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IOT-Based Farmland Intrusion Detection System 

 
Emmanuel Onwuka Ibam1, Olutayo K. Boyinbode2, Helen O. Aladesiun3 

1,2,3 School of Computing, Federal University of Technology Akure, Nigeria  

eoibam@futa.edu.ng, okboyinbode@futa.edu.ng, olamipositosin@gmail.com 

  

 

Abstract  

As crop vandalization with conflicts between farmers and herdsmen become recurrent in Nigeria, 

existing farm intrusion prevention methods such as fence mounting and placement of farm guards can 

no longer guarantee farm security. This is because intruders either jump over the fence or attack guards 

on duty without visual evidence. Therefore, a complementary approach using computer technologies 

for effective detection is required. This paper presents an IoT-based farm intrusion detection model 

using RFID and image recognition technology. RFID sensor as well as cameras are placed at entrances 

of a fenced farmland for simultaneous identification. The sensor reads workers’ tags for identification, 

while cameras capture images of users for further identification as captured images are sent to 

Convolutional Neutral Network (CNN) for recognition. A user whose image cannot be recognized is 

flagged as an intruder and an intrusion alert with visual evidence is sent to the farm owner. The system 

showed a high level of effectiveness with an accuracy of 90%, Precision of 70%, and 80% Recall rate 

and effectively controlled the rate of illegal encroachment into farmland 

Keywords:  Buzzer; Convolutional Neural Network (CNN); Internet of Things (IOT); Microcontroller;   

PIR Sensors; Radio Frequency Identification (RFID) 

 

Abstrak 

Perusakan tanaman dengan konflik antara petani dan penggembala menjadi berulang di Nigeria, 

metode pencegahan intrusi pertanian yang ada seperti pemasangan pagar dan penempatan penjaga 

pertanian tidak dapat lagi menjamin keamanan pertanian. Ini karena penyusup melompati pagar atau 

menyerang penjaga yang bertugas tanpa bukti visual. Oleh karena itu, diperlukan pendekatan 

pelengkap menggunakan teknologi komputer untuk deteksi yang efektif. Makalah ini menyajikan model 

deteksi intrusi pertanian berbasis IoT menggunakan teknologi RFID dan pengenalan gambar. Sensor 

RFID serta kamera ditempatkan di pintu masuk lahan pertanian berpagar untuk identifikasi simultan. 

Sensor membaca tag pekerja untuk identifikasi, sementara kamera mengambil gambar pengguna untuk 

identifikasi lebih lanjut saat gambar yang diambil dikirim ke Convolutional Neutral Network (CNN) 

untuk dikenali. Seorang pengguna yang gambarnya tidak dapat dikenali ditandai sebagai penyusup 

dan peringatan intrusi dengan bukti visual dikirim ke pemilik tambak. Sistem menunjukkan tingkat 

efektivitas yang tinggi dengan akurasi 90%, Presisi 70%, dan tingkat Recall 80% dan secara efektif 

mengendalikan laju perambahan ilegal ke lahan pertanian. 

Kata kunci:   Buzzer; Jaringan Syaraf Konvolusional (CNN); Internet of Things (IOT); Mikrokontroler;  

Sensor PIR; Identifikasi Frekuensi Radio (RFID) 

 

I. Introduction 

Virtually every country in the world relies on agriculture to survive, not just because it is a source 

of food but it is also connected to the production of most basic human needs. In Nigeria, agriculture 

remains the leading non-oil sector of the country’s economy, providing about 70% of the nation’s 

population with jobs. It is a major source of livelihood for those in rural areas as they depend on the 

proceedings from their farm harvest to cater for their family.  

P-ISSN : 2715-2448 | E-ISSN : 2715-7199 
Vol.4 No.2 July 2023 
Buana Information Technology and Computer Sciences (BIT and CS) 



 

Vol. 2, No.2 | 40  

 

However, the sector has suffered a major setback with persistent cases of intruders; human and 

animal alike causing serious damages to crops by stealing, eating, and trampling on crops. In recent 

times, the destruction of crops has aggravated with the incessant conflicts between herders and farm 

owners arising from illegal encroachment of cattle into farmlands. Usually, mounting of fences round 

farmland, engaging farm guards, use of repellants, among others are used as means to wade off invaders. 

