Adaptive CNN Ensemble for Apple Detection: Enabling Sustainable Monitoring Orchard

Abstract

Accurate detection of apples in orchards under variable weather and illumination remains a key challenge for precision horticulture. This study presents a flexible framework for automated ensemble selection and optimization of convolutional neural network (CNN) inference. The system integrates eleven ensemble methods, dynamically configured via Pareto-based multi-objective optimization balancing accuracy (mAP, F1-Score) and performance (FPS). A key innovation is its pre-deployment benchmarking whereby models are evaluated on a representative field sample to recommend a single optimal model or lightweight ensemble for real-time use. Experimental results show ensemble models consistently outperform individual detectors, achieving a 7–12% improvement in accuracy in complex scenes with occlusions and motion blur, underscoring the approach’s value for sustainable orchard management.