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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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MIX-NET: Deep Learning-Based Point Cloud Processing Method for Segmentation and Occlusion Leaf Restoration of

Binbin Han^1,2,3,4,5, Yaqin Li⁵, Zhilong Bie^6,7

¹College of Engineering, Huazhong Agricultural University, Wuhan 430070, China.

Plants (Basel, Switzerland)

|December 11, 2022

Summary

This study introduces a new framework for precise melon seedling leaf area measurement using point cloud data. The method enhances point cloud quality and integrates segmentation and completion for improved accuracy.

Keywords:

MIX-Net deep learning leaf area measurement point cloud completion point cloud segmentation seedlings

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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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Area of Science:

Agricultural Technology
Computer Vision
Biotechnology

Background:

Accurate leaf area measurement is crucial for plant growth monitoring and agricultural management.
Existing point cloud processing methods face challenges with noise and integrated segmentation/completion tasks.

Purpose of the Study:

To develop a novel point cloud segmentation and completion framework for high-quality leaf area measurement of melon seedlings.
To enhance the accuracy and efficiency of point cloud data processing for agricultural applications.

Main Methods:

A neighborhood space-constrained method was developed to filter noise and improve point cloud data quality.
A novel network, MIX-Net, utilizing a purely linear mixer mechanism, was designed for simultaneous point cloud segmentation and completion.
Point cloud data was acquired using an Azure Kinect camera from a top-view perspective of melon seedlings.

Main Results:

The proposed method significantly enhances point cloud quality by filtering noise and outlier points.
MIX-Net achieved superior performance in seedling segmentation, outperforming PointNet++ by 3.1% and DGCNN by 1.7%.
Leaf area measurement accuracy improved, with R2 increasing from 0.87 to 0.93 and Mean Squared Error (MSE) decreasing from 2.64 to 2.26 after leaf shading completion.

Conclusions:

The novel framework effectively addresses challenges in point cloud segmentation and completion for agricultural measurements.
The integrated approach of MIX-Net offers a more definite and effective solution compared to methods separating these tasks.
The proposed method demonstrates significant potential for improving the precision and reliability of leaf area measurements in precision agriculture.