Tracking Growth of Tomato Based on Image Recognition -an Example of Traditional Tomato Greenhouse
碩士 === 國立暨南國際大學 === 資訊管理學系 === 108 === Recently, with the rise of artificial intelligence, the government has also promoted and encouraged smart agriculture. Despite having great development in environmental and plant physiological sensing, the use of image sensing technology is still rare in ag...
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| Format: | Others |
| Language: | zh-TW |
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2019
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| Online Access: | http://ndltd.ncl.edu.tw/handle/eh5zb5 |
| Summary: | 碩士 === 國立暨南國際大學 === 資訊管理學系 === 108 === Recently, with the rise of artificial intelligence, the government has also promoted and encouraged smart agriculture. Despite having great development in environmental and plant physiological sensing, the use of image sensing technology is still rare in agricultural field.
This study is based on daily image tracking to monitor the change in the proportion of color patches of tomato in YuYing Elementary school's traditional greenhouses. It also uses Faster R-CNN to establish a deep learning model to investigate the characteristics and process of tomato’s daily growth. The study began with the seedling stage of the tomato and ended at the end of the harvest stage for a total of 78 days. This study shown the difference between the two identification methods and the actual growth of the tomato to examine the accuracy of the two methods.
This study will track the change in the proportion of tomato photoblocks grown in the YuYing Elementary school's traditional greenhouses in Puli Township. TensorFlow and Faster RCNN were also used to identify the characteristics of daily tomato growth to supplement the deficiency. The study began with the seedling stage of the tomato and ended at the harvesting stage. The total duration needed is around 78 days. The study has shown the difference between two identification methods. The actual growth of the tomato represents the accuracy of the two methods.
Through research, it is found that both types of identification have different advantages and disadvantages in different growth stages. The way to track the area of the color patch is basically consistent with the growth of the tomato when identifying the green area. The yellow and red are susceptible to external factors and the accuracy is not high. The study shown that object detection is more accurate in identifying flowers and fruits compared to that of tracking the area of patches.
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