Vol. 2, Issue 2, Part A (2025)

Papaya (Carica papaya L.) is a high-value tropical fruit grown in various regions across the globe. However, its yield is influenced by various factors, including soil nutrient composition, particularly the availability of zinc (Zn) and boron (B). These elements play a crucial role in the plant’s physiological processes, including photosynthesis, flowering, and fruit development. This research aims to develop a predictive model for the yield of Papaya cv. Red Lady under variable soil treatments of zinc and boron using machine learning (ML) techniques. The research employs various ML algorithms, including linear regression, decision trees, and support vector machines, to model the impact of Zn and B supplementation on papaya yield. Soil samples from experimental plots with varying levels of zinc and boron were analyzed for their effects on plant growth and yield, with machine learning models trained on data derived from these observations. The results show a strong correlation between soil nutrient levels and yield outcomes, with ML models effectively predicting papaya productivity under different treatment scenarios. The developed models provide valuable insights into optimizing nutrient management for improved papaya production. This predictive approach can assist farmers in making data-driven decisions to enhance crop yield through precise soil treatment applications. Furthermore, the findings underscore the potential of machine learning as a powerful tool in agricultural research, particularly in the optimization of crop yield under variable environmental conditions.