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AI-POWERED MODEL ENHANCES RICE LODGING DETECTION FOR IMPROVED AGRICULTURAL
OUTCOMES

by Chinese Academy of Sciences

The geographic location of the experimental site. Credit: Plant Phenomics
(2024). DOI: 10.34133/plantphenomics.0182

By leveraging advanced convolutional neural network (CNN) architecture and
intelligent optimization algorithms, an AI-powered model significantly surpasses
conventional techniques, offering enhanced accuracy and reduced computational
costs.



Rice lodging, the bending or falling of crops caused by environmental factors
like wind or rain, poses a substantial threat to crop productivity. It hinders
photosynthesis, complicates harvesting, and increases vulnerability to pests,
making it crucial for farmers and researchers to monitor and predict lodging
effectively.

Traditional methods, including visual inspection, mathematical modeling, and
satellite remote sensing, are often labor-intensive and imprecise, lacking the
scalability and immediacy required for large-scale agricultural assessment.

A study published in Plant Phenomics can guide timely remedial actions, such as
adjusting irrigation or pest control strategies, to mitigate potential yield
losses.

The AAUConvNeXt model, developed through multi-objective optimization using the
AFOA-APM algorithm, offers an enhanced version of the UConvNeXt CNN architecture
for segmenting rice lodging. The research method involved optimizing the number
of channels in the model's convolutional layers to improve performance and
efficiency.

Unlike the conventional approach where channels increase or decrease in a fixed
pattern, the AAUConvNeXt model strategically adjusts channels, increasing them
in layers that require high feature learning while reducing them in less
critical layers to balance complexity and resource use.



The results from extensive experiments highlight the superiority of AAUConvNeXt
over existing models. The optimized architecture achieved a Pixel Accuracy (PA)
of 96.3%, Mean Pixel Accuracy (MPA) of 96.3%, and a mean Intersection over Union
(mIoU) of 93.2%, outperforming other models like DeepLabV3+ and HRNet.

Additionally, AAUConvNeXt reduced parameter count and computational complexity
by 8.66%, making it more resource-efficient.

The model's advanced feature extraction capabilities contributed to high
segmentation accuracy, especially in distinguishing challenging rice lodging
categories, including full, partial, and non-lodged states.

Ablation studies confirmed that combining AFOA with APOM significantly improved
segmentation metrics, with AAUConvNeXt outperforming its predecessors.
Furthermore, targeted channel adjustments optimized model complexity, allowing
efficient learning of both early-stage and refined features.

(A and B) The U-Net architecture with ConvNeXt blocks. Credit: Plant Phenomics
(2024). DOI: 10.34133/plantphenomics.0182

According to the study's senior researcher, Dr. Xiaobo Sun, "By integrating deep
learning with intelligent optimization, our model provides a powerful tool for
efficient crop lodging monitoring. This advancement holds immense potential to
transform rice farming practices by offering timely, reliable, and
cost-effective solutions."

The AAUConvNeXt model represents a significant advancement in agricultural
technology, combining deep learning with intelligent optimization for efficient
rice lodging monitoring. Its integration into farming practices could
revolutionize crop management, offering a promising pathway to improved
productivity and sustainability.

More information: Panli Zhang et al, AAUConvNeXt: Enhancing Crop Lodging
Segmentation with Optimized Deep Learning Architectures, Plant Phenomics (2024).
DOI: 10.34133/plantphenomics.0182

Provided by Chinese Academy of Sciences

Citation: AI-powered model enhances rice lodging detection for improved
agricultural outcomes (2024, November 11) retrieved 11 November 2024 from
https://phys.org/news/2024-11-ai-powered-rice-lodging-agricultural.html
This document is subject to copyright. Apart from any fair dealing for the
purpose of private study or research, no part may be reproduced without the
written permission. The content is provided for information purposes only.

--------------------------------------------------------------------------------

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