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Sugarcane stem node detection and localization for cutting using deep learning.

Weiwei Wang^1,2, Cheng Li¹, Kui Wang^1,2

¹School of Engineering, Anhui Agricultural University, Hefei, China.

Frontiers in Plant Science

|December 29, 2022

Summary

This study introduces an improved YOLOv4-Tiny algorithm for precise sugarcane stem node recognition, enhancing seed cutting accuracy and efficiency. The developed intelligent cutting machine significantly reduces labor intensity in sugarcane farming.

Keywords:

cutting system enhanced YOLOv4-Tiny identification system sugarcane seed cutting sugarcane stem node

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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Area of Science:

Agricultural Engineering
Computer Vision
Machine Learning

Background:

Optimizing sugarcane planting requires accurate identification and cutting of stem nodes for quality seed production.
Traditional methods are labor-intensive and can lack precision, impacting yield.

Purpose of the Study:

To develop an intelligent system for accurate and fast identification and cutting of sugarcane stem nodes.
To improve seed cutting quality and efficiency while reducing labor intensity in sugarcane farming.

Main Methods:

An enhanced YOLOv4-Tiny algorithm was developed, incorporating Spatial Pyramid Pooling (SPP) modules and 1x1 convolution modules.
The modifications aimed to fuse local and global features, improve localization accuracy, reduce parameters, and increase prediction speed.

Main Results:

The improved algorithm achieved a mean average precision (MAP) of 99.11% and a detection accuracy of 97.07% on a sugarcane dataset.
The system demonstrated a processing speed of 30 frames per second (fps), enabling real-time identification and cutting.

Conclusions:

The developed intelligent system accurately and rapidly identifies and cuts sugarcane stem nodes in real-time.
This technology enhances seed cutting quality and efficiency, significantly reducing manual labor in sugarcane cultivation.