Though relatively effective, these methods can be less than ideal and are sometimes prohibitively 

expensive to put in place [3].  Moreover, with the herders- farmers conflict, the existing security 

measure cannot completely guarantee safety as several farm guards have been killed and fence been 

jumped over without visual evidences. In order to overcome these challenges, there is need to build a 

system that will alert farmers of any intruder and also repel animals away from the farm. The aim of 

this paper is to detect and prevent intruders from entering into the farm land by implementing an IoT 

based intrusion detection model for farmlands using RFID technology and image recognition technique. 

Radio Frequency Identification technology is a technology which uses radio waves to automatically 

identify people or objects. Currently, this technology is dramatically increasing the use of wireless 

technology in many areas such as healthcare, transport, military, textile, and agriculture. Unlike barcode 

systems, RFID systems reflect better result in industrial applications like baggage tracking, 

access/vehicle control, animal tracking, etc. Apart from traditional usage of RFID technology, another 

innovative research path of RFID is integrating RFID technology with mobile devices such as mobile 

phones and PDAs [14].  

This automation can provide accurate and timely information without any human intervention, 

access to such information where one can individually identify each one of the tagged items uniquely; 

help in improving your processes and also to make informed decision. Radio Frequency Identification 

(RFID) technology is an automatic identification system consisting of a tag and a reader which can 

communicate with radio waves [11]. In this way, a lower costs more efficient process management and 

monitoring is provided.   

The remaining of this paper is organized as follows: Section 2 provides the related works. Section 

3 describes the proposed system architecture and the methods applied to actualize the system. Section 

4 explains how the system was implemented, while Section 5 provides the evaluation of the system. 

Section 6 presents the conclusion. 

 

II. Related work 

[15] carried out a Survey on Animal Detection Methods in Digital Images and observed that animal 

in image processing have been an important field to numerous applications. They narrowed the 

applications to three main branches, namely detection, tracking and identification of animal. They 

identified the efficacy of applying computer vision in image processing for animal detection, use of 

transformation function such as the Fourier transform, Face Detection Approach and Thresholding 

Segmentation Method. They identified lighting problem and changes of natural environment from day 

to night at outdoor surveillance system as two  problems that need to be considered in developing an 

animal detection algorithm.  

There are copious numbers of researches undertaken to improve the old system of securing 

farmlands. [5], a smart farmland using raspberry pi for crop vandalization prevention and intrusion 

detection system was developed. They showed how Passive Infrared Sensors (PIR) was used to detect 

motion of human body, once the employed PIR sensors detect motion the cameras capture an image 

and start recording the video, the owner of the farmland gets notified about the intrusion. This 

information along with the captured video is stored onto cloud from where the administrator /farm 

owner can access it once he receives the message. RFID tags was used to differentiate between the 

authorized person and the intruders, if the person is an authorized one then no action is taken by the 

system. System uses two mechanisms to ward off animals namely: the rotten egg spray and electronic 



Vol. 4, No.2 July 2023  | 41  

 

firecrackers. Their system requires no human supervision, hence saves a lot of time and energy. The 

system works in real time to detect the animals in the field, in addition the farmers can access the view 

of their fields remotely. However, the use of fire cracker can cause damages to crops, while rotten eggs 

spray can result in crop diseases.  

[12] worked on an animal detection system using ultrasonic sensors to detect the movement of the 

animal and send signal. This signal is transmitted to GSM and which gives an alert to farmers and forest 

department immediately. However, the system can only detect the animals but cannot prevent them 

from destroying the crops.  

Kaluti et al. (2018) developed an IoT Based Wireless Sensor Network for easy detection and 

Prevention of wild animal’s attack on farming lands. In their study, they showed how sensors such as 

PIR motion sensor, sound recognizing sensor, and web cameras were used to collect data. Aurdino 

mainly the raspberry pi, acts as master node and collects the data from the sensors which sends those 

data to the server for further processing. The server processes the data and sends signals to the speakers 

to produce sound in order to stop the animals by crossing the forest boarder, and also sends message to 

the mobiles of the nearer villagers, farmers, and the forest office to take the safety precautions. Their 

system was helpful to the farmers in protecting fields and save them from financial losses and also saves 

them from unproductive efforts that they endure for the protection of their fields. However, in this work 

the stealing of RFID tags can give intruders access into the farmland.  

