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** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** HARNESSING RECENT ADVANCES IN SENSOR DEVELOPMENT, WASTEWATER TREATMENT, HYDROPONIC SYSTEMS, MACHINE LEARNING (ML), AND REINFORCEMENT LEARNING (RL), WE PROPOSE A NEW DECENTRALIZED WASTEWATER-HYDROPONIC SYSTEM (WWHS) TO ACHIEVE STABLE AND HIGH VEGETABLE PRODUCTION WITH MINIMIZED RESOURCE AND ENERGY CONSUMPTION THROUGH THE DEVELOPMENT OF NOVEL ONLINE DATA ACQUISITION AND SYSTEM CONTROL AND OPTIMIZATION METHODOLOGIES. LOCAL AND DECENTRALIZED CONTROLLED ENVIRONMENT AGRICULTURE (CEA) HAS THE POTENTIAL TO COMBAT CENTRALIZED RESOURCE (WATER, NUTRIENT) IMBALANCE AND SCARCITY TO ENHANCE FOOD SECURITY AND ENVIRONMENTAL CONTROL. HOWEVER, CURRENT CEAS STILL FACE SIGNIFICANT CHALLENGES INCLUDING LIMITED DATA REQUISITION, LACK OF RELIABLE DYNAMIC CONTROL, AND HIGH OVERALL COST. TO ADDRESS THESE ISSUES, THE PROJECT AIMS TO EMPOWER NEXT-GENERATION CEA FACILITIES WITH CAPABILITIES OF RELIABLE SENSING, PERCEPTION, AND OPTIMAL DECISION-MAKING, PAVING THE WAY FOR AUTONOMY. THIS STUDY IS CORROBORATED BY TWO WELL-ESTABLISHED PHYSICAL TESTBEDS INCLUDING A PILOT-CEA SYSTEM AT GEORGIA TECH (GT) AND A HYDROPONIC GREENHOUSE SYSTEM AT THE UNIVERSITY OF GEORGIA (UGA), NOVEL DATA ACQUISITION DEVICES DEVELOPED AT GT INCLUDING ONLINE MEMBRANE SENSOR DEVICES AND ROBOTIC PHENOTYPING TECHNOLOGIES, AND A SUITE OF ADVANCED REINFORCEMENT LEARNING ALGORITHMS DEVISED AT GT FOR OPTIMIZING STOCHASTIC DYNAMIC SYSTEMS.?

$125,000FY2024National Institute of Food and AgricultureUSDA

University Of Georgia Research Foundation, Inc.

Investigators

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** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** HARNESSING RECENT ADVANCES IN SENSOR DEVELOPMENT, WASTEWATER TREATMENT, HYDROPONIC SYSTEMS, MACHINE LEARNING (ML), AND REINFORCEMENT LEARNING (RL), WE PROPOSE A NEW DECENTRALIZED WASTEWATER-HYDROPONIC SYSTEM (WWHS) TO ACHIEVE STABLE AND HIGH VEGETABLE PRODUCTION WITH MINIMIZED RESOURCE AND ENERGY CONSUMPTION THROUGH THE DEVELOPMENT OF NOVEL ONLINE DATA ACQUISITION AND SYSTEM CONTROL AND OPTIMIZATION METHODOLOGIES. LOCAL AND DECENTRALIZED CONTROLLED ENVIRONMENT AGRICULTURE (CEA) HAS THE POTENTIAL TO COMBAT CENTRALIZED RESOURCE (WATER, NUTRIENT) IMBALANCE AND SCARCITY TO ENHANCE FOOD SECURITY AND ENVIRONMENTAL CONTROL. HOWEVER, CURRENT CEAS STILL FACE SIGNIFICANT CHALLENGES INCLUDING LIMITED DATA REQUISITION, LACK OF RELIABLE DYNAMIC CONTROL, AND HIGH OVERALL COST. TO ADDRESS THESE ISSUES, THE PROJECT AIMS TO EMPOWER NEXT-GENERATION CEA FACILITIES WITH CAPABILITIES OF RELIABLE SENSING, PERCEPTION, AND OPTIMAL DECISION-MAKING, PAVING THE WAY FOR AUTONOMY. THIS STUDY IS CORROBORATED BY TWO WELL-ESTABLISHED PHYSICAL TESTBEDS INCLUDING A PILOT-CEA SYSTEM AT GEORGIA TECH (GT) AND A HYDROPONIC GREENHOUSE SYSTEM AT THE UNIVERSITY OF GEORGIA (UGA), NOVEL DATA ACQUISITION DEVICES DEVELOPED AT GT INCLUDING ONLINE MEMBRANE SENSOR DEVICES AND ROBOTIC PHENOTYPING TECHNOLOGIES, AND A SUITE OF ADVANCED REINFORCEMENT LEARNING ALGORITHMS DEVISED AT GT FOR OPTIMIZING STOCHASTIC DYNAMIC SYSTEMS.? · GrantIndex