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Smart farming transforming Agriculture by ICT technologies including AI, Robotics, IoT and Network technologies

Agriculture plays a fundamental role in the world both as a key source of livelihood and its role in the global food supply chain. It is a foundation of human survival.

Agriculture is a multibillion-dollar industry and one of the largest in the world, accounting for almost 1% of GDP in the UK, 6% in the US and 12% in Australia.



The agriculture is currently experiencing major changes and is facing significant challenges including such as population growth, climate change, expansion of industrial development, land degradation, environmental and regulatory concerns.


According to UN calculations, by 2050 there will be 9.7 billion people in the world, in other words around 2 billion more mouths to feed than in 2020. This increase, according to FAO — the UN food and agriculture agency —, needs to be met through a 70 % rise in agricultural production, involving an additional quantity of nearly 1 billion tonnes of cereals and 200 million tons of meat.


Population growth will also strain the world’s water supply, with some experts estimating that 57% of the world’s population will live in water-scarce areas by 2050. To make matters worse, around 25% of the world’s arable land has been degraded and requires significant restoration before it can produce crops at maximal efficiency.



The food industry is currently responsible for 30 % of the world’s energy consumption and 22 % of greenhouse gas emissions. The production of the chemical industry for agriculture is extremely energy intensive. The challenge, therefore, is not just producing more food, but doing it sustainably.

In the European Union, agriculture is under strong regulatory and legal pressure, e.g. control of the use of fertilizers and pesticides



Impact of Ukraine-Russia conflict

Ukraine-Russia conflict has created huge impact on global food security as The Russian Federation and Ukraine are prominent players in global trade of food and agricultural products.  According to FAO, “It is clear that the war has resulted in a massive, and more deteriorating, food insecurity situation, disrupted livelihoods during the agricultural growing season in Ukraine, and has also affected global food security.”

For a more thorough treatment of Smart and Precision Agriculture please visit

Precision Agriculture to Smart Agriculture for a more sustainable world

Smart farming is about using the new technologies which have arisen at the dawn of the Fourth Industrial Revolution in the areas of agriculture and cattle production to increase production quantity and quality, by making maximum use of resources and minimizing the environmental impact. Farmers may gain better control over the process of raising livestock and harvesting crops using various smart farming equipment, making it more predictable and boosting its efficiency. Smart irrigation reduces the wastage of water, which is an important natural resource, smart control over the farming processes may increase crop yield, and sensors data help in cost management and waste reduction.


As the name suggests, precision farming or precision agriculture involves highly controlled, accurate, and optimized agricultural production. It facilitates more efficient resource utilization, better yield, and reduced environmental impact, all at the same time e.g. by avoiding excessive fertilization or pest management and optimizing the agrotechnical treatments, thus contributing to sustainable use of natural resources and limiting the natural environment contamination.


PA is based on observing and measuring the spatial variability – with high resolution and accuracy of the order of single centimeters – of the properties of arable land (e.g. soil type, its abundance, reaction, the influence of the neighbourhood, terrain slope and its exposure, water conditions, microclimate, etc.), the occurrence of phenomena (e.g. properties of cultivated plants, their yield, presence of pests, damage due to violent weather conditions or caused by wildlife, etc.) and then adjusting the local point response to this variability. Consequently, e.g., the sowing rate can be adjusted locally to the soil properties, fertilizers’ doses – to the nutritional requirements of plants, and pesticides’ doses – to the local scale of infection or infestation


Sometimes, the term Smart Agriculture (or “Agriculture 4.0”), in which the emphasis is on the rapid exchange of completely digitized information at all stages of agricultural production and also with external partners, as well as on advanced decision support by cloud-based expert systems, is the next stage of the technological revolution in agriculture after PA.


Enabling Technologies

Smart Farming is digitization of agriculture, combining traditional agricultural practices with information and communication technologies (ICT) to enhance farm produce and quality while optimizing the human labor required by production.

