YOLOv12-based Detection and Yield Estimation for Persimmon Orchards: A Multi-scale, Field-Validated Pipeline for Precision Harvesting

Articles in Press

Document Type : Research Article- En

Authors

H. Nikkhah

A. Hosseinpour

Department of Biosystems Mechanical Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran

https://doi.org/10.22067/jam.2026.96324.1444

Abstract

Accurate pre-harvest yield estimation is essential in precision horticulture, enabling informed decisions in labour allocation, logistics, and market forecasting. This study presents an integrated computer vision framework based on the YOLOv12 architecture for accurate detection and quantification of persimmon (Diospyros kaki) fruits under real orchard conditions. Multi-scale feature extraction was combined with lightweight regression modelling to predict individual fruit weights directly from RGB imagery. A dedicated dataset of 2,118 annotated images was compiled to capture variations in canopy density and illumination, ensuring robust performance under natural occlusions. After fine-tuning five YOLOv12 variants, detection precision exceeded 0.92 mAP@0.5, with the YOLOv12x configuration achieving the highest accuracy of 0.945. However, the YOLOv12n variant provided the best trade-off between accuracy (0.925 mAP@0.5) and efficiency (5.7 ms inference, >100 FPS), making it optimal for IoT-based real-time deployment in smart orchard environments. Yield estimation was derived by mapping ellipsoidal volume approximations to actual fruit weights using linear regression, achieving a mean absolute error of 6.2 g per fruit and an average tree-level deviation below 5%. The framework maintained real-time inference speeds on edge devices, confirming its suitability for practical field applications. The results demonstrate its reliability for data-driven yield forecasting and highlight its potential integration into IoT-enabled harvesting systems to minimise post-harvest losses through predictive resource allocation.

Keywords

Attention mechanisms

Field validation

Multi-scale object detection

Orchard robotics

Precision agriculture

Subjects

• Digital, Smart, and Precision Agriculture

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Authors retain the copyright. This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

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