Apply of Deep Learning Techniques to Measure the Sweetness Level of Watermelon via Smartphone

Main Article Content

Nattavadee Hongboonmee
Nutthapong Jantawong

Abstract

This research proposed the development of automated system for analyzing sweetness and watermelon varieties from photos using deep learning techniques for use on smartphones. To help the public who would like to know the names of varieties and sweetness of watermelons. The main components of the system include: (1) Modeling, classifying varieties and sweetness levels of watermelons with deep learning neural network through the TensorFlow library, using the InceptionV3 and MobileNet algorithms to compare image classification. In which the trainers are able to classify 4 types of images, each type of 100 images, and training our model for 500 rounds. The result shows that the model from the InceptionV3 algorithm has the same accuracy as the model from the MobileNet algorithm. The accuracy is 97.20%. Therefore considering the model size obtained from learning, it found that MobileNet model size is smaller than InceptionV3, so choose the model from MobileNet to develop the system further. (2) Using the model from MobileNet algorithm to develop application on smartphones, which developed by Android Studio program. Results of the user satisfaction test, found that the average satisfaction is 4.34, standard deviation 0.62, it at good level. In conclusion, this application is effective and can use in real life.

Downloads

Download data is not yet available.

Article Details

How to Cite
[1]
N. Hongboonmee and N. Jantawong, “Apply of Deep Learning Techniques to Measure the Sweetness Level of Watermelon via Smartphone”, JIST, vol. 10, no. 1, pp. 59-69, Jun. 2020.
Section
Research Article: Soft Computing (Detail in Scope of Journal)

References

[1] E. MuengKasem, “Watermelon Secrets We Never Knew: I Love Nature Set,” 1st ed. Bangkok: Nanmeebooks Kiddy, 2016.

[2] T. Chaerueangyot, “Growing crops and making good money,” 1st ed. Bangkok: Agricultural Knowledge Distribution Club, 2015.

[3] Q. Wu, Y. Liu, Q. Li, S. Jim and F. Li, “The Application of Deep Learning in Computer Vision,” in Proceeding of 2017 Chinese Automation Congress (CAC), Jinan, China, 2017, pp. 6522–6527.

[4] P. Chandran, B. Byju, R. Deepak, K. Nishakumari, P. Devanand and P. Sasi, “Missing Child Identification System using Deep Learning and Multiclass SVM,” in Proceeding of 2018 IEEE Recent Advances in Intelligent Computational Systems (RAICS), Thiruvananthapuram, India, 2018, pp. 113–116.

[5] B. Debnath, M. O’Brien, M. Yamaguchi and A. Behera, “Adapting MobileNets for Mobile Based Upper Body Estimation,” in Proceeding of 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Auckland, New Zealand, 2018.

[6] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, Rethinking the Inception Architecture for Computer Vision. In Proceeding of 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, Nevada, 2016, pp. 2818-2826.

[7] F. Ertam and G. Aydin, “Data Classification with Deep Learning using Tensorflow,” in Proceeding of 2017 International Conference on Computer Science and Engineering (UBMK), Antalya, Turkey, 2018, pp. 755–758.

[8] KBThaiscale, “Brix Refractometer 0-32%,”2019. [Online].Available:http://www.kbthaiscale.com/product/489. [Accessed: April 11, 2020].

[9] C. Sarawong, S. Somabut, P. Imtongkhum, C. Pimson and C. So-In, “Notification Validation and Identification Systems of Lost Dog,” in Proceeding of 14th National Conference on Computing and Information Technology (NCCIT), King Mongkut’s University of Technology North Bangkok, Chiang Mai, 2018, pp. 678–685.

[10] P. Wairotchanaphutha, N. Boonsirisumpun, W. Puarungroj, “Detection and Classification of Vehicles using Deep Learning Algorithm for Video Surveillance Systems,” in Proceeding of 14th National Conference on Computing and Information Technology (NCCIT), King Mongkut’s University of Technology North Bangkok, Chiang Mai, 2018, pp. 402–407.

[11] A. Rangsuk, T. Kungkajit, “Classification of Amulets using Deep Learning Techniques,” in Proceeding of 10th National Conference on Information Technology (NCIT), Mahasarakhan University, Khon Kaen, 2018, pp. 190–194.

[12] L. Pan, S. Pouyanfar, H. Chen, J. Qin and S. Chen, "DeepFood: Automatic Multi-Class Classification of Food Ingredients Using Deep Learning", 2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC), San Jose, CA, 2017, pp. 181-189.

[13] A. V. Singh, Content-based Image Retrieval using Deep Learning, New York: Rochester Institute of Technology, 2015.

[14] E. Pacharawongsakda, “An Introduction to Data Mining Techniques,” 2nd ed. Bangkok: Data cube, 2014.