Flavor-cyber-agriculture: Optimization of plant metabolites in an open-source control environment through surrogate modeling

PLoS One. 2019 Apr 3;14(4):e0213918. doi: 10.1371/journal.pone.0213918. eCollection 2019.

Abstract

Food production in conventional agriculture faces numerous challenges such as reducing waste, meeting demand, maintaining flavor, and providing nutrition. Contained environments under artificial climate control, or cyber-agriculture, could in principle be used to meet many of these challenges. Through such environments, phenotypic expression of the plant-mass, edible yield, flavor, and nutrients-can be actuated through a "climate recipe," where light, water, nutrients, temperature, and other climate and ecological variables are optimized to achieve a desired result. This paper describes a method for doing this optimization for the desired result of flavor by combining cyber-agriculture, metabolomic phenotype (chemotype) measurements, and machine learning. In a pilot experiment, (1) environmental conditions, i.e. photoperiod and ultraviolet (UV) light (known to affect production of flavor-active molecules in edible plants) were applied under different regimes to basil plants (Ocimum basilicum) growing inside a hydroponic farm with an open-source design; (2) flavor-active volatile molecules were measured in each plant using gas chromatography-mass spectrometry (GC-MS); and (3) symbolic regression was used to construct a surrogate model of this chemistry from the input environmental variables, and this model was used to discover new combinations of photoperiod and UV light to increase this chemistry. These new combinations, or climate recipes, were then implemented in the hydroponic farm, and several of them resulted in a marked increase in volatiles over control. The process also led to two important insights: it demonstrated a "dilution effect", i.e. a negative correlation between weight and desirable chemical species, and it discovered the surprising effect that a 24-hour photoperiod of photosynthetic-active radiation, the equivalent of all-day light, induces the most flavor molecule production in basil. In this manner, surrogate optimization through machine learning can be used to discover effective recipes for cyber-agriculture that would be difficult and time-consuming to find using hand-designed experiments.

Publication types

  • Research Support, Non-U.S. Gov't
  • Retracted Publication

MeSH terms

  • Agriculture / methods*
  • Cybernetics / methods*
  • Environment, Controlled*
  • Machine Learning
  • Metabolomics
  • Ocimum basilicum / metabolism*
  • Pilot Projects
  • Plant Leaves / metabolism*
  • Research Design
  • Volatile Organic Compounds / metabolism

Substances

  • Volatile Organic Compounds

Grants and funding

CBH, AJJ, and TLS received support in the form of a portion of their salary from commercial funders of this study: Target Corporation, Lee Kum Kee Health Products Group, and Welspun. However, these funders did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. RM and EM received support from Sentient Technologies (https://www.sentient.ai/) and Technology Solutions (https://cognizant.com/). RM and EM first had an affiliation with commercial funder Sentient Technologies and later with Cognizant Technology Solutions. These funders provided support in the form of salary for RM and EM but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.