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Light Acquisition

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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 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.

Plant Phenomics (Washington, D.C.)

|April 26, 2024

Summary

This summary is machine-generated.

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.