING LABOR SHORTAGES AND CLIMATE CHANGE, INCREASING THE NUMBER OF SUCCESSFUL SMALL TO MEDIUM SIZED FARMS SERVES TO MITIGATE THE RISK OF FOOD SHORTAGES IN THE UNITED STATES. VERDANT'S TECHNOLOGY PROVIDES AN ECONOMICALLY VIABLE OPTION TO SMALL AND MEDIUM SIZED FARMS LOOKING TO USE AI AND ML TO IMPROVE THEIR FARM PRODUCTIVITY, WHILE REDUCING LABOR COSTS AND OFFERING A SOLUTION TO THE DIMINISHING SUPPLY OF FARM LABORERS.VERDANT'S MISSION IS TO USE THE TECHNIQUES OF MODERN DATA SCIENCE, DEEP LEARNING, AND ROBOTICS TO CREATE IMPROVED ECONOMICS IN AGRICULTURE AND SECURE THE AGRICULTURAL PRODUCTION BASE AGAINST FUTURE DISRUPTION (ALSO KNOWN AS DIGITAL AGRICULTURE OR AGRICULTURE 4.0). THIS IS STRATEGICALLY IMPORTANT TO THE UNITED STATES, IMPORTANT TO THE FUTURE OF THE AMERICAN FARMER, AND IMPORTANT TO FOOD SECURITY FOR BILLIONS OF PEOPLE THROUGHOUT THE WORLD.AUTONOMOUS OR SEMI-AUTONOMOUS FARM ROBOTICS IS A VERSATILE AND INCREASINGLY ECONOMICALLY VIABLE SOLUTION TO BOTH RISING LABOR COSTS AND FARM WORKER SHORTAGES THAT WILL BE ACCESSIBLE FINANCIALLY TO FARMS OF ALL SIZES AND THAT HAS BEEN MADE POSSIBLE BY SWEEPING INNOVATION IN INFORMATION TECHNOLOGIES OVER THE PAST DECADE. THE INDIVIDUAL PLANT LEVEL DATA GATHERED BY THE PROPOSED ROBOTIC TOOL WILL ALLOW FARMERS TO TARGET AREAS OF NEED WITH PRECISION, THUS LOWERING LABOR COSTS, LIMITING PESTICIDE USE AND INCREASING CROP YIELD.DIGITAL FARMING TECHNOLOGIES LIKE THOSE BEING PIONEERED BY VERDANT HAVE THE POTENTIAL TO TRANSFORM AGRICULTURAL SYSTEMS MAKING THEM MORE PRODUCTIVE, RESILIENT, AND SUSTAINABLE. GLOBAL AGRICULTURE FACES SIGNIFICANT CHALLENGES TO MEET THE GROWING DEMAND FOR VALUE-ADDED AGRICULTURAL PRODUCTS IN A WORLD WHERE THE TOTAL POPULATION IS EXPECTED TO REACH NEARLY 11 BILLION BY 2050. NOT ONLY DOES THE PRODUCTIVITY PER UNIT LAND NEED TO INCREASE BY 70% OVER THE NEXT 30 YEARS, THIS NEEDS TO HAPPEN WITHOUT FURTHER POLLUTION OF SOIL, WATER, AND OTHER AGRICULTURAL/ECOLOGICAL SYSTEMS. DIGITAL FARMING ADDRESSES THESE PROBLEMS USING INFORMATION TECHNOLOGIES, ROBOTICS, AND AUTONOMY TO COLLECT AND ANALYZE DATA TO SUPPORT THE MOST EFFICIENT FARMING PROCESS AND TO ACT ON THOSE ANALYSES IN REAL TIME WITH MINIMAL HUMAN OVERSIGHT.$650,000
· FY2022 · National Institute of Food and Agriculture