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Agriculture

Volume 16

Issue 17

10.3390/agriculture16171888

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Open Access Article

A Feature Enhancement Framework for Joint Mango Fruit and Stem Detection in Complex Orchard Environments

by

Jiahuan Lu

Jiahuan Lu

SciProfilesScilitPreprints.orgGoogle Scholar

1,2,

Qihan Deng

Qihan Deng

SciProfilesScilitPreprints.orgGoogle Scholar

1,2,

Weiping Zheng

Weiping Zheng

SciProfilesScilitPreprints.orgGoogle Scholar

1,2,

Binglong Cai

Binglong Cai

SciProfilesScilitPreprints.orgGoogle Scholar

1,2,

Shan Zeng

Shan Zeng

SciProfilesScilitPreprints.orgGoogle Scholar

1,2 and

Jiehao Li

Jiehao Li

SciProfilesScilitPreprints.orgGoogle Scholar

1,2,*

1

College of Engineering, South China Agricultural University, Guangzhou 510642, China

2

National Key Laboratory of Agricultural Equipment Technology, South China Agricultural University, Guangzhou 510642, China

Author to whom correspondence should be addressed.

Agriculture2026, 16(17), 1888; https://doi.org/10.3390/agriculture16171888 (registering DOI)

Submission received: 30 July 2026 / Revised: 27 August 2026 / Accepted: 28 August 2026 / Published: 31 August 2026

(This article belongs to the Special Issue Smart Sensor-Based Systems for Crop Monitoring)

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Abstract

Reliable joint detection of mango fruits and stems is an essential upstream perception task for robotic harvesting, but remains challenging because stems are small, slender, frequently occluded, and visually degraded by illumination variation. This study proposes MangoNET, a YOLOv11n-based framework for joint mango fruit and stem detection in complex orchard environments. A P2 high-resolution detection head preserves fine spatial information for small targets, while SPPF-ELAN aggregates local and contextual features for partially visible objects. SENet recalibrates channel responses under illumination variation, and WIoU v3 regulates bounding-box samples with different localization qualities. A dataset containing 1782 original images of Tainong and Jinhuang mangoes was collected from two orchards and data augmentation was applied only to the training set, increasing its size from 1172 to 2886 images through rotation, contrast adjustment, and Gaussian noise addition. MangoNET achieved fruit and stem F1-scores of 0.920 and 0.916, respectively, with mAP50 and mAP50–95 values of 0.941 and 0.690. Compared with YOLOv11n, mAP50 and mAP50–95 increased by 1.6 and 2.9 percentage points, respectively, while stem recall increased from 0.877 to 0.906. Source-image-independent five-fold cross-validation yielded mean mAP50 and mAP50–95 values of 0.944 and 0.711, respectively. Pilot evaluations using images acquired by a UAV and an RGB-D camera in a geographically distinct orchard suggested that MangoNET could maintain detection performance in a different orchard environment. MangoNET s