Disambiguation of multiple nutrient deficiency stresses in coconut using compositional nutrient diagnostic norms powered by machine learning algorithms.

Crosscutting technologies to resolve several stress factors should be exploited to address multiple nutrient deficiencies in standing crops. Executing translational research is vital for the long-term sustenance of soil‒plant‒human interrelationships. Constructing compositional nutrient diagnosis (CND) norms powered by machine learning algorithms as a high-fidelity standard to disambiguate multiple nutrient deficiency stresses was the objective of the research. Compositional nutrient diagnosis norms can be used as the diagnostic standard to disambiguate multiple nutrient deficiencies in West Coast Tall (WCT) variety of coconut. CND technique was adopted to abridge the complicated task of disambiguating multiple nutrient deficiencies and their interconnection with biotic stresses. The data from 120 coconut fields were integrated into a comprehensive database to devise diagnostic standards. Nutrient indices derived from independent samples identified Mg as the primary limiting nutrient, followed by K, while deficiencies of P, S, B, and Zn occurred in a subset of palms. The CND approach provides a decisive diagnostic framework by resolving the ambiguity associated with multiple, co-occurring nutrient stresses, enabling simultaneous identification of the most critical limiting nutrient in standing crops and hierarchical ranking of nutrient constraints according to the magnitude of relative imbalance. The CND norms could serve as a valuable diagnostic tool to reconcile multiple deficiencies in coconut, which may help in developing nutrient management plans. It has the capacity to address various nutrient deficiencies in coconuts and has the relative advantage of operating a small, robust, and compact database to facilitate an inclusive approach to detect nutrient disproportions.