PA is enabled primarily by the proliferation of Geographic Information Systems (GISs) and Global Navigation Satellite Systems (GNSSs), but also by the development of electronics (in particular, agriculture parameters metrology and ubiquity of embedded microprocessor systems), mechatronics, Artificial Intelligence (AI), and wireless data transmission technology.  Producers also incorporate a combination of devices and machinery that help capture vital field data, including Remote sensors, Telematics, Drones, Autonomous vehicles, and Robotics.

(i) Internet of Things (IoT) 

Factors that cannot be controlled by the impact of climate change increase the loss of on-farm food in agricultural production. Common causes of losses include restrictions on the use of resources in production practices, improper harvesting techniques, and post-harvest handling and storage. Traditional production methods of farmers cannot provide professional monitoring of plant growth.

IoT (Internet of Things) and connected devices refer to the integration of physical devices, vehicles, home appliances and other items embedded with electronics, software, sensors, and network connectivity, allowing them to collect and exchange data. The Internet of Things (IoT) and connected devices are critical components of smart agriculture. IoT-enabled devices, such as sensors and other equipment, allow farmers to gather data from the field and transmit it to central systems for analysis and decision-making. This information can be used to optimize crop production and reduce waste, as well as to monitor the health and well-being of crops and livestock.

In agriculture, IoT and connected devices can be used to track and monitor various aspects of farming operations such as crop growth, soil health, weather patterns, and equipment performance, among others. This information can then be analyzed and used to optimize various aspects of farming, such as irrigation, fertilization, and pest management, leading to improved efficiency and productivity.  The use of communication technologies allows for real-time monitoring of crop growth and enables farmers to respond quickly to changing conditions, reducing waste and increasing efficiency. Additionally, communication technologies enable remote control of farm machinery and automation of farm processes, leading to increased productivity and reduced manual labor.

The combination of IoT and satellite technology allows farmers to analyze data from previous years and make actionable decisions that can positively impact the current season. For example, data can help inform farmers on the best places to plant seeds based on trends identified looking at past successes and failures.

IoT Networks can optimize the monitoring and control of farms, mainly through connected smart sensors, and actuators by better monitoring and control of growth factors, like irrigation and fertilizer, leaf moisture and stem diameter, soil health, water, light, and humidity, to the temperature of each animal in the case of livestock.

Weight sensors, biosensors, GPS sensors, pH and electrochemical sensors, temperature sensors, optical sensors to measure soil quality, and airflow sensors to measure soil permeability.

Agriculture has always been at the mercy of the weather. Previously, farms had to rely on weather reports from the nearest airport or city, which didn’t give a sufficiently accurate reading. With hyperlocal weather sensors, enabled by edge IoT technology, farms can eliminate any climate surprises and plan accordingly.

The increased spectrum and bandwidth of 5G enables farmers to increase the scale of data collection, facilitating massive IoT for many millions of devices all connected in a small area.

(ii)Software: specialized software solutions that target specific farm types or IoT platforms. The primary objective of Intelligent Decision Support System (IDSS) for Farmers is to significantly enhance the capacity of (smallholder) farmers, retailers and extension agents providing localized advice. IDSS services are available through different delivery channels including SMS, automated outbound calls, and an android based platform – Fosholi. Farmers can get advisory services on crop suitability, agronomic practices, pest and disease alerts, weather forecasts, market information, and customized notifications through the Fosholi platform.


(iii) Global Positioning System (GPS) and geographic information systems (GIS)

The development and implementation of precision agriculture has been made possible by combining the Global Positioning System (GPS) and geographic information systems (GIS). These technologies enable the coupling of real-time data collection with accurate position information, leading to the efficient manipulation and analysis of large amounts of geospatial data. GPS-based applications in precision farming are being used for farm planning, field mapping, soil sampling, tractor guidance, crop scouting, variable rate applications, and yield mapping.

