Class-wise guided weighted soft voting for deep learning-based date palm nutrient deficiency classification

Abstract

Diagnosing nutrient deficiencies in date palm (Phoenix dactylifera L.) is challenging due to the high visual similarity of symptoms, such as between Magnesium and Potassium deficiency, making classic subjective methods unreliable. While automated deep learning models offer an alternative, the reliability of individual models is a key concern; a statistical evaluation over five independent runs confirmed that while a strong model like ConvNeXtTiny can establish a near-perfect performance ceiling (macro F1-score of 0.9969 ± 0.0028), weaker architectures like MobileNetV2 are highly unstable and less accurate (macro F1-score of 0.9219 ± 0.0486), posing a significant risk for reliable deployment. To mitigate this unreliability, we proposed and evaluated a Class-wise Guided Weighted Soft Voting (CG-WSV) ensemble heuristic. The empirical results establish a new, statistically robust performance benchmark, with the proposed CG-WSV ensemble achieving a high-performance macro F1-score of 0.9971 ± 0.0027. This performance matched that of Unweighted and Globally Weighted Soft Voting baselines, demonstrated a 0.33% relative improvement over Hard Voting, and represented a significant relative increase of 8.16% over the unstable MobileNetV2 model. The gains over the stronger individual models were 0.54% (vs. EfficientNetB0) and 0.02% (vs. ConvNeXtTiny), confirming its ability to match the observed performance ceiling. Critically, all soft voting ensembles, including CG-WSV, demonstrated exceptional stability by completely mitigating the high variance of the weaker model, validating it as a robust strategy for ensuring reliable diagnostic accuracy and providing a definitive statistical benchmark for this agricultural diagnostic problem.