Autonomous Tomato Harvesting Robotic System in Greenhouses: Deep Learning Classification

  • Ooi Peng Toon Universiti Malaysia Pahang
  • Muhammad Aizzat Zakaria Universiti Malaysia Pahang
  • Ahmad Fakhri Ab. Nasir Universiti Malaysia Pahang
  • Anwar P.P. Abdul Majeed Universiti Malaysia Pahang
  • Chung Young Tan KUKA Robot Automation (Malaysia) Sdn. Bhd.
  • Leonard Chong Yew Ng KUKA Robot Automation (Malaysia) Sdn. Bhd.
Keywords: Convolution Neural Network (CNN), deep learning, tomato, harvesting robot, classification


Solanum lycopersicum or generally known as tomato came from countries of South America and has been growing in many tropical countries and its healthy nutrients in tomato becomes one of the food demand by the locals in Malaysia when their lifestyle shifted to more concern for healthy food. Since export value and production has increased for the past few years, a vast amount of labours considered for the fruit-picking process. Hence, farmers are now preferring to look for automation to replace labour problems and high cost that they are facing. To pick a correct fruit within clusters, a harvesting robot requires guidance so that it can detect a fruit accurately. In this study, a new classification algorithm using deep learning specifically convolution neural network to classify the image is either a tomato or not tomato and next, the image is classified into either a ripe or unripe tomato. Furthermore, there are two classification neural networks which are tomato or not tomato and ripe and unripe tomato. Each network consists of 600 training data and 33 testing data. The accuracies that obtained from network 1 (tomato or not tomato) and network 2 (ripe or unripe tomato) are 76.366% and 98.788% respectively.

How to Cite
O. P. Toon, M. A. Zakaria, A. F. Ab. Nasir, A. P.P. Abdul Majeed, C. Y. Tan, and L. C. Y. Ng, “Autonomous Tomato Harvesting Robotic System in Greenhouses: Deep Learning Classification”, Mekatronika, vol. 1, no. 1, pp. 80-86, Jan. 2019.
Original Article

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