Recent research strategies have applied advanced technologies including artificial intelligence (AI) and automation towards agriculture, especially crops, in attempt to achieve an equilibrium between sufficient food supply and food production sustainability. For example, controlled-release fertilization strategies have resulted in reductions in greenhouse gas emission and nitrogen leaching without compromising the overall yield \cite{Li_2018,Sikora_2020,Xiao_2019,ul_Haq_2020,Wang_2020}. Furthermore, multiple studies have harnessed AI to achieve precision agriculture, crop yield prediction, and decision support in agriculture and supply chain management \cite{Zhang_2021,Kouadio_2018,Jung_2021,Basso_2020,Kim_2019,Waleed_2020,Geethanjali_2020,Ghasemi_Varnamkhasti_2019}. These approaches have demonstrated the feasibility and potential benefit from intersecting agriculture and AI and thus, provide a new avenue towards digitized, sustainable farming. Proposing an alternative approach to sustainably achieve improved crop yield, we report the application of an optimization platform, termed WisDM Green, to simultaneously pinpoint suitable compound combinations (e.g. biostimulants) in peat moss and pinpoint their concentration ratios that can mediate positive effects on the yield of Amaranthus cruentus (red spinach), which was used for experimental validation in this proof-of-concept study. Red spinach was selected for this study due to its short growing season, manageable growing conditions and importantly, its rising popularity in healthy diets.