The Analysis of Ripening of Pineapple Fruits Using Machine Learning Technique
Keywords:
Model YOLOv4, Deep Learning, Machine Learning, CNN, R-CNN, GPU, PineappleAbstract
During the Fourth Industrial Revolution, artificial intelligence is being widely applied in a variety of fields. However, in the current agricultural model, humans are still used as the primary labor force, which is costly in terms of both finance and human resources. Furthermore, each region's typical fruits, particularly pineapple, have a rather complicated ripening period. It is difficult to control and manage hundreds of hectares of land. As a result, in this paper, we propose using deep learning models to aid in the identification and detection of ripe pineapple growth stages in order to ensure that care and harvesting are completed on time.
References
Inkyu Sa, ZongYuan Ge, Feras Dayoub, B. Upcroft, Tristan Perez, C. McCool. DeepFruits: A Fruit Detection System Using Deep Neural Networks (2016).
YunongTian, GuodongYang, ZheWang, HaoWang, EnLi, ZizeLiang. Apple detection during different growth stages in orchards using the improved YOLO-V3 model (2012).
Byoungjun Kim, You-Kyoung Han, Jong-Han Park and Joonwhoan Lee1. Improved Vision-Based Detection of Strawberry Diseases Using a Deep Neural Network (2021).
Jose Luis Rojas-Aranda, Jose Ignacio Nunez-VarelaJ. C. Cuevas-TelloGabriela Rangel-Ramirez. Fruit Classification for Retail Stores Using Deep Learning (2020).
Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi. You Only Look Once: Unified, Real-Time Object Detection
Guoxu Liu, Joseph Christian Nouaze, Philippe Lyonel Touko Mbouembe and Jae Ho Kim. YOLO-Tomato: A Robust Algorithm for Tomato Detection Based on YOLOv3 (2020).
Redmon, J.; Divvala, S.; Girshick, R.; Farhadi, A. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27 June 2016; pp. 779–788.
Redmon, J.; Farhadi, A. YOLO9000: Better, faster, stronger. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 26 July 2017; pp. 7263–7271.
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun, “Faster R-CNN: Towards RealTime Object Detection with Region Proposal Networks,” arXiv:1506.01497v3 [cs.CV], Jan. 2016.
Redmon, J.; Farhadi, A. YOLOv3: An incremental improvement. arXiv 2018, arXiv:1804.02767.
Munera, S., Amigo, J. M., Blasco, J.; Cubero, S.; Talens, P., Alexios, N. (2017). Ripeness monitoring of two cultivars of nectarine using VIS-NIR hyperspectral reflectance imaging. Journal of Food Engineering, 214(3), 29-39.
Optimal deep learning model for classification of lung cancer on CT images, (2019), Lakshmanaprabu S.K., Sachi Nandan Mohanty, Shankar K., Arunkumar N., Future Generation Computer Systems, 19, 1, 374-382.
Lakshmanaprabu S.K., Sachi Nandan Mohanty, Shankar K., Arunkumar N., Optimal deep learning model for classification of lung cancer on CT images, (2019), Future Generation Computer Systems, 19, 1, 374-382.
Tian, Y., Yang, G., Wang, Z., et al., 2019. Apple detection during different growth stages in orchards using the improved YOLO-V3 model[J]. Computers and Electronics in Agriculture 157, 417–426.
A. B. Alexey, “YOLO mark,” 2018. [Online]. Available: https: github.com/AlexeyAB/Yolo mark Apple detection during different growth stages in orchards using the improved YOLO-V3 model
P. Rajeshwari, P. Abhishek, P. Srikanth, T. Vinod (2019) Object Detection: An Overview. International Journal of Trend in Scientific Research and Development (IJTSRD), Vol. 3: pp.1663- 1665
Xiongwei Wu, Doyen Sahoo, Steven C.H. Hoi, “Recent Advances in Deep Learning for Object Detection,” arXiv:1908.03673v1 [cs.CV], Aug. 2019
Yuting Zhang, Kihyuk Sohn, Ruben Villegas, Gang Pan, Honglak Lee, “Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction,” arXiv:1504.03293 [cs.CV], Jan. 2016.
Stajnko, Denis and Lakota, Miran and Ho?cevar, Marko. (2004). Estimation of number and diameter of apple fruits in an orchard during the growing season by thermal imaging. Computers and Electronics in Agriculture. 42. 31-42. 10.1016/S0168-1699(03)00086-3.
Redmon, J., Farhadi, A., 2018. YOLOv3: An incremental improvement. In: IEEE conference on Computer Vision and Pattern Recognition, arXiv:1804.0276.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015.
Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao. YOLOv4: Optimal Speed and Accuracy of Object Detection.
