Author:
Kim EungChan,Hong Suk-Ju,Kim Sang-Yeon,Lee Chang-Hyup,Kim Sungjay,Kim Hyuck-Joo,Kim Ghiseok
Abstract
AbstractModern people who value healthy eating habits have shown increasing interest in plum (Prunus mume) fruits, primarily owing to their nutritiousness and proven efficacy. As consumption increases, it becomes important to monitor work to prevent Prunus mume fruits from falling out. Moreover, determining the growth status of Prunus mume is also crucial and is attracting increasing attention. In this study, convolutional neural network (CNN)-based deep learning object detection was developed using RGBD images collected from Prunus mume farms. These RGBD images consider various environments, including the depth information of objects in the outdoor field. A faster region-based convolutional neural network (R-CNN), EfficientDet, Retinanet, and Single Shot Multibox Detector (SSD) were applied for detection, and the performance of all models was estimated by comparing their respective computing speeds and average precisions (APs). The test results show that the EfficientDet model is the most accurate, and SSD MobileNet is the fastest among the four models. In addition, the algorithm was developed to acquire the growth status of P. mume fruits by applying the coordinates and score values of bounding boxes to the depth map. Compared to the diameters of the artificial Prunus mume fruits used as the experimental group, the calculated diameters were very similar to those of the artificial objects. Collectively, the results demonstrate that the CNN-based deep learning Prunus mume detection and growth estimation method can be applied to real farmlands.
Publisher
Springer Science and Business Media LLC
Reference45 articles.
1. Moghadam, P. et al. Plant disease detection using hyperspectral imaging. In DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications vols. 2017-December 1–8 (Institute of Electrical and Electronics Engineers Inc., 2017).
2. Chetan Dwarkani, M., Ganesh Ram, R., Jagannathan, S., Priyatharshini, R. Smart farming system using sensors for agricultural task automation. In Proceedings - 2015 IEEE International Conference on Technological Innovations in ICT for Agriculture and Rural Development, TIAR 2015 49–53 (Institute of Electrical and Electronics Engineers Inc., 2015). Doi: https://doi.org/10.1109/TIAR.2015.7358530.
3. Migdall, S., Klug, P., Denis, A., Bach, H. The additional value of hyperspectral data for smart farming. In International Geoscience and Remote Sensing Symposium (IGARSS) 7329–7332 (2012). Doi:https://doi.org/10.1109/IGARSS.2012.6351937.
4. Meyer, M. H. et al. Importance of horticulture and perception as a career. J. Am. Soc. Horticu. Sci. 26, 114 (2016).
5. Grift, T., Zhang, Q., Kondo, N. & Ting, K. C. A review of automation and robotics for the bio-industry. J. Biomech. Eng. 1, 37 (2008).
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