This work sought to overcome a pervasive challenge in yield enhancement without reliance on excessive use of fertilizers. While a number of compounds have been proposed as potential mediators of improved crop yield, the importance of pinpointing suitable compound combinations, and the respective concentration of each compound in these combinations is a key barrier towards yield enhancement. For example, the role of compound concentrations in determining which compounds should comprise optimal combinations creates prohibitively large parameter spaces that cannot be resolved through brute force, as the sheer number of experiments required may be insurmountable. To overcome this challenge, WisDM Green interrogated the interaction space from a pool of 8 compounds via an AI-discovered, second-order quadratic series that describes the correlation between compounds and their corresponding biological response (e.g. dry weight). The biomedical implications of this correlation were previously discovered in in vitro cellular response to therapeutics using neural networks \cite{Al_Shyoukh_2011}. Subsequently, this correlation was validated in multiple in vitro and in vivo studies for biomedical applications ranging from oncology to COVID-19 \cite{Clemens_2019,Lee_2017,Rashid_2018,Silva_2016,Blasiak_2020,Blasiak_2021,Abdulla_2020,Ding_2019,Khong_2020}. The optimization of treatment outcomes using the second-order quadratic series was further confirmed in prospective human studies \cite{Kee_2019,Pantuck_2018,Zarrinpar_2016,de_Mel_2020}. Due to the broadly demonstrated effectiveness of this approach towards mediating optimal outcomes in living systems, this study sought to apply this approach for multi-compound prioritization towards positive yield outcomes. It should be noted that previous studies have examined the role of a quadratic model towards optimizing drug combinations to achieve optimal clinical outcomes. However, this current study aimed to bridge the multi-compound design input with plant yield output. In addition, this current study has harnessed WisDM Green and associated drug development-centric approaches to pinpoint unforeseen concentration-dependent compound interactions that may actionably mediate yield improvement with a simultaneous reduction in the concentrations of certain compounds towards sustainable implementation of this approach. Importantly, WisDM Green differs from traditional AI-based approaches as it does not utilize any pre-existing compound information, big data, or in silico modeling. Instead, WisDM Green harnesses experimentally obtained data (e.g. biological yield or dry weight) to determine suitable compound combinations and their respective concentration ratios via prospective validation studies. Furthermore, WisDM Green also differs from the response surface methdology (RSM), which has been used to modulate input variables (e.g. magnetic field, minerals) to improve growth and yield in plants \cite{Iqbal_2013,poothong2020}. In this study, WisDM Green simultaneously interrogated the interaction space of multiple compounds at various concentration ranges, pinpointing effective combinations based on experimentally-detected compound interactions. However, RSM only assesses 2 input variables at a time to determine the response, or interaction, of the input factors (e.g. magnetic field). Nonetheless, RSM has laid important foundations for paired interactions in farming applications.