Harnessing the Power of Sensors and Machine Learning to Design Smart Fence to Protect Farmlands
Figure 25.Flow chart of the combined system.5. Results
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| Figure 25. Flow chart of the combined system. 5. Results The complete setup of the optical fiber cable sensor has been tested in a regressed manner. The testing was performed for the identification of humans, tigers, and elephants. The identification of humans was performed in the farms where a complete setup was deployed. Three sets of humans (50 person in each set) walked randomly on the deployed setup. The persons used for testing was not included for the data collection for training. The second test was performed for the identification of elephants, because many farms are destroyed by elephants. Three pet elephants were rented and tested on the same setup that was used for humans. All four elephants crossed the setup in a random manner (single elephants and in groups. A total of 85 elephant crossings were tested. The third test was performed for tigers in the zoo. The setup was deployed in the movement area of the tigers. Three different tigers were selected for the testing purpose. A camera was placed for ground truth verification and to count the crossings of the tigers. The setup was fixed for 15 days, where 115 crossings of tiger was recorded. There have been very few instances where the algorithm has been unable to identify the type of animal. The efficiency of the result is shown in Table, and we consider them quite promising. Electronics 2021, 10, 3094 19 of 20 Table 5. Test results for three datasets. Recognition % Recognition % Recognition % (Dataset 1) (Dataset 2) (Dataset 3) Human 96.5 96.5 Tiger 80.7 Elephant 75.0 75.0 6. Conclusions Fences are incredibly important for the security of farmland. Wild animals cause damage to crops, which causes damages to the finances of farmers. Every farmer builds fences before they begin farming. Farmers depend on many kinds offences. A few of them are truly dangerous to animals. The unsafe use offences kill animals relatively frequently— according to records gathered by a few organizations, a large number of elephants, cows, and other animals die every year. In this paper, we proposed a virtual fencing using a fiber optic sensor. The virtual fence is smart enough to identify the animals. Following the identification of the animals, it can send a message to the owner of the farm and generate an alarm. The smart fence presented in the paper used machine learning algorithms that are effective in identifying signals unique to humans, elephants, and tigers. Other than identification of specified animals, the smart fence is currently able to recognize the movement of anyone around the farmland. The complete configuration of the optical fiber cable sensor was tested very regres- sively. Tests have been carried out to identify humans, tigers, and elephants. Human identification was conducted at farms where the full configuration was deployed. Three sets of humans (50 persons in each set) were moved randomly on the deployed configuration. The individuals used for the tests were not taken into account for the collection of training data. The efficiency of the system is 94–96% for identification of humans. A second test was carried out to identify elephants because plenty of farms are destroyed by elephants. Three pet elephants were hired on the same configuration that was used for humans. All four elephants were randomly intersected (one elephant and in groups. There were 85 elephant crossings tested. The efficiency of the system is 75% for identification of elephants. The third test was carried out on tigers at the zoo. The configuration has been deployed in the tiger’s motion zone. A camera was placed to check the truth on the field and count tiger passes. Three different tigers were used for testing purposes. The facility was repaired for 15 days, while 115 crossings on the tiger were recorded. The efficiency of system is for identification of tigers. Very few cases were found where the algorithm was unable to identify the type of animal. The efficiency of the result is shown in Table, and are promising. This setup will be very useful for farmers they can save their crops without harming animals. The future research will be focused on identifying more animals and the development of a deterrent fence to repel animal incursions into the farm. The deterrent fence will consist of drum noises, honeybee attacks, or smells disliked by animals. The smart fence can be further developed into a closed area monitoring system. In the future, the results will also be gathered for different ambiance and temperatures. Download 2.4 Mb. Share with your friends:
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