Monitoring the Growth of Tomatoes in Real Time with Deep Learning-based Image Segmentation

Increasing agricultural productivity such as tomatoes needs to be increased, considering the consumption growth reaches 6.34% per year. Efforts to increase productivity can be made through several methods, such as counting and predicting the time of fruit to be harvested. This information is a a visual problem, so computer vision should solve it as an automation method in the industry world. With this information, the farmer can monitor the tomato fruit growth. The proposed method is a framework that has been implemented in real-time processing. To obtain growth information of tomatoes, the tomato area can be used as a region of interest (ROI) every week or another scheduled time. As the challenge of this research, this ROI can be extracted using segmentation analysis. The segmentation method used is Mask Region-Convolutional Network (R-CNN) with ResNet101 architecture. The accuracy of this method is obtained from the similarity value between the proposed method and the ground truth used, namely 97.34% using the Dice Coefficient and 94.83% using the Jaccard Coefficient. This result indicates that the method can extract the ROI information with high accuracy. So, the result can be used as a reference for the farmer to treat each tomato plant.

@article{Widiyanto2021,
  title     = {Monitoring the Growth of Tomatoes in Real Time with Deep Learning-based Image Segmentation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {12},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Sigit Widiyanto and Dheo Prasetyo Nugroho and Ady Daryanto and Moh Yunus and Dini Tri Wardani},
  doi       = {10.14569/IJACSA.2021.0121247},
  url       = {https://doi.org/10.14569/IJACSA.2021.0121247}
}