Agricultural Informatics. Группа авторов

Чтение книги онлайн.

СКАЧАТЬ SD BeagleBone Black Winner Operating Systems Angstrom (Default), Ubuntu, Android, Arch Linux, Gentoo, Minix, RISC OS Raspbian (Default), Ubuntu, Android, Arch Linux, Fedora, RISC OS Tie Power Draw 210–460 mA @ 5V 150–350 mA @5V Raspberry Pi Winner GPIO Capability 65 Pins 8 Pins BeagleBone Black Winner Peripherals 1 USB Host, 1 Mini-USB Client, 1 10/100 Mbps Ethernet 2 USB Hosts, 1 Micro-USB Power, 1 10/100 Mbps Ethernet, RPi Camera Connector Tie

       2.7.2 Ease of Setup

      Raspberry Pi bit Laborious whereas BeagleBone Black as simple as it gets.

      Winner: BeagleBone Black.

       2.7.3 Connections

BeagleBone Black	Raspberry Pi	Result
3 I2C Buses	1 I2C Bus	BeagleBone Black Winner
CAN Bus	1 SPI Bus
SPI Bus	8 GPIO Pins
4 Timers	1 UART Interface
5 Serial Ports
65 GPIO Pins
8 PWM O/P
7 Analog Inputs

       2.7.4 Processor Showdown

      BeagleBone Black is nearly 2 times as fast as Raspberry Pi.

      Winner:BeagleBone Black.

       2.7.5 Right Choice for Projects

BeagleBone Black	Raspberry Pi
1. Projects that need to interface plenty of External Sensors.	1. Multimedia based Projects.
2. High Speed Processing.	2. Community Driven.
3. Commercialization Projects.	3. Graphical Learning Platform
4. Embedded System Learning	4. Internet Connected Projects.

2.8 Conclusions

The proposed integrated Agro-IoT system tends to integrate the earlier problems faced in the previous smart farming as well as tried to find out the solution for the same. Thus all the traditional monitoring problems faced by one farmer get solved by getting together in a one integrated agro-IoT system. The researchers have succeeded in reaching their objective and they made the whole system efficient for the upcoming modern farming era.

2.9 Future Work

      The prototype model of the proposed system can be built up in real farming area to replace the traditional monitoring system of farmers in the field. With the advancement of upcoming technology, the agro-IoT system can be more advanced in future.

References

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      2. Zhu, Y., Song, J., Dong, F., Applications of wireless sensor network in the agriculture environment monitoring. Procedia Eng., 16, 608–14, Jan, 2011.

      3. Jaishetty, S.A. and Patil, R., IoT sensor network based approach for agricultural field monitoring and control. IJRET: Int. J. Res. Eng. Technol., 5, 06, Jun, 2016.

      4. Keerthi.v and Kodandaramaiah, G.N., Cloud IoT Based greenhouse Monitoring System. Int. J. Eng. Res. Appl., 5, 10, (Part—3), 35–41, 2015.

      5. Srisruthi, S., Swarna, N., Susmitha Ros, G.M., Elizabeth, E., Sustainable Agriculture using Eco-friendly and Energy Efficient Sensor Technology. IEEE International Conference On Recent Trends In Electronics Information Communication Technology, May 2016.

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      8. Satyanarayana, G.V. and Mazaruddin, S.D., Wireless Sensor Based Remote Monitoring System for Agriculture using ZigBee and GPS. Conference on Advances in Communication and Control Systems, 2013.

      9. Sakthipriya, N., An Effective Method for Crop Monitoring Using Wireless Sensor Network. Middle East J. Sci. Res., 20, 9, 1127–1132, 2014.

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      11. Baggio, A., Wireless Sensor Networks in Precision Agriculture, in: ACM Workshop Real-World Wireless Sensor Networks, 2005.

      12. Kavitha, T., Preethi, D.L., Saranya, S., Evert, P.J.A., Realizing IoT based real time monitoring and controlling system. i-manager’s J. Comput. Sci., 4, 4, 20–24, 2017.

      13. George, T., Bagazonzya, H., Ballantyne, P., Belden, C., Birner, R., Castello, R.D., Castren, T., Choudhary, V., Dixie, G., Donovan, K., Edge, P., ICT in agriculture: Connecting smallholders to knowledge, networks, and institutions. The World Bank, Nov 1, 2011.

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