# An Automatic Tomato Growth Analysis System Using YOLO Transfer Learning
## 5 Discussion  
In this research, it was found that if the plant to be measured and the ball attached to it can be detected correctly in the image, the stem diameter can be calculated with an error of less than 10%. Owing to the characteristics of the camera lens, it was difficult to measure the stem diameter correctly when the stem is detected at the edge of the image due to image distortion. After changing the orientation of the camera so that the detected stem is in the center of the image, the measurement can be performed with less error.  
The camera device developed in this study cost about USD 150 each, and once installed, it does not need to be moved unless they malfunction. This is a significant cost saving as commercially available stem diameter change sensors cost USD 780 to USD 3900 per plant [16]. The reference balls need to be attached to each plant, one on each stem near the tomato flower cluster and one near the growing point, and the tomatoes grow so fast that they need to be moved once every two weeks. The work involved in this system requires  
Figure 11: Measurement results of stem 2.  
Figure 12: Measurement results of stem 3.  
Figure 10: Measurement results of stem 1.  
only moving the reference balls once every two weeks, and can be done in less time than measuring tomato stems with calipers. This work can also be done at the same time if the plants are being cultivated with attractant cords. In this experiment, it took about 5 min per 10 plants to change the position of the reference ball. This method takes about 80% less time than the method using calipers, and there is no possibility of damaging the stems. This method can greatly reduce the user's labor.  
However, the system developed in this research has difficulty in detecting tomato plants by machine learning when the distance between the camera and the target tomatoes is large, or when the angle of the camera changes. Therefore, the number of plants that could be detected was reduced. Future issues include improving the detection performance of machine learning. We also believe that the use of a camera with an autofocus function as a hardware improvement will make it possible to measure plants at greater distances.  
Conceptualization, K.F. and H.N.; methodology, K.F., K.H., J.C. and D.T.; software, K.F. and K.H.; validation, K.F. and K.H.; formal analysis, K.F.; investigation, K.F.; resources, T.K., K.K., K.S. and Y.N.; data curation, K.F.; writing--original draft preparation, K.F.; writing--review and editing, I.S., H.N. and T.K.; visualization, K.F.; supervision, H.N.; project administration, H.N.; funding acquisition, H.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Tokyo Metropolitan Industrial Technology Research Institute (TIRI), project JFNP16007 commissioned by the New Energy, and Industrial Technology Development Organization (NEDO) and JSPS KAKENHI, grant numbers 21K11804 and 19K11879.  
Not applicable.  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.