Integrating evolutionary algorithms and enhanced-YOLOv8 + for comprehensive apple ripeness prediction.

Abstract

The assessment of apple quality is pivotal in agricultural production management, and apple ripeness is a key determinant of apple quality. This paper proposes an approach for assessing apple ripeness from both structured and unstructured observation data, i.e., text and images. For structured text data, support vector regression (SVR) models optimized using the Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Sparrow Search Algorithm (SSA) were utilized to predict apple ripeness, with the WOA-optimized SVR demonstrating exceptional generalization capabilities. For unstructured image data, an Enhanced-YOLOv8+, a modified YOLOv8 architecture integrating Detect Efficient Head (DEH) and Efficient Channel Attention (ECA) mechanism, was employed for precise apple localization and ripeness identification. The synergistic application of these methods resulted in a significant improvement in prediction accuracy. These approaches provide a robust framework for apple quality assessment and deepen the understanding of the relationship between apple maturity and observed indicators, facilitating more informed decision-making in postharvest management.

Plant phenotyping relevance

画像と最適化アルゴリズムを用いてリンゴ果実の成熟度を推定する手法が中心であり、果実の状態を直接評価する植物フェノタイピングとして適格。

Code and data availability

The paper uses two Kaggle-hosted datasets (structured apple quality text data and 650 orchard ripeness images), but the Data availability statement only points to the generic Kaggle homepage without any dataset name, identifier, or authors' repository URL. No author code, trained models, or paper-specific public asset,

No evidence-backed public reproduction asset is currently recorded.