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Related Experiment Video

Updated: Apr 30, 2026

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

Research progress on precision orchard yield estimation based on multi-source information perception sensors.

Zifan Rong¹, Yu Ru¹, Xiao Zhang¹

¹College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China.

Frontiers in Plant Science

|April 29, 2026

Summary

Accurate orchard yield estimation is crucial for precision agriculture. This review highlights machine vision and remote sensing as effective methods, with data fusion enhancing accuracy for better orchard management.

Keywords:

crop monitoring data heterogeneity deep learning precision agriculture yield mapping

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Area of Science:

Agricultural Science
Computer Vision
Remote Sensing

Background:

Accurate orchard yield data is vital for economic assessment and management optimization.
Traditional manual yield estimation methods are labor-intensive and often inaccurate for precision agriculture.

Purpose of the Study:

To systematically review and compare machine vision, remote sensing, and multi-source data fusion methods for orchard yield estimation.
To identify current challenges and future directions in automated orchard yield estimation.

Main Methods:

Systematic literature review following PRISMA guidelines.
Comparison of methodologies and applications of machine vision, remote sensing, and data fusion techniques.
Analysis of existing studies on automated orchard yield estimation.

Main Results:

Machine vision and remote sensing effectively support automated orchard yield estimation.
Multi-source heterogeneous data fusion improves robustness and accuracy by integrating fruit detection with canopy traits.
Current methods show promise but face challenges like fruit occlusion and limited adaptability.

Conclusions:

Advanced algorithm integration and multi-modal data fusion are key for future progress.
Development of intelligent, automated yield-estimation platforms is needed for diverse orchard environments.
Addressing challenges like data heterogeneity and fruit occlusion is critical for widespread adoption.