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ACSE: an efficient deep learning model for wheat disease identification.

Xiaoyan Yan¹, Junqi Dong¹, Longguo Wu²

¹School of Computer and Artificial Intelligence, Henan University of Urban Construction, Pingdingshan, China.

Frontiers in Plant Science

|July 13, 2026

Summary

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Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

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A new deep learning model, ACSE, accurately identifies wheat diseases with 98.51% accuracy. This advancement aids in efficient disease diagnosis, improving wheat production and reducing pesticide use.

Area of Science:

Agricultural Science
Computer Science
Plant Pathology

Background:

Wheat is a vital global crop, but diseases significantly impact its yield and quality.
Accurate and timely wheat disease diagnosis is crucial for effective management and production.

Purpose of the Study:

To develop a novel deep learning model for accurate wheat disease recognition.
To enhance disease feature extraction and model robustness for improved diagnostic capabilities.

Main Methods:

An improved AlexNet architecture was utilized as the base model.
A convolutional block attention module (CBAM) was integrated for richer texture feature extraction.
A squeeze-and-excitation (SE) network was incorporated to boost predictive ability and robustness.

Keywords:

deep learning dual attention mechanism robustness smart agriculture wheat leaf disease

Related Experiment Videos

Main Results:

The proposed ACSE model achieved a high recognition accuracy of 98.51%.
ACSE outperformed other deep learning models (MobileNetV2, DenseNet, ShuffleNetV1) in precision, adaptability, and generalization.
The model demonstrated a lower misjudgment rate and a smaller parameter count.

Conclusions:

The ACSE model offers a promising tool for intelligent agricultural systems, enabling efficient wheat disease diagnosis.
This technology can help reduce pesticide abuse and ensure the safety and quality of wheat production.