Harnessing the Power of Sensors and Machine Learning to Design Smart Fence to Protect Farmlands

electronics
Article
Harnessing the Power of Sensors and Machine Learning to
Design Smart Fence to Protect Farmlands
Preetam Suman
1,
*, Deepak Kumar Singh
2,
*, Fahad R. Albogamy
3
and Mohammad Shibee
4







Citation:
Suman, P Singh, D.K.;
Albogamy, FR Shibee, M.
Harnessing the Power of Sensors and
Machine Learning to Design Smart
Fence to Protect Farmlands.
Electronics 2021, 10, 3094. https://
doi.org/10.3390/electronics10243094
Academic Editors Imad Rida, Lunke
Fei, Dan Istrate and Arturo de la
Escalera Hueso
Received: 12 October Accepted 8 December Published 13 December 2021
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Attribution (CC BY) license (https://
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4.0/).
1
Vellore Institute of Technology, Kothrikalan, Bhopal 466114, Madhya Pradesh, India
2
Jaipuria Institute of Management, Lucknow 226010, Uttar Pradesh, India
3
Turabah University College, Taif University, PO. Box 11099, Taif 21944, Saudi Arabia;
f.alhammdani@tu.edu.sa
4
Industrial Engineering Department, Taif University, PO. Box 11099, Taif 21944, Saudi Arabia;
ms.husain@tu.edu.sa
*
Correspondence:preetam.suman@vitbhopal.ac.in (PS d.singh@jaipuria.ac.in (D.K.S.)
Abstract:
Agriculture and animals are two crucial factors for ecological balance. Human–wildlife conflict is increasing day-by-day due to crop damage and livestock depredation by wild animals,
causing local farmer’s economic loss resulting in the deepening of poverty. Techniques are needed to stop the crop damage caused by animals. The most prominent technique used to protect crops from animals is fencing, but somehow, it is not a full-proof solution. Most fencing techniques are harmful to animals. Thousands of animals die due to the side effects of fencing techniques, such as electrocution. This paper introduces a virtual fence to solve these issues. The proposed virtual fence is invisible to everyone, because it is an optical fiber sensor cable, which is laid inches- deep in soil. A laser light is used at the start of the fiber sensor cable, and a detector detects at the end of the cable. The technique is based on the reflection of light inside the fiber optic cable. The interferometric technique is used to predict the changes in the pattern of the laser light. The fiber cable sensors are connected to a microprocessor, which can predict the intrusion of any animal. The use of machine learning techniques to pattern detection makes this technique highly efficient. The machine learning algorithms developed for the identification of animals can also classify the animal.
The paper proposes an economical and feasible machine-learning-based solution to save crops from animals and to save animals from dangerous fencing. The description of the complete setup of optical fiber sensors, methodology, and machine learning algorithms are covered in this paper. This concept was implemented and regressive tests were carried out. Tests were performed on the data, which were not used for training purposes. Sets of people (50 people in each set) were randomly moved into the fiber optic cable sensor in order to test the effectiveness of the detection. There have been very few instances where the algorithm has been unable to categorize the detections into different animal classes. Three datasets were tested for configuration effectiveness. The complete setup was also tested in a zoo to test the identification of elephants and tigers. The efficiency of identification is for human, 80% for tiger, and 75% for elephant.