Van B. Dang, Duy-Dinh Le, Duc A. Duong, Image Filtering Using Visual Information Consistency, Proceedings of the First International Conference on Theories and Applications of Computer Science (ICTACS 2006), Ho Chi Minh City, Vietnam, 2006, pp 51 – 64
Thang B. Dinh, Van B. Dang, Duc A. Duong, Tuan T. Nguyen, Duy-Dinh Le, Hand Gesture Classification Using Boosted Cascade of Classifiers, Proceedings of 4th IEEE International Conference Research, Innovation and Vision of the Future RIVF 2006, Ho Chi Minh City, Vietnam, Feb. 12-16, 2006, pp 138-143
Hoang Van Kiem, Duong Anh Duc, Le Dinh Duy, A Fast Algorithm for Polygon Clipping, Journal of Institute of Mathematics and Computer Sciences, India, Vol. 13, No. 1, 2002.
P. Rajeshwari, P. Abhishek, P. Srikanth, T. Vinod (2019) Object Detection: An Overview. International Journal of Trend in Scientific Research and Development (IJTSRD), Vol. 3: pp.1663- 1665
Xiongwei Wu, Doyen Sahoo, Steven C.H. Hoi, “Recent Advances in Deep Learning for Object Detection, ” arXiv:1908.03673v1 [cs.CV], Aug. 2019
Yuting Zhang, Kihyuk Sohn, Ruben Villegas, Gang Pan, Honglak Lee, “Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction,” arXiv:1504.03293 [cs.CV], Jan. 2016
K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015.
Dumitru Erhan, Christian Szegedy, Alexander Toshev, Dragomir Anguelov (2014) Scalable Object Detection using Deep Neural Networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2147-2154
Yongxi Lu, Tara Javidi, Svetlana Lazebnik, “Adaptive Object Detection Using Adjacency and Zoom Prediction,” arXiv:1512.07711 [cs.CV], Apr. 2016
Sun Q., Pfahringer B. (2011) Bagging Ensemble Selection. In: Wang D., Reynolds M. (eds) AI 2011: Advances in Artificial Intelligence. AI 2011. Lecture Notes in Computer Science, vol 7106. Springer, Berlin, Heidelberg
Zhong-Qiu Zhao, Peng Zheng, Shou-tao Xu, Xindong Wu, “Object Detection with Deep Learning: A Review, ” arXiv:1807.05511 [cs.CV], Apr. 2019
Keiron O’Shea, Ryan Nash, “An Introduction to Convolutional Neural Networks,” arXiv:1511.08458 [cs.NE], Dec. 2015
Munera, S., Amigo, J. M., Blasco, J.; Cubero, S.; Talens, P., Alexios, N. (2017). Ripeness monitoring of two cultivars of nectarine using VIS-NIR hyperspectral reflectance imaging. Journal of Food Engineering, 214(3), 29-39.
Santagapita, P.R., Tylewicz, U. Panarese, V., Rocculi, P, Dalla, Rosa, M. (2016). Nondestructive assessment of kiwifruit physic-chemical parameters to optimize the osmotic dehydration process, A study on FT-NIR spectroscopy. Journal of Biosyst. Eng. 142, (2), 101-129.
S. Kim, Y. Ji and K. Lee,” An Effective Sign Language Learning with Object Detection Based ROI Segmentation,” 2018 Second IEEE International Conference on Robotic Computing (IRC), Laguna Hills, CA, 2018, pp. 330-333.
Stajnko, Denis and Lakota, Miran and Ho?cevar, Marko. (2004). Estimation of number and diameter of apple fruits in an orchard during the growing season by thermal imaging. Computers and Electronics in Agriculture. 42. 31-42.
Nguyen H.H.C., Luong A.T., Trinh T.H., Ho P.H., Meesad P., Nguyen T.T. (2021) Intelligent Fruit Recognition System Using Deep Learning. In: Meesad P., Sodsee D.S., Jitsakul W., Tangwannawit S. (eds) Recent Advances in Information and Communication Technology 2021. IC2IT 2021. Lecture Notes in Networks and Systems, vol 251. Springer, Cham. https://doi.org/10.1007/978-3-030-79757-7_2''.
Nguyen, H.H.C., Nguyen, D.H., Nguyen, V.L., Nguyen, T.T.: Smart solution to detect images in limited visibility conditions based convolutional neural networks. In: Advances in Computational Collective Intelligence. ICCCI 2020. Communications in Computer and Information Science, vol. 1287, pp. 641–650. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63119-2-52.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 International Journal of Machine Learning and Networked Collaborative Engineering
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
https://creativecommons.org/licenses/by/4.0/legalcode
