Machine learning application for predicting the strawberry harvesting time

  • Yang, Mi-Hye (Department of Bioresources and Rural Systems Engineering, Hankyong National University) ;
  • Nam, Won-Ho (Department of Bioresources and Rural Systems Engineering, Hankyong National University) ;
  • Kim, Taegon (Institute on the Environment, University of Minnesota) ;
  • Lee, Kwanho (CESeL Primus) ;
  • Kim, Younghwa (Rural Research Institute, Korea Rural Community Corporation)
  • Received : 2019.03.15
  • Accepted : 2019.05.29
  • Published : 2019.06.01


A smart farm is a system that combines information and communication technology (ICT), internet of things (IoT), and agricultural technology that enable a farm to operate with minimal labor and to automatically control of a greenhouse environment. Machine learning based on recently data-driven techniques has emerged with big data technologies and high-performance computing to create opportunities to quantify data intensive processes in agricultural operational environments. This paper presents research on the application of machine learning technology to diagnose the growth status of crops and predicting the harvest time of strawberries in a greenhouse according to image processing techniques. To classify the growth stages of the strawberries, we used object inference and detection with machine learning model based on deep learning neural networks and TensorFlow. The classification accuracy was compared based on the training data volume and training epoch. As a result, it was able to classify with an accuracy of over 90% with 200 training images and 8,000 training steps. The detection and classification of the strawberry maturities could be identified with an accuracy of over 90% at the mature and over mature stages of the strawberries. Concurrently, the experimental results are promising, and they show that this approach can be applied to develop a machine learning model for predicting the strawberry harvesting time and can be used to provide key decision support information to both farmers and policy makers about optimal harvest times and harvest planning.

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Fig. 1. Comparison of agricultural crop income data between 2005 and 2017 year.

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Fig. 2. Classifcation of crop image according to growing steps: (a) Test image of immature strawberry, (b) Test image of mature strawberry, (c) Test image of over mature strawberry.

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Fig. 3. Accuracy results based on number of training images and steps for immature strawberry.

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Fig. 4. Accuracy results based on number of training images and steps for mature strawberry.

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Fig. 5. Accuracy results based on number of training images and steps for over mature strawberry.

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Fig. 6. Detection results of immature strawberries according to training steps: (A) Step 100, (B) Step 2,000, (C) Step 20,000, (D) Step 50,000, (E) Step 100,000, (F) Step 150,000, (G) Step 200,000.

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Fig. 7. Classifcation loss of training data based on training steps.

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Fig. 8. Classifcation loss of validation data based on training steps.

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Fig. 9. Localization loss of training data based on training steps.

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Fig. 10. Localization loss of validation data based on training steps.

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Fig. 11. Detection results of growth stages strawberries: (A, D, G) Detection of immature strawberry images, (B, E, H) Detection of mature strawberry images, (C, F, I) Detection of over mature strawberry images.

Table 1. Accuracy results based on number of training images and steps for immature strawberry.

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Table 2. Accuracy results based on number of training images and steps for mature strawberry.

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Table 3. Accuracy results based on number of training images and steps for over mature strawberry.

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Supported by : 농림식품기술기획평가원


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