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Related Experiment Video

Updated: Jun 22, 2025

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

Detect and attribute the extreme maize yield losses based on spatio-temporal deep learning.

Renhai Zhong^1,2, Yue Zhu¹, Xuhui Wang³

¹College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang 310058, China.

Fundamental Research

|June 27, 2024

Summary

Deep learning accurately estimates maize yield variations and identifies extreme heat as the primary driver of yield loss, crucial for global food security.

Keywords:

Attribution analysis Crop yield estimation Deep Learning Extreme yield loss Long short-term memory Multi-task learning

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Area of Science:

Agricultural Science
Climate Science
Data Science

Background:

Accurate crop yield estimation and understanding climate stress impacts are vital for global food security.
Deep learning shows promise for yield prediction, but its ability to attribute climate extreme impacts is unclear.

Purpose of the Study:

To develop a deep learning framework for estimating maize yield variations.
To attribute yield losses to extreme climate events in the US Corn Belt.
To identify critical crop growth stages affected by climate stress.

Main Methods:

Developed a deep neural network-based multi-task learning framework.
Applied the model to county-level maize yield data from the US Corn Belt (2006-2018).
Conducted attribution analysis to determine the impact of heat, vapor pressure deficit, and precipitation.

Main Results:

The model accurately hindcasted yield variations (R² = 0.81) and extreme 2012 anomalies (R² = 0.79).
Extreme heat stress was the main cause of yield loss (72.5%), followed by vapor pressure deficit (17.6%) and precipitation (10.8%).
The silking stage was identified as most critical for yield response to climate stress in 2012.

Conclusions:

A novel spatio-temporal deep learning framework can assess and attribute crop yield responses to climate variations.
This approach is valuable for understanding and mitigating climate change impacts on agriculture.
Findings support enhanced strategies for ensuring food security in a changing climate.