Deep learning-based semantic segmentation for rice yield estimation by analyzing the dynamic change of panicle coverage.

ABSTRACT:

Introduction

Rising global populations and climate change necessitate increased agricultural productivity. Most studies on rice panicle detection using imaging technologies rely on single-time-point analyses, failing to capture the dynamic changes in panicle coverage and their effects on yield. Therefore, this study presents a novel temporal framework for rice phenotyping and yield prediction by integrating high-resolution RGB imagery with deep learning-based semantic segmentation.

Methods

High-resolution RGB images of rice canopies were acquired over two growing seasons. We evaluated five semantic segmentation models (DeepLabv3+, U-Net, PSPNet, FPN, LinkNet) to effectively delineate rice panicles. Time-series panicle coverage data, extracted from the segmented images, were

SUBMITTER: Bak HJ 

PROVIDER: S-EPMC12390994 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature