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Updated: Jun 6, 2025

An Effective Inoculation Method for Phytophthora capsici on Black Pepper Plants

An Effective Inoculation Method for Phytophthora capsici on Black Pepper Plants

Published on: September 16, 2022

A multimodal framework for pepper diseases and pests detection.

Jun Liu¹, Xuewei Wang²

¹Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Weifang, China. liu_jun860116@wfust.edu.cn.

Scientific Reports

|November 23, 2024

Summary

This summary is machine-generated.

A new PepperNet model accurately detects pepper diseases and pests in complex images using natural language descriptions. This advanced object detection method achieves high accuracy and speed, even with challenging noise and occlusion.

Keywords:

Multimodal Natural Language Object detection Pepper diseases and pests image Visual features

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Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Pepper diseases and pests present detection challenges due to small size, varied shapes, and complex backgrounds.
Existing object detection methods struggle with diverse targets and lack noise suppression in agricultural imaging.

Purpose of the Study:

To develop a robust object detection model for pepper diseases and pests in complex scenarios.
To create the first multimodal pepper diseases and pests object detection dataset (PDD) with natural language descriptions.

Main Methods:

Proposed the PepperNet model, which decomposes multimodal features into explicit attributes.
Employed fine-grained multimodal attribute contrast learning for distinguishing subtle differences.
Utilized a novel dataset (PDD) integrating images and detailed textual descriptions.

Main Results:

Achieved a mean Average Precision (mAP@0.5) of 91.93% for object detection.
Reached a high detection speed of 121.8 frames per second.
Demonstrated model robustness against noise and occlusion through visualizations.

Conclusions:

PepperNet offers superior performance and stability for detecting pepper diseases and pests in complex, real-world conditions.
The fine-grained multimodal attribute learning effectively maps language to vision for precise identification.
The PDD dataset and PepperNet model advance automated pest and disease detection in agriculture.