[6], worked on human-animal conflict using PIR Sensors and camera as first round of security 

where the animal movement is detected using the sensor and the sensor in turn triggers the camera to 

take the picture of the animal and transmit the image for processing via microcontroller i.e., through 

WSN. The microcontroller transmits the image from the camera to the PC in the command center where 

the image processing and classification of animal is done. Once the animal is found to be a threat the 

PC will send the signal to the repellent system via microcontroller to take appropriate action. However, 

the system results in crop diseases.  

In Cherukat et al., (2014), a farm field protection using sensor networks, due to the fact that fields 

nearer to the forests were facing problem of attack of wild animals on the crops. To this effect, they 

designed a smart field that is on low cost, low energy consuming, small sensor nodes. They showed 

how sensor nodes are deployed in groups, this group of nodes is connected to a common node and 

common node to the main node. The three parts of sensor deployment is primary, secondary and tertiary. 

The primary node controls the network, secondary nodes passes data from tertiary to primary node. By 

including more sensor nodes more farm areas can be covered. Except the node in primary all other 

nodes are connected with PIR (Passive Infrared) sensor. Their system helped to protect farmlands from 

wild animals. It was a self-managing, cost effective and energy efficient system. However, any failure 

in one of the nodes results in failure of the whole system.  

Koik et al., (2016), in their research, did a comprehensive analysis on Animal Detection Methods 

in Digital Images. The study was carried out in order to design a system that uses digital image 

processing for animal detection. They showed how digital image processing system was built up by the 

use of power spectral in trying to test animal presence in the image, Fourier transform by transforming 

from spatial domain to frequency domain, Animal Detection Based on Thresholding Segmentation 

Method in which if the threshold is greater than a pixel of gray that value is set to white and others are 

set to black. The system helped to detect the exact animal that entered the field. However, this work 

cannot be suitable for fast detection of animals.  

[8] developed an intruder recognition in a farm through wireless sensor network, this came as a 

result of the struggle farm owners go through for top yield in varied ways after which, their yield may 

be curtailed due to the interference of animals and unauthorized humans. Due to this, many farmers 

sleep in field area to save their crops risking their lives if wild animals attack their fields. Animal attack 

on the crops could also cause infections to the buyer  when the crops are sold in the market due to the 



 

Vol. 2, No.2 | 42  

 

animal poison. Hence, it is much essential to monitor the boundaries of the farm to discover movement 

of unauthorized entries into the farm. They therefore aimed at designing a WSN in border surveillance 

and intrusions detection that is cheaper for the farmer. They showed how the system is implemented to 

detect intrusion of animals in farms using wireless sensors and buzzers which detects the animals and 

produce acoustic sounds. At various locations around the farm, motion sensors are placed where certain 

distance is maintained between them and one of the motion sensors is made as the centralized from 

where we can operate all other sensors. The sensors which are present frequently sense the movement 

and pass it to the Coordinator through RFID. An Arduino board is placed near the centralized sensor to 

which GSM module is interfaced along with buzzers and RFID transmitter. Animals are being detected 

by the motion sensors in the agricultural area. When an animal or human is being detected by the sensors 

in the agricultural area, the sensors are activated through RFID transmitter and the system produces 

sounds through the buzzer and will give a very minor shock to the animal. This sound irritates the 

animals and they cannot accommodate it at that place and due to minor shock animals will fall. 

However, the system was not able to provide video processing.  

[2], Animals from wild area were continuously attacking crops for so many years and the 

protection of these crops field from wild animals was a serious issue. The wild animals face shortage 

of water and food as a result of which they move towards the agriculture area which creates great loss 

to the crops and annual income of farmers. When wild animals enter in a farm there is a need for an 

alert system to prevent crops from being damaged by wild animals. The developed a system and their 

objective was to prevent the loss of crops and protect the area from intrusion of wild animals which 

causes major damage to the agricultural area. Their system has a wireless sensor network (WSN) 

consisting of a large number of autonomous sensors to cooperatively monitor physical or environmental 

conditions, such as temperature, sound, vibration, pressure, motion or pollutants. The WSN consists of 

various clusters connected with the sink node. Each cluster has number of sensor nodes having one 

Master node capable of collecting the data from remaining nodes, web camera and GSM connected to 

the raspberry pi kit. Camera is used to detect the motion of wild animal and ones it gets detected it 

captures its image and distinguishes its features as dangerous or not, if it is dangerous then it sends 

instant message to the farmer. It saves farmers from unproductive efforts that they endure for the 

protection of their fields. However, the system is expensive and require high maintenance.  

