Fruit Ripeness Assertion Using Deep Learning
DOI:
https://doi.org/10.5281/zenodo.7900479Keywords:
Deep Learning, YoLo V3, Convolutional Neural Network, OpenCVAbstract
Agriculture is the most crucial division in the country by contributing to numerous domains. In underdeveloped countries, farmers and agriculture field have delimited access to advanced technology in comparison to developed countries. In productive companies irrespective of public or private sector, large or small scale, there is a need to increase profitability with reduced cost. Hence it is required to develop appropriate ways to achieve these goals. This agricultural field is obviously a challenging field to the digital world. This paper discuss Smart fruit ripening assertion technique by incorporating the deep learning techniques such as YoloV3 , a deep Convolutional Neural Network (CNN). The focus of this model is to design and deploy practical tasks, predicting the ripening stages of various kinds of fruits based on shape, texture, and color by using and comparing various Machine learning techniques, OpenCV and Internet of Things (IOT). The main intention of this model is to provide accurate prediction of ripening stages of the fruits by computer application which results in a lot of time saving and reduction of large-scale manpower.
References
Arefi, A., Motlagh, A. M., Mollazade, K., & Teimourlou, R. F. (2011). Recognition and localization of ripen tomato based on machine vision. Australian Journal of Crop Science, 5(10), 1144-1149.
Memoria, M., Shah, S. K., Anandaram, H., Ali, A., Joshi, K., Verma, P., ... & Akram, S. V. (2023). An internet of things enabled framework to monitor the lifecycle of Cordyceps sinensis mushrooms. International Journal of Electrical & Computer Engineering (2088-8708), 13(1).
Bargoti, S., & Underwood, J. (2017, May). Deep fruit detection in orchards. In 2017 IEEE international conference on robotics and automation (ICRA) (pp. 3626-3633). IEEE.
Vitalkar, N., Nisheetha, R., Niveda, A., Rohith, N., & Ali, A. (2021). CONTRIVANCE FOR SMART MANUFACTURING INTENSIFIED APPLICATION BASED ON CLOUD COMPUTING. organization, 8(5).
Ahmed, S. T., Sreedhar Kumar, S., Anusha, B., Bhumika, P., Gunashree, M., & Ishwarya, B. (2020). A generalized study on data mining and clustering algorithms. New Trends in Computational Vision and Bio-inspired Computing: Selected works presented at the ICCVBIC 2018, Coimbatore, India, 1121-1129.
Kuznetsova, A., Maleva, T., & Soloviev, V. (2020). Using YOLOv3 algorithm with pre-and post-processing for apple detection in fruit-harvesting robot. Agronomy, 10(7), 1016.
Africa, A. D. M., Tabalan, A. R. V., & Tan, M. A. A. (2020). Ripe fruit detection and classification using machine learning. Int J, 8(5).
Uluişik, S., Yildiz, F., & Özdemİr, A. T. (2018, April). Image processing based machine vision system for tomato volume estimation. In 2018 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT) (pp. 1-4). IEEE.
Kinjo, H., Oshiro, N., & Duong, S. C. (2015, May). Fruit maturity detection using neural network and an odor sensor: Toward a quick detection. In 2015 10th Asian Control Conference (ASCC) (pp. 1-4). IEEE.
Sooryah, P., Mahapatra, A., Dhanshree, R., & Ajil, A. (2020). STOCK PREDICTOR ANALYSIS USING ML AND DATA SCIENCE. International Journal of Advanced Research in Computer Science, 11.
Sreedhar Kumar, S., Ahmed, S. T., Mercy Flora, P., Hemanth, L. S., Aishwarya, J., GopalNaik, R., & Fathima, A. (2021, January). An Improved Approach of Unstructured Text Document Classification Using Predetermined Text Model and Probability Technique. In ICASISET 2020: Proceedings of the First International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICASISET 2020, 16-17 May 2020, Chennai, India (p. 378). European Alliance for Innovation.
Maia, R. F., Netto, I., & Tran, A. L. H. (2017, October). Precision agriculture using remote monitoring systems in Brazil. In 2017 IEEE global humanitarian technology conference (GHTC) (pp. 1-6). IEEE.
Gupta, A., Nagda, D., Nikhare, P., & Sandbhor, A. (2021). Smart crop prediction using IoT and machine learning. International Journal of Engineering Research & Technology (IJERT), 2278-0181.
Gunashree, M., Ahmed, S. T., Sindhuja, M., Bhumika, P., Anusha, B., & Ishwarya, B. (2020). A New Approach of Multilevel Unsupervised Clustering for Detecting Replication Level in Large Image Set. Procedia Computer Science, 171, 1624-1633.
Teja, N. N., Reddy, G. Y. N., Reddy, G., & Ajil, A. (2021). HEART BEAT LEVEL PREDICTION USINGMACHINE LEARNING ALGORITHMS. International Journal of Advanced Research in Computer Science, 12.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 A Ajil, Anooja Ali, A Kanthi Kiran Reddy, L Vasista C Reddy, A V Guna Sekhar Reddy, OmPrakash Goud
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
