Journal
ACS ES&T ENGINEERING
Volume 2, Issue 1, Pages 3-19Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acsestengg.1c00269
Keywords
Artificial Intelligence; Phenotyping; Automation; Precision Agriculture; Plant Growth Modeling
Categories
Funding
- U.S. Department of Agriculture [2018-68011-28371]
- National Science Foundation [2112533]
- National Science Foundation-U.S. Department of Agriculture [202067021-31526]
- Directorate For Engineering
- Div Of Civil, Mechanical, & Manufact Inn [2112533] Funding Source: National Science Foundation
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Inefficiencies and imprecise input control in agriculture have caused devastating consequences to ecosystems. Urban controlled environment agriculture (CEA) is proposed as a solution, but precise control of inputs is limited. This review suggests integrating mechanistic and physiological plant growth models (MPMs) with machine learning (ML) algorithms to reduce data requirements and costs, and ML can help elucidate parameters and causal inference in MPMs. The proposed dynamically controlled environment agriculture (DCEA) framework has the potential to enhance urban resilience, human health, and optimize productivity and nutritional content.
Inefficiencies and imprecise input control in agriculture have caused devastating consequences to ecosystems. Urban controlled environment agriculture (CEA) is a proposed approach to mitigate the impacts of cultivation, but precise control of inputs (i.e., nutrient, water, etc.) is limited by the ability to monitor dynamic conditions. Current mechanistic and physiological plant growth models (MPMs) have not yet been unified and have uncovered knowledge gaps of the complex interplay among control variables. Moreover, because of their specificity, MPMs are of limited utility when extended to additional plant species or learning (ML) can uncover latent interactions across conditions, phenotyping bottlenecks have hindered successful application. To bridge these gaps, we propose an integrative approach whereby MPMs are used to construct the foundations of ML algorithms, reducing data requirements and costs, and ML is used to elucidate parameters and causal inference in MPM. This review highlights research about control and automation in CEA, synthesizing literature into a framework whereby ML, MPM, and biofeedback inform what we call dynamically controlled environment agriculture (DCEA). We highlight synergistic characteristics of MPM and ML to illustrate that a DCEA framework could contribute to urban resilience, human health, and optimized productivity and nutritional content.
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