Implementation Of Artificial Intelligence In Crop Monitoring For Agriculture
Keywords:
cultivating crops, farmers, Robot, frequenciesAbstract
Farmers have stopped cultivation in present days due to not being able to get the facilities which would help them in the process. On top of this, animals are causing other issues that frustrate the farmers and drive them to abandon crop cultivation; You can get our paper immune to the latter. Paper aims to scare the animal involving our fields to destroy. Wildlife intervention often leads to destruction of locally owned crops. This creates protection of yield and to protect the yield, those organisms need to be terrified — which is the only way of saying that the farmer is a worry to those organisms.” Farmers cannot constraint the entire fields and guard it. Due to time and resource constraints, it is impractical for farmers to monitor and guard whole fields by hand. Thus, there is an urgent requirement for novel, automated systems that can effectively deter animal poaching in a safe manner.
The use of such technologies allows farmers to protect crops, improve productivity, and minimize the need for real-time human intervention. This article describes an everyday application that can be used to prevent crop-damage on agriculture farms by animals. To address this problem, this research utilizes Global System for Mobile Communication (GSM) and Short Message Service (SMS) technology to present a useful, automatic system for the farmers. The proposed system approach depends on mobile telecommunication technology to send instant notification to inform farmers of a possible animal attack in their farms. This method gives instant responses by applying detection techniques along with GSM based alerts, which reduces the level of crop loss and yield security. The study endeavours to adapt the existing mobile communication infrastructure that is readily accessible, affordable, and conducive to a range of farmers in diverse locations, maximizing usability and cost efficiency. This Research paper it helps the concept of identification of a specific target by an image processing system and generating intolerable frequency than the targets audible range frequency.
Downloads
Metrics
References
IOT Solutions for Crop Protection against Wild Animal Attacks. Giordano, S., Seitanidis, I., Ojo, M., Adami, D., & Vignoli, F. (2018). IoT solutions for crop protection against wild animal attacks. 2018 IEEE International Conference on Environmental Engineering (EE). doi:10.1109/ee1.2018.8385275.
T. Utstumo, F. Urdal, A. Brevik et al., “Robotic in-row weed control in vegetables,” Computers and Electronics in Agriculture, Amsterdam, vol. 154, pp. 36–45, 2018.
P. Lottes, J. Behley, A. Milioto, and C. Stachniss, “Fully convolutional networks with sequential information for robust crop and weed detection in precision farming,” IEEE Robotics and Automation Letters. IEEE. Piscataway, vol. 3, no. 4, pp. 2870–2877, 2018
IoT-based Wild Animal Intrusion Detection System,Prajna. P, Information Science Engineering, Vivekananda College of Engineering and Technology, Puttur.
Fabio VignoliNatechSrl, Via AlgeroRosi 46 , 56100,Siena,Italyfabiovignoli@natechescape.com
Soujanya B.S , Information Science engineering ,Vivekananda College of Engineering and Technology, Puttur.
Mrs. Divya, Assistant Professor, Information Science Engineering, Vivekananda College of Engineering and Technology, Puttur. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056, Volume: 05 Issue: 03, Mar-2018 www.irjet.net p-ISSN: 2395-0072© 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 3829
A Smart Farmland Using Raspberry Pi Crop Prevention and Animal Intrusion Detection System, S. Santhiya1, Y. Dhamodharan, N E. Kavi Priya, C S. Santhosh, M.Surekha, Knowledge Institute of Technology, Salem-637504
Real Time Animal Repellent System using Image Processing, Mrs. S. Jalaja Abel Daniel T, Gowtham Shankar B, Kaliraj M,Yuthish Khanna J, Department of Electrical & Electronic Engineering ,Vel Tech High Tech Dr Sr Engineering College, Avadi, Chennai.
[10] J. L. Tang, X. Q. Chen, R. H. Miao, and D. Wang,“Weed detection using image processing under different illumination for site- specific areas spraying,” Computers and Electronics in Agriculture, Amsterdam, vol. 122, pp. 103–111, 2016.
A. van der Meulen and B. S. Chauhan, “A review of weed management in wheat using crop competition,” Crop Protection, vol. 95, pp. 38–44, 2017.
G. E. Meyer and J. C. Neto, “Verification of color vegetation indices for automated crop imaging applications,” Computers and Electronics in Agriculture, Amsterdam, vol. 63, no. 2, pp. 282–293, 2008.
T. N. Carlson and D. A. Ripley,“On the relation between NDVI, fractional vegetation cover, and leaf area index,” Remote Sensing of Environment, Amsterdam, vol. 62, no. 3, pp. 241–252, 1997.
P. Bosilj, T. Duckett, and G. Cielniak, “Analysis of morphology-based features for classification of crop and weeds in precision agriculture,” IEEE Robotics and Automation Letters, Piscataway, vol. 3, no. 4, pp. 2950–2956, 2018.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Los Alamitos, vol. 40, no. 4, pp. 834–848, 2018.
T. Kounalakis, G. A. Triantafyllidis, and L. Nalpantidis, “Weed recognition framework for robotic precision farming,” in Imaging Systems and Techniques (IST), 2016 IEEE International Conference on, 2016, pp. 466–471, Proceedings[…]Piscataway: IEEE, Chania, 2016.
M. Di Cicco, C. Potena, G. Grisetti, and A. Pretto, “Automatic model based dataset generation for fast and accurate crop and weeds detection,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5188–5195, Proceedings[…]. IEEE. Piscataway, Vancouver, 2017.
A. Milioto, P. Lottes, and C. Stachniss, “Real-time semantic segmentation of crop and weed for precision agriculture robots leveraging background knowledge in cnns,” in IEEE International Conference on Robotics and Automation (ICRA), pp. 2229–2235, Proceedings[…]. IEEE. Piscataway, 2018.
Svitak, Amy, Cost of NASA's Next Mars Rover Hits Nearly $2.5 Billion, http://www.space.com/10762-nasa-mars-roveroverbudget.html, retrieved 2011-02-03.
Wesley T. Huntress JR., Mikhail Ya Marov, Soviet Robots in the Solar System: Mission Technologies and Discoveries, Springer, 2011.
NASA Jet Propulsion Laboratory, Mars Exploration Rovers, http://marsrover.nasa.gov/home/index.html, retrieved 2011-12-16.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
