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dc.contributor.authorKuang, Ye Chowen_NZ
dc.contributor.authorStreeter, Leeen_NZ
dc.contributor.authorCree, Michael J.en_NZ
dc.contributor.authorOoi, Melanie Po-Leenen_NZ
dc.coverage.spatialAuckland, New Zealanden_NZ
dc.date.accessioned2019-07-03T00:41:06Z
dc.date.available2019en_NZ
dc.date.available2019-07-03T00:41:06Z
dc.date.issued2019en_NZ
dc.identifier.citationKuang, Y. C., Streeter, L. V., Cree, M. J., & Ooi, M. P.-L. (2019). Evaluation of Deep Neural Network and alternating decision tree for kiwifruit detection. Presented at the I2MTC 2019 IEEE International Instrumentation & Measurement Technology Conference, Auckland, New Zealand.en
dc.identifier.urihttps://hdl.handle.net/10289/12675
dc.description.abstractRobotic kiwifruit harvesting systems are currently being introduced to improve the reliability and farming yields of kiwifruit harvesting operations. Machine learning is widely used to carry out the visual detection tasks required of such systems. This paper specifically compares two types of machine learning algorithms: the multivariate alternating decision tree and deep learning based kiwifruit classifiers. The purpose of the study is to investigate the cost of implementation against the classification performance. Thus, discussion is centred around computational cost and its impacts on the overall system architecture. We found that the traditional decision tree classifiers can achieve comparable classification performance at a fraction of the cost and complexity, providing robust and cost-effective instrument design.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.relation.urihttps://i2mtc2019.ieee-ims.org/sites/i2mtc19/files/documents/call-docs/I2MTC-2019_Program_FINAL_Ad_updated_V2_0.pdf
dc.sourceI2MTC 2019 IEEE International Instrumentation & Measurement Technology Conferenceen_NZ
dc.subjectclassification
dc.subjectdecision tree
dc.subjectdeep learning
dc.subjectprecision agriculture
dc.subjectvision
dc.titleEvaluation of Deep Neural Network and alternating decision tree for kiwifruit detectionen_NZ
dc.typeConference Contribution
pubs.elements-id238468
pubs.finish-date2019-05-23en_NZ
pubs.publisher-urlhttps://i2mtc2019.ieee-ims.org/sites/i2mtc19/files/documents/call-docs/I2MTC-2019_Program_FINAL_Ad_updated_V2_0.pdfen_NZ
pubs.start-date2019-05-20en_NZ


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