GPS is integral in bringing new technology to farms, such as autonomous tractors, smart sprayers with AI and better tools for data collection. Using GPS, farmers can easily calculate optimal routes for a tractor moving through the field autonomously,

GPS allows farmers to work during low visibility field conditions such as rain, dust, fog, and darkness. Geolocation services and sensors are helping monitor insect infestations and track cattle and other assets in real time, helping prevent hazards and reduce losses.

The localization precision of GPS is optimally to between three and five meters, which is much too inaccurate for plowing topsoil in a field. The most used is the Real-Time Kinematic (RTK) technology providing the accuracy of less than 3 cm. 5G can relay back corrected high-precision position data in real time to vehicles, drones, and autonomous machines to stay precisely on track

Using GPS and sensors, farmers can strategically plant seeds evenly spaced from each other, giving each seed the best possible opportunity to reach its full potential without having to fight with other seeds for resources like sunlight, water and nutrients.



(iv) Drones and Robotics

Smart machines and robots may take fully control of the tasks from sowing of seed to harvesting of crops with the aid of artificial intelligence and machine learning algorithms which may keep minimal intervention of mankind

Drones can supervise hundreds of acres in one flight, gathering, multispectral images and a wide variety of information about the condition of the land, irrigation needs, crop growth, the existence of pathogens, and, in the case of cattle, the number of animals, their weight and possible anomalies such as lameness or unusual movements.

Drones equipped with multispectral cameras or robot vehicles that register light in the near infrared range can establish the growth and nitrogen requirement of crop plants or measure soil humidity and temperature.

A platform of drones can function as flying ad hoc 5G network and provides acquisition of data from the agricultural IoT sensors located in rural areas with poor coverage. The drones can be also equipped with cameras and sensors for remote crop inspection.

For a big dairy farm (1000 cows), Drones with cameras and 5G connectivity, and image recognition-based system for Real-Time (RT) can perform individual dairy cow monitoring, behavior analysis and feeding.

An electronic fence with 5G-connected cameras and image recognition is proposed for RT detection of unauthorized persons’ access for reduction of damages and thefts on farms

Drones are the most ubiquitous type of robot on farms today covering millions of acres of fields every year. Other types of robots are emerging, including autonomous tractors and specialist picking-robots – but so far, the costs are high, and reliability is still being proven.

Autonomous drone sprayers.

Solar-powered robotic devices autonomously treating and removing weeds.

Autonomous robotic devices equipped with a weed scanner and crop sprayer, scan crops using AI to identify weeds then apply pesticide only where needed.

Autonomous crop pickers handpick ripe fruit and vegetables.

This autonomous crop monitoring system recognizes the requirement for water and responds accordingly.


(v) Cloud computing and Edge computing: Smart agriculture knowledge-based system involves edge computing; cloud computing, knowledge discovery system, artificial intelligence system, and farm management.

The cloud serves as a central digital data store for audio, video, image, text, and digital maps, which are collected in large quantities and diversity from various sources. The cloud allows farmers to sensor-monitor hundreds of different points and create an aggregated view of the data, which can then be analyzed by AI for insights. This will be particularly important for farmers who want to monitor hundreds of crops or cattle assets close together or run several autonomous machines at the same time.

A large amount of data produced in agricultural land and all the data is moved to the cloud leads to network traffic, communication delay and an increase in latency. Edge computing saves network bandwidth by processing part of the data locally instead of passing it to the cloud database server. The main purpose of the edge is to reduce the information traffic that is sent to the cloud server during data processing by enabling processing, storage, and data management activities on edge nodes. As a rule, for critical applications like deploying IoT-supported autonomous machines, the proximity of servers making decisions can reduce latency.

Network of Networks

Network of network technologies ensure continuous communication within the entire technical system of PA,

Low latency enabled by 5G and edge computing

For some devices and monitoring processes to maintain safety, such as critical systems and robotics, low latency is key. In this sense, 5G—with its lower latency and edge cloud, and where computation happens closer to the IoT device—can give farmers more authority over their systems, facilitating absolute control and monitoring of autonomous devices and near-instantaneous field intelligence.