[1], implemented an intelligent security system for farm protection from wild animals in order to 

provide food requirements of the people and produce several raw materials for industries as animal 

interference in agricultural lands has resulted into huge loss of crops. Hence, they developed a 

prohibitive fencing to the farm, to avoid losses due to animals. They showed how fencing wire is used 

as a sensor. When animals come in contact with this open cable the circuit will be grounded and we get 

initial input signal that indicates presence of animals at fencing. After getting that initial input signal 

followed by amplifier circuit passing it for further processing, then, it will be given to the 

microcontroller and their system will be activated, immediately buzzer will be on, at night time, flash 

light will be on and message will be sent to the farmer. Continuous monitoring can be done because it 

works on Solar panel. However, damages still occurred during storms, thunder and lightning which is 

still a risk of dangerous shocks to farmers.  

Therefore, the development of an Internet of Things farmland intrusion detection system with 

automatic facial recognition module for effective security and safety of both crops and farm owners 

aimed at tackling the shortcomings pointed in the reviewed literatures has become imperative. 

 

III. Methods 

Based on the proposed system architecture in Figure 1, the RFID reader, camera, and Buzzer are 

all connected to a microcontroller placed at the entrance of the farmland. The RFID tags unique 

identification number is configured with the staff information stored in the database.  At the entry point 



Vol. 4, No.2 July 2023  | 43  

 

into the farm, the tag is brought closer to the reader for staff identification as the staff tag is cross-

checked with the staff information in the database. While confirming staff ID, the microcontroller will 

activate the camera to capture facial image of staff. The image is then fed into face recognition module 

for further prove of identification. This process is carried out simultaneously to prevent intruders using 

authorized tag into the farmland.  If any of these actions should fail, an intruder alert message is sent to 

the administrator’s system or the farmer’s smart phone, and the buzzer will produce a loud irritation 

noise. 

 

Figure 1. IOT-Based Farmland Intrusion Detection System Architecture. 

 

The The detection of an authorized person with a valid RFID is estimated using the frequency of 

signal reading received by the reader. The signal power measured at the receiving node (RFID reader) 

is known as RSSI (Received Signal Strength Indicator), the RSSI received is translated from reader’s 

antenna into frequency of signal reading which is displayed by the reader. The RSSI transmission power 

for the person is expressed mathematically as: 

 

𝑝(𝑅) = 𝑝(𝑇) − 10𝑛log(
𝑅

𝑇
) − 𝐵  (1) 

 

𝐵 =  {
𝑞 ∗ 𝑝        𝑖𝑓 𝑞 < 𝑐

𝑐 ∗ 𝑝        𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 
    (2) 

 

where n is the attenuation factor, p(T) is the signal power at the reference distance T, R is the 

distance between the transmitter and the receiver for the tag, q is the number of obstacles between the 

transmitter and the receiver,  p is the attenuation factor of the farm, and c is the maximum number of 

obstacles between the transmitter and the receiver.  The signal strength is converted into frequency of 

signal from the reader’s antenna and displayed by the reader. The transmission frequency increases with 

proximity of the RFID reader with the reference tag.  The signal power in RSSI is converted to distance 

by using the Euclidean equation which is stated as: 

 

𝐸 =  √∑ (𝐴𝑛 −  𝐵𝑛)2𝑁
𝑛=1            (3) 

 



 

Vol. 2, No.2 | 44  

 

where E is the relative position of the reference tag A and unknown tag B, A_n represents the 

signal strength of the reference tag, B_n is the signal strength of the unknown tag received on the reader, 

N is the number of times the measurement is taken.  

However, the microcontroller activates the camera to take snap shot of the person. Captured 

images were preprocessed by way of image value normalization and feature extraction.   Image value 

normalization involves bringing images into a range of into a range of intensity value that is normal. 

