Tuesday, May 5, 2020

Android App Detecting Mellow Fruits Samples †MyAssignmenthelp.com

Question: Discuss about the Android App Detecting Mellow Fruits. Answer: Introduction Bee Jeng Fruit Supply Pte Ltd has recently developed an app which can identify the mellow fruits using a phone camera and phone sensor (Zhang et al. 2014). The app even has the capability to calculate the time period for a fruit to mature. This report will highlight the Mellow Fruits app developed by Bee Jeng Fruit Supply Pte Ltd and its beneficial aspects. Hardware used Android phone is used as the hardware for this task. After developing an android app, it is necessary to test the app on the real device. Therefore, one needs to set up an Android phone device at first, one the phone device, one will have to go to Settings app, have to select Developer options, and then have to enable USB debugging (Developer.android.com 2017). On the windows, computer one will have to install OEM USB drivers so that it can recognise the phone device. After that, one will have to connect a computer with the android phone via a cable. To test the whole setup one will have to write code on the computer and have to run on phone device ( Developer.android.com 2017). Software used Language used- JAVA Tool used- SDK packages Platform used- Android Computer Platform used- Windows Backend used- SQLite server Software used- Android Studio Security Key- RSA key- it enables debugging through the computer; When one connects a device running Android to a computer, the computer system asks whether to accept the RSA key (Developer.android.com 2017). Interconnectivity The degree of mellowness of fruits is detected using some standardised process. This standardised process involves complex algorithms, processing of digital image and in this way the detailed characteristics of a particular fruit can be determined (Gomes-Junior, Arruda and Marcos-Filho 2017). The processing technique is fully wireless. For this task, the phone camera, the phone image sensor is used to retrieve the required information of a particular fruit. The processing of the digital image is done with the help of the app (Capizzi et al. 2015). The app uses the image processing procedure for the RGB framework that predicts the mellowness of the fruits step by step in details. The app also processes the image to control the lighting and the shadows of the fruits, this lighting and shadows come handy while the analysis of the fruit is made (Nguyen et al. 2014). Processing To detect the mellowness of fruits, Bee Jeng Fruit Supply Pte Ltd is following the following methods. At first, RGB inputs are taken of the given fruit (Gomes et al. 2015). Later RGB inputs are divided into R channels and B channels respectively, R and B channels are further divided into R mask and B mask respectively. After taking the R mask and B mask from the intervening masks, the intervening fruit area and intervening colour indices are analysed (Cubero et al. 2014). Lastly, the shadow area and the final mask are analysed and combined with the RGB inputs, in this way, final fruit area is obtained (Cubero et al. 2014). Thus, the mellowness of the fruits can be detected from the above process. A fruits image is taken at first and then through this vigorous process, the mellowness of fruits is checked. Budget for developing the app Cost 1.Human Resources 1.1.Developers SGD 4000 1.2.Testers SGD 1200 1.3.Managers SGD 1600 1.4.Content Writer SGD 600 2.Hardware cost 2.1.Device SGD 10000 2.2.Networking modules SGD 4000 3.Software Development 3.1.Planning SGD 750 3.2.Designing SGD 700 3.3.Development SGD 600 3.4.Implementation SGD 550 3.5.Testing SGD 1100 Total SGD 25100 Conclusion It can be concluded from the above discourse that Bee Jeng Fruit Supply Pte Ltd has done a fabulous job developing this android app, the app will not only detect the mellowness of the fruit, it will also sense the time required for a fruit to mature. With the advent of this app, the industries especially the fruit industries have been greatly benefitted, the companies can now increase their productivity. It is a nice app and is improving day by day. Bee Jeng Fruit Supply Pte Ltd is trying to add some extra features to the app so that it can be able to detect mellowness of all kinds of fruits available. References Capizzi, G., Sciuto, G.L., Napoli, C., Tramontana, E. and Wo?niak, M., 2015, September. Automatic classification of fruit defects based on co-occurrence matrix and neural networks. InComputer Science and Information Systems (FedCSIS), 2015 Federated Conference on(pp. 861-867). IEEE. Cubero, S., Aleixos, N., Albert, F., Torregrosa, A., Ortiz, C., Garca-Navarrete, O. and Blasco, J., 2014. Optimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform.Precision agriculture,15(1), pp.80-94. Developer.android.com. (2017).Android Developers. [online] Available at: https://developer.android.com/index.html [Accessed 20 Jul. 2017]. Gomes, J.F.S., de Oliveira Baldner, F., Costa, P.B. and Leta, F.R., 2015. Colorimetry and Computer Vision for Color Characterization by Image, Applied to Integrated Fruit Production. In17th International Congress of Metrology(p. 11004). EDP Sciences. Gomes-Junior, F.G., Arruda, N. and Marcos-Filho, J., 2017. Swingle citrumelo seed vigor and storability associated with fruit maturity classes based on RGB parameters.Scientia Agricola,74(5), pp.357-363. Nguyen, T.T., Vandevoorde, K., Kayacan, E., De Baerdemaeker, J. and Saeys, W., 2014, July. Apple detection algorithm for robotic harvesting using a RGB-D camera. InInternational Conference of Agricultural Engineering, Zurich, Switzerland. Wu, H., Huo, D., Jiang, H., Dong, L., Ma, Y., Hou, C., Fa, H., Yang, M., Luo, X., Li, J. and Shen, C., 2017. Highly Selective and Sensitive Colorimetric Sensor for Aminotriazole Residues in Vegetables and Fruits Using Glutathione Functionalized Gold Nanoparticles.Journal of Nanoscience and Nanotechnology,17(7), pp.4733-4739. Zhang, B., Huang, W., Li, J., Zhao, C., Fan, S., Wu, J. and Liu, C., 2014. Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review.Food Research International,62, pp.326-343.

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