A Lightweight Shape-Aware YOLO Network for Field Strawberry Maturity Detection Under Complex Orchard Conditions

Abstract

Precise, non-destructive detection of fruit maturity is a cornerstone of modern precision agriculture, directly impacting harvest scheduling and post-harvest quality control. In the case of strawberries (Fragaria × ananassa), in-field automated assessment is persistently hampered by the fruit’s diminutive size, subtle physiological colour transitions, and frequent occlusion by foliage. To overcome these limitations, we developed SMLO-YOLO, a specialised lightweight vision system designed to reliably detect different maturity stages on edge devices under complex orchard conditions. The proposed architecture incorporates a Cross-Scale Aggregation Neck (HDP-Neck) driven by entropy-guided dynamic sampling, which effectively concentrates computational resources on fruit regions while filtering background noise. Additionally, we introduce a Shape-aware Intersection-over-Union (ShapeIoU) loss and a Boundary- and Class-aware Knowledge Distillation (BCKD) strategy to specifically address the challenge of detecting overlapping clusters and low-maturity fruits. Validation on custom datasets collected from commercial orchards in Sichuan and Shanxi demonstrated that the final SMLO-YOLO model, after BCKDloss-based knowledge distillation, achieved an mAP50 of 92.4% at an inference speed of 256.41 FPS, with 6.49 M parameters and 15.0 GFLOPs. These metrics indicate that the system successfully balances high-throughput detection with the non-harvestable low-maturity fruits of agricultural robotics, offering a robust tool for objective, real-time maturity monitoring.

Plant phenotyping relevance

イチゴ果実の成熟段階という植物器官の状態を画像から推定する軽量YOLO手法を開発し、実圃場データで性能検証しており、フェノタイピング手法が研究の中心です。

Code and data availability

The supplied blocks describe a custom strawberry maturity dataset (1075 images from Sichuan/Shanxi orchards) and the SMLO-YOLO model, but contain no public deposit, availability statement, or authors' URL for the dataset, images, code, or trained weights. The dataset is described as 'custom' with no repository or data-

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