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      Evaluation of Deep Neural Network and alternating decision tree for kiwifruit detection

      Kuang, Ye Chow; Streeter, Lee; Cree, Michael J.; Ooi, Melanie Po-Leen
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      I2MTC2019 Kiwifruit detection v3.pdf
      Accepted version, 233.9Kb
      Link
       i2mtc2019.ieee-ims.org
      Citation
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      Kuang, 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.
      Permanent Research Commons link: https://hdl.handle.net/10289/12675
      Abstract
      Robotic 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.
      Date
      2019
      Type
      Conference Contribution
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      • Science and Engineering Papers [3122]
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