Predicting Nutritional and Morphological Attributes of Fresh Commercial Opuntia Cladodes Using Machine Learning and Imaging

Opuntia ficus-indica L. is a prominent crop in Mexico, requiring advanced non-destructive technologies for the real-time monitoring and quality control of fresh commercial cladodes. The primary research objective of this study was to develop and validate high-precision mathematical models that correlate hyperspectral signatures (400–1000 nm) with the specific nutritional, morphological, and antioxidant attributes of fresh cladodes (cultivar Villanueva) at their peak commercial maturity. By combining hyperspectral imaging (HSI) with machine learning algorithms, including K-Means clustering for image preprocessing and Partial Least Squares Regression (PLSR) for predictive modeling, this study successfully predicted the concentrations of 10 minerals (N, P, K, Ca, Mg, Fe, B, Mn, Zn, and Cu), chlorophylls (a, b, and Total), and antioxidant capacities (ABTS, FRAP, and DPPH). The innovative nature of this work lies in the simultaneous non-destructive quantification of 17 distinct variables from a single scan, achieving coefficients of determination (R2) as high as 0.988 for Phosphorus and Chlorophyll b. The practical applicability of this research provides a viable replacement for time-consuming and destructive laboratory acid digestion, enabling producers to implement automated, high-throughput sorting lines for quality assurance. Furthermore, this study establishes a framework for interdisciplinary collaborations between agricultural engineers, data scientists for algorithm optimization, and food scientists to enhance the functional value chain of Opuntia products.