Enhancing precision harvesting in smart orchards: a light-weight neural network for apple maturity detection

Abstract

Introduction Deep learning-based apple maturity detection supports precise management in smart agriculture. However, deployment on resource-constrained edge devices requires minimizing network weights while ensuring accuracy, a challenge compounded by inclement weather and dense fruit clustering in orchard environments. Methods To address these challenges, we propose HRLN-YOLO, a lightweight high-efficiency apple maturity detection model that minimizes network weights while ensuring accuracy. Specifically, we design a lightweight backbone HGBackbone to enhance feature extraction and accelerate inference, construct an enhanced neck module RCF_Neck to improve multi-scale feature fusion under occlusion, develop a lightweight detection head LADH-Head to alleviate task conflicts with minimal computational cost, and introduce NWD-Loss to improve localization stability for small-scale targets. Results Experiments on the Orchard Apple Maturity Dataset demonstrate that HRLN-YOLO improves mAP@0.5 by 1.7% over the YOLO11n baseline while reducing parameters by 37.3% and computational complexity by 34.9%. Discussion The core contribution of this study lies in minimizing network weights while ensuring detection accuracy, providing a practical solution for edge deployment in smart orchard automated harvesting.

Plant phenotyping relevance

リンゴ果実の成熟状態を推定する軽量画像認識モデルの開発と性能評価が中心であり、単なる収穫対象の位置検出ではなく、植物器官の状態を測定する方法である。

Code and data availability

The paper uses the public Orchard Apple Maturity Dataset (FigShare, Tom 2025) for its apple maturity detection experiments, but no authors' URL for the dataset, code, or trained models appears in the supplied blocks, and no allowed_url matches a paper-specific asset. The only URL present (github.com/ultralytics/ultraly

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