30Food production in conventional agriculture faces numerous challenges such as reducing 31 waste, meeting demand, maintaining flavor, and providing nutrition. Contained environments 32 under artificial climate control, or cyber-agriculture, could in principle be used to meet many of 33 these challenges. Through such environments, phenotypic expression of the plant---mass, edible 34 yield, flavor, and nutrients---can be actuated through a "climate recipe," where light, water, 35 nutrients, temperature, and other climate and ecological variables are optimized to achieve a 36 desired result. This paper describes a method for doing this optimization for the desired result of 37 flavor by combining cyber-agriculture, metabolomic phenotype (chemotype) measurements, and 38 machine learning. In a pilot experiment, (1) environmental conditions, i.e. photoperiod and 39 ultraviolet (UV) light (known to affect production of flavor-active molecules in edible plants) 40 were applied under different regimes to basil plants (Ocimum basilicum) growing inside a 41 hydroponic farm with an open-source design; (2) flavor-active volatile molecules were 42 measured in each plant using gas chromatography-mass spectrometry (GC-MS); and (3) 43 symbolic regression was used to construct a surrogate model of this chemistry from the input 44 environmental variables, and this model was used to discover new combinations of photoperiod 45 and UV light to increase this chemistry. These new combinations, or climate recipes, were then 46 implemented in the hydroponic farm, and several of them resulted in a marked increase in 47 volatiles over control. The process also led to two important insights: it demonstrated a "dilution 48 effect", i.e. a negative correlation between weight and desirable chemical species, and it 49 discovered the surprising effect that a 24-hour photoperiod of photosynthetic-active radiation, the 50 equivalent of all-day light, induces the most flavor molecule production in basil. In this manner, 51 surrogate optimization through machine learning can be used to discover effective recipes for 52 3 cyber-agriculture that would be difficult and time-consuming to find using hand-designed 53 experiments. 54 55 1 Introduction 56 The so-called "dilution effect," noted since the 1940's and systematically reviewed since 57 the early 1980's [1], describes an inverse relationship between yield and nutrient concentration in 58 food: For many nutritionally-important chemical constituents of food plants, such as minerals, 59 protein, and vitamins, an increase in biomass is accompanied by a decrease in nutrient 60 concentration. This effect has been systematically demonstrated in historical nutrient content 61 studies over the last 50-70 years [2,3], as well as in controlled side-by-side trials that have shown 62 a relationship between nutrient dilution and genetics [4], artificial fertilization [5], and elevated 63 carbon dioxide levels related to climate change [6,7]. Flavor, known to be an important element 64 of food and of ea...
