A pilot programme of fruit grading system using computer vision

RESEARCH QUESTIONS: 1. How can New Zealand’s agriculture industry use computer vision technologies to improve the accuracy of their fruit grading system? 2. What are the similarities and differences in object detection and image classification effectiveness in grading fruits? 3. What factors influence agricultural companies’ decision on embracing computer vision for fruit grading in New Zealand? ABSTRACT: Traditional fruit grading work depends on a large labour force during harvesting time. However, the grading accuracy varies, resulting in difficulties in product quality management. Due to the COVID-19 pandemic, many of New Zealand's farming industries lack seasonal workers from overseas. Hence, they are looking at taking advantage of promising computer vision technologies to avoid reliance on the labour-intensive grading method. This research project designs a prototype fruit grading system using object detection algorithms to automatically sort fruits (e.g., squash), minimising manual intervention during the production process. The proposed system consists of fruit handling and image processing modules. Amazon's machine learning platform SageMaker and Google's machine learning framework TensorFlow are the two main software components in the system. We tested the prototype in a simulated production environment. The result proved that the selected approach could suit farming industries to achieve automation transformation during post-harvesting. Other findings include that object detection has better performance than image classification on identifying defects on fruits. The high cost of setting up a new fruit grading system has hindered agriculture companies from adopting the plan. Further research within this topic could incorporate farming experts to help gain higher dataset accuracy during labelling jobs and choose a proper approach to avoid unnecessary difficulties in manipulating data between different machine learning platforms.

Name thesis:degree_name

Master of Information Technology

Level thesis:degree_level

Masters

Grantor

Eastern Institute of Technology (EIT)

Year dc:date.issued

2021

Author dc:creator

• Wang, Liang

• computer vision
• object detection
• machine learning
• deep learning
• fruit grading
• SageMaker
• MXNet
• TensorFlow
• New Zealand

Statement dc:rights

• All rights reserved

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10652/5667

OAI identifier oai:identifier

oai:null:10652/5667