This is achieved using equation (4) 

 

𝑜𝑢𝑡𝑝𝑢𝑡𝑐ℎ𝑎𝑛𝑛𝑒𝑙 = 255 ∗
(𝑖𝑛𝑝𝑢𝑡𝑐ℎ𝑎𝑛𝑛𝑒𝑙−𝐼𝑚𝑖𝑛)

(𝐼𝑚𝑎𝑥−𝐼𝑚𝑖𝑛)
         (4) 

 

where 〖output〗_(channel )is the normalized image, input_channel  is the image to be 

normalized, I_max is the maximum pixel value, and I_min is the minimum pixel value. Thereafter, 

global features were extracted from normalized image using principal component analysis (PCA). PCA 

involves computing the eigenvalues λ and eigenvectors μ of the data correlation matrix C =X^T X, 

where X is an n*q data matrix of n number of samples and z features using equation (5):  

 

(𝐶 − 𝐼𝜆𝑖)𝜇𝑖 =  0 | 𝑖 =  1,2,3, … , 𝑞             (5) 

 

where q is the total number of eigenvalues. The k numbers of eigenvectors μ having the largest 

eigenvalues λ were picked as principal components (PCs) based on a given threshold value as defined 

in equation (6) 

 

∑ 𝜆𝑖
𝑘
𝑖=1

∑ 𝜆𝑖
𝑞
𝑖=1

∗ 100 > 𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑     (6) 

 

where threshold value specifies the percentage of information to be retained,  k∈q represents the 

number of eigenvalues whose corresponding eigenvectors to be retained. PCA extracted features were 

fed into CNN model for recognition.  CNN takes the preprocessed input image vector x=(x_1, 

x_2,x_3,…,x_n  ) with an assigned class c_l which are the images of farm staff. At the convolutional 

layer, images are convolved to extract features that forms feature maps f_m using equation (7) 

 

𝑓𝑚= 𝑡𝑎𝑛ℎ(𝑤𝑥𝑖 + 𝑏)       (7) 

 

Where b represents the bias term, w represents weight matrix, and  x_i is the input vector. 

Thereafter, the feature maps f_m is passed to the max-pool layer where max-pooling operation is 

applied to each feature map to obtain the most significant features by selecting features with the 

maximum value as expressed in equation (8) 

 

                            𝑓𝑚 = max {𝑓𝑚}                (8) 

 

where f_m represents the downsized features. The obtained features f_m is fed to the fully 

connected layer which contain the softmax function that will classify the image data as intrusive or non-

intrusive as in the equation 9. 

 

𝑦 = 𝑠𝑜𝑓𝑡 max(𝑤𝑜𝑓𝑚 + 𝑏𝑜)    (9)  



Vol. 4, No.2 July 2023  | 45  

 

 

Where y represents the output, w_o  represents the output weight, and b_o  represent the output 

bias. If the recognition model give a recognition rate below 70%, it triggers an irritation alarm and 

subsequently send alert to the administration’s system (base station) and the owner’s smart phone 

 

IV. Results and Discussions 

This section presents the implementation of the Internet of Things farm land intrusion detection 

system, using RFID technology and image recognition technique to prevent unauthorized person into 

the farmland, and a safe defensive mechanism using irritation noise to scare away intruders. RFID 

reader was mounted at the entrance of a farm to read the tags of farm workers for identification purpose. 

The microcontroller, the RFID reader, tags, the buzzer, the camera and application software which 

includes the user interface design. The tools used in the development of the software part are as follows: 

• Personal computer with 64 bit Operating System and Processor Intel® of 2.20GHz. 

• PHP, MySQL, HTML, CSS and JavaScript programming languages. 

• XAMPP Apache Local Server. 

• Python Programing language for the image recognition algorithm. 

A.  User Interface Design 

 A user interface is the portion of a program with which a user interacts with the computer system. 

The user interface of the farm land intrusion detection system contains: 

1.  Login Page 

This is the default page that appears when the application is launched. This page enables the farm 

owner to access its features. Figure 2 shows the system interface. The system contains three major parts, 

farmers login, farmers face identity prediction, farm worker enrolment. They are shown below:can be 

presented with tables or figures. Results and discussions must also interconnect with theory that used. 

Avoid excessive use of citations and discussion of published literature 

 

 
Figure 2. User Interface Design 

 



 

Vol. 2, No.2 | 46  

 

 

Figure 3. Signing in of a farm owner 

2. Farmer’s Face Identification Prediction 

The image identity checking module contains an image upload module for uploading an image 

which would be used to check if the face is authorized to enter into the farm. The module activates 

when an object or a person attempts to enter into the farmland via the entrance points. This is to prevent 

the possibility of an intruder using a worker’s tag. Figure 5 shows the identity checking module.  

 

 

Figure 5. Identity checking interface 

 

3.   Farm Worker Enrolment 

The system takes the required data and save in the database, the system wait for the farm owners 

to click on proceed and then put the RFID tag close to the RFID reader. Figure 6 shows the unique 

RFID tag number and the login details to be provided by the farm owners.  



