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Using deep learning to identify maturity and 3D distance in pineapple fields.

Chia-Ying Chang¹, Ching-Shan Kuan², Hsin-Yi Tseng³

¹Bachelor of Program in Scientific Agriculture, National Pingtung University of Science and Technology, Pingtung, 91201, Taiwan. csiefly@mail.npust.edu.tw.

Scientific Reports

|May 24, 2022

Summary

This summary is machine-generated.

This study introduces an automated deep learning system for detecting ripe pineapples, crucial for Taiwan

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Area of Science:

Agricultural Technology
Computer Vision
Machine Learning

Background:

Pineapples are a significant economic crop in Taiwan, vulnerable to sun damage.
Manual protection and ripeness assessment require substantial labor and are inefficient.
Current methods like paper bagging hinder ripeness evaluation.

Purpose of the Study:

To develop an automated system for detecting ripe pineapples.
To overcome limitations of manual protection and ripeness assessment.
To improve efficiency and reduce losses in pineapple cultivation.

Main Methods:

A deep learning architecture was designed for automated pineapple ripeness detection.
The system utilizes an Nvidia TX2 for real-time processing.
The system was tested for accuracy and speed under various conditions.

Main Results:

Achieved over 99.27% accuracy in ripeness detection.
Maintained an error rate below 2% across a 300–800 mm detection range.
Operates at 15 frames per second, suitable for mobile platforms.

Conclusions:

The proposed deep learning system offers a highly accurate and efficient solution for automated pineapple ripeness detection.
This technology can significantly reduce labor costs and improve crop management in pineapple farming.
The system's performance in varied lighting conditions and its processing speed make it practical for agricultural applications.