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Light Acquisition02:16

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A Precise Framework for Rice Leaf Disease Image-Text Retrieval Using FHTW-Net.

Hongliang Zhou1, Yufan Hu1, Shuai Liu1

  • 1College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, Hunan, China.

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This study introduces FHTW-Net, a novel framework for cross-modal rice leaf disease retrieval, enhancing agricultural decision support. The model significantly improves accuracy in identifying diseases from images and text descriptions, safeguarding rice production.

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Cross-modal retrieval is vital for agricultural decision support in disease prevention.
  • Existing frameworks for crop leaf disease retrieval have limitations.
  • Accurate identification of rice leaf diseases is crucial for safeguarding global food production.

Purpose of the Study:

  • To introduce cross-modal retrieval to rice leaf disease identification.
  • To develop a novel framework, FHTW-Net, for rice leaf disease image-text retrieval.
  • To establish the first cross-modal rice leaf disease retrieval dataset (CRLDRD).

Main Methods:

  • Utilized Vision Transformer (ViT) and BERT for fine-grained image and text feature extraction.
  • Introduced two-way mixed self-attention (TMS) to enhance feature sequences and uncover semantic information.
  • Implemented a false-negative elimination-hard negative mining (FNE-HNM) strategy and warm-up bat algorithm (WBA) for model optimization.

Main Results:

  • FHTW-Net demonstrated superior performance compared to state-of-the-art models.
  • Achieved high accuracies in image-to-text retrieval (R@1: 83.5%, R@5: 92%, R@10: 94%).
  • Achieved high accuracies in text-to-image retrieval (R@1: 82.5%, R@5: 98%, R@10: 98.5%).

Conclusions:

  • FHTW-Net provides effective technical support and algorithmic guidance for cross-modal rice leaf disease retrieval.
  • The developed dataset and framework advance the field of agricultural disease identification.
  • This research contributes to data-driven decision support for disease threat management in rice production.