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Related Experiment Video

Updated: May 17, 2025

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

Comparison of three algorithms for estimating crop model parameters based on multi-source data: A case study using

Yonghui Zhang¹, Yujie Zhang², Haiyan Jiang³

¹School of Computer Engineering, Weifang University, Weifang, People's Republic of China.

|May 14, 2025

Summary

Accurate soybean phenology prediction is crucial for crop management. Modified Non-dominated Sorting Genetic Algorithm (MNSGA-II) showed a slight advantage in calibrating crop model parameters compared to Generalized Likelihood Uncertainty Estimation (GLUE) and Differential Evolution (DE).

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Last Updated: May 17, 2025

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High-Throughput Analysis of Non-Photochemical Quenching in Crops Using Pulse Amplitude Modulated Chlorophyll Fluorometry

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A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

Area of Science:

Agricultural Science
Computational Biology
Environmental Science

Background:

Accurate crop phenological stage prediction is vital for effective agricultural management and understanding crop responses to environmental shifts.
Soybean phenology modeling requires precise calibration of cultivar-specific parameters (CSPs) for reliable simulation.

Purpose of the Study:

To compare the performance of Modified Non-dominated Sorting Genetic Algorithm (MNSGA-II), Generalized Likelihood Uncertainty Estimation (GLUE), and Differential Evolution (DE) in calibrating CSPs for the CROPGRO-Soybean phenological model.
To evaluate the accuracy and stability of different algorithms in simulating soybean phenology using multi-source datasets.

Main Methods:

Calibrated CROPGRO-Soybean phenological model parameters using MNSGA-II, GLUE, and DE algorithms.
Utilized multi-site, multi-year, and multi-cultivar soybean datasets for calibration.
Validated the calibrated model using independent experimental data and evaluated performance using RMSE, MAE, and R2 metrics.

Main Results:

MNSGA-II, GLUE, and DE showed comparable simulation accuracy, with RMSEs of 4.28, 4.76, and 5.17 days, respectively.
MNSGA-II demonstrated a slight advantage in calibration effectiveness.
GLUE exhibited the highest stability across repeated calibration runs.

Conclusions:

MNSGA-II is a suitable algorithm for crop model parameter estimation.
The choice of algorithm for calibrating crop model parameters should align with specific project requirements.
Results offer guidance for selecting appropriate algorithms for crop model parameter estimation.