A Comparative Analysis of Classical Biometrics and AI‐Driven Models for Yield Prediction in Brinjal ( Solanum melongena L.)

Optimizing yield stability in brinjal ( Solanum melongena L.) remains a challenge, as traditional biometric methods struggle to capture the complex, nonlinear trait interactions governing productivity. This study evaluates the biological information gap inherent in traditional biometrics by establishing a benchmarking framework that compares classical correlation and path analysis against high‐resolution predictive modeling and neural networks. Nineteen brinjal genotypes were evaluated for thirteen quantitative traits over two consecutive years. Numerical evidence showed that while classical path analysis was constrained by a high residual effect (0.383), the AI‐driven artificial neural network (ANN) model successfully recovered 37.3% of the unexplained variance, achieving superior predictive accuracy ( R ²  = 0.92). Principal component analysis (PCA) identified specific reproductive strategies, with the first two components explaining 60.79% of the phenotypic variation. Multiple linear regression (MLR) identified the number of fruits per plant, 100‐seed weight, fruit weight, and leaf area as the key linear factors affecting yield, with 100‐seed weight exerting the maximum influence. Additionally, a multilayer perceptron (MLP) model captured the nonlinear influence of these traits, demonstrating an absolute physiological tipping point where disproportionate yield leaps occur above 14 fruits and weights near 200 g. To translate this into broader actionable field metrics, the classification and regression trees (CART) algorithm established an elite threshold, identifying that selecting for individual fruit weights strictly between 193.6 and 204.7 g captures the absolute maximum of this high‐yield potential. This benchmarking resolves the inherent predictive limitations of traditional modeling, enhancing breeding precision for targeted genetic gains aligned with physiological potential and agronomic productivity.