Vol. 4, No.2 July 2023  | 47  

 

          

Figure 6. Registration of a farm owner 

 

Figure 7 shows the unique RFID tag number and the login details to be provided by the farm 

owners: 

 

 
Figure 7. Registration of a farm owner 

 

Figure 8 shows the data of the farm owner has been successfully uploaded. 
 



 

Vol. 2, No.2 | 48  

 

 
Figure 8. Successful of a farm owner 

 

4.     Face Recognition with Camera 

Captured images will undergo different pre-process steps such as image value normalization, 

image enhancement, and feature extraction.  

 

 
Figure 9. Sample of pre-processed images 

 



Vol. 4, No.2 July 2023  | 49  

 

 
Figure 10. Scree Plot for PCA 

 

These are the output of the pre-processed images 

 
Figure 11. PCA Reduced Images 

Figure 12 Shows the face recognition operations indicating authorized and unauthorized farm 

owners. 

 

 

 

 



 

Vol. 2, No.2 | 50  

 

 
Figure 12. Face Recognition Operation 

 

 

Figure 14 shows a layout of the system database showing farm owners that is been recognized by 

the system. 

 
Figure 14. A layout of the system database 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



Vol. 4, No.2 July 2023  | 51  

 

Figure 15 Shows typical messages sent to the Farm owner’s mobile phone 

 

 
Figure 15. Sample of received message on farm owner’s mobile phone 

 

5.     Evaluation 

Confusion matrix table (shown in Table 1) was used in this study to describe the performance of 

the recognition module on a set of test data for which the true values are known. In this study, 20 image 

test data were used of which 15 were authorized farm workers and 5 were regarded as intruders. The 

true positive (TP) represents the number of correctly detected intruders. True negative (TN) represents 

number of correctly recognized farmers. False positive (FP) represents the number of farmers that were 

incorrectly recognized as intruders, while false negative  (FN) represents intruders that were incorrectly 

recognized as farmers. 

 

Table 1. Confusion Matrix of system recognition module 

 Prediction Class 

Intruder Farmer  

  Actual  

Class 

   

Intruder 4 1 

Farmer 2 14  

 

𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =
4 + 13

4 + 12 + 2 + 1
= 0.9 

 

 

𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =  
4

4+2
  = 0.7 

 



 

Vol. 2, No.2 | 52  

 

Recall =  
4

4+1
 = 0.8 

 

The system shows a high level of effectiveness with an accuracy of 90%, Precision of 70%, and 

80% Recall rate. 

 

6.     System Performance and Evaluation 

Table 2 shows the comparison of recognition accuracy score of the developed system with existing 

farm intrusion detection systems, while Table 4 depicts the comparison of the developed system with 

existing system using other metrics such as efficiency, technology used, operations performed by the 

system and platform used. 

 

Table 2. Comparison of Recognition Rate of Developed System with Existing Systems 

 

Performance Metrics  

Vinaya et al 

(2018) 

Saieshwar 

et al. 

(2018) 

Sachin et 

al., (2017  
Developed 

System. 

  Accuracy 87.35% 54.32% 82.5% 90% 

 

Figure 17 is the chart showing the accuracy of the previous work and developed work. 

 

 

Figure 17. Accuracy Chart 

 

V. Conclusion 

This research has established an effective farmland intrusion detection system using RFID 

technology and image recognition technique to prevent unauthorized person into the farmland, and a 

safe defensive mechanism using irritation noise to scare away intruders. RFID reader was mounted at 

the entrance of a farm to read the tags of farm workers for identification purpose, therefore, the system 

proceeded by taking face images of the workers for further identification. The capture images were 

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

Vinaya A (2018) Saieshwar (2018) Sachin (2017) Developed research
system.



Vol. 4, No.2 July 2023  | 53  

 

preprocessed and salient features were extracted using PCA algorithm. Extracted features were fed into 

the recognition module driven by CNN for recognition. The system was implemented using 

programming language, and other packages such as flask 1.0, PyTorch, MySQL. The result of the 

system shows a recognition score of 90% accuracy, precision of 70% and 80% recall rate, while existing 

system shows the accuracy of 87.35%, 54.32% and 82.5% respectively.    Hence, with the objective of 

developing an IoT farmland intrusion detection system using sensors and CNN, this research therefore 

has succeeded in controlling the rate of illegal encroachment into farmer’s farmland. 

 

 

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