(vI) AI & Data analytics: Using Big data to analyse massive amounts of data, farmers can manage all the information obtained from drones, the Internet of Things and other measuring instruments and integrate it both with historical information for the farm and with weather data, in order to optimise all stages of the production process.

Applying deep learning techniques to drone camera footage to help identify concentrated areas of weeds, applying herbicides only where they are needed.

Apply fertilisers and pesticides with surgical precision

Crops can be gathered earlier or later using analysis of the colour and size of the crops.

Artificial intelligence has also been used in irrigation system.

The plants will be regularly checked for any diseases that threaten the crops, as well as any changes in crop quality, which will be immediately reported to the farmer.

AI is already spotting patterns that allow yields to be improved, for instance by giving early warnings of disease in greenhouses.

A massive increase in compute power and data collection are the driving forces behind the rise in artificial intelligence (AI), However, artificial intelligence requires adequate data to work efficiently. 5G will bridge the gap in data availability that is still evident by speeding up a massive amount of data transfer to where it’s needed for analysis., which will help AI perform efficiently.



(vii) Network of Networks Technologies

Network Requirements

The system of PA is related to a production process in which actions must be taken in response to numerous factors of varying variability in time and space.

While soil properties change over a very long period of time, other phenomena, e.g. the nutritional status and hydration of plants, and especially the occurrence of an infestation with a pathogen, may require a very quick response.

Smart agriculture has different requirements in terms of speed, latency and reliability depending on the work function type.

  • Getting data from sensors. For environmental sensors data and remote monitoring, latency requirement is not critical
  • Operation of smart machines (low latency and reliable communication). M2M communication would require latency less than 100ms
  • Monitoring of farm (high speed data)


Additionally, the possibility of a reaction may depend on external factors (e.g. suitable weather, soil moisture, time of day, temporary legal limitations) and the availability of resources (e.g. personnel, farming machinery, production means). Moreover, in agriculture there is a strong spatial condition related to, e.g., the structure of the farm’s land (concentrated or highly dispersed). Therefore, logistics will also affect the limitations of possible reaction scenarios.


Traffic Pattern Environmental sensors report the farm condition once per hour and traffic pattern is deterministic periodic in nature. High resolution camera installed on machines and/or aerial vehicles for crop monitoring generates burst traffic. It might be the case that central control center requests online monitoring of agriculture land which requires high speed connectivity


Node Mobility Environmental sensors are static while agribots can change their location. Besides, automated guided vehicles may be deployed to perform special tasks such as cultivation, harvesting, pesticides sprinkling, etc., also lie in the category of mobile nodes.

Connectivity of environmental sensors deployed globally especially in oceans and forests to monitor the environmental parameters


Terrestrial 5G Networks

Network of network requirements are needed to take full advantage of the efficiencies of smart farming technologies. Connectivity has posed a significant challenge for those working in the agricultural industry. 5G networking, the cloud and multi-access edge computing (MEC) will be key to help bring down the cost and maximize the gains

5G is critical to smart farming as it supports machine-to-machine (M2M) services. 5G speeds up everything, allowing machines to be controlled centrally and data to be sent back in real-time; 5G is going to be helpful for monitoring along with sensor networks, precision farming, smart irrigation, climate change mitigation, livestock monitoring, and agricultural drones.


Efficient monitoring of farms and crops by collecting and distributing data gathered from farms, satellites, drones, and sensors in real time.


Precise control and communication of autonomous harvesters, seed drones, tractors and field robots for Plowing, sowing, harvesting and getting rid of weeds in the field


The ability to locate and monitor valuable livestock – particularly in upland areas and ranches – is critical to farmers. “

5G will enable connectivity and geolocation services, which could reduce the cost and increase the performance of livestock monitoring. If cows’ health, their food intake and even their fertility, can be communicated back to farmers, they can decide when to intervene.


At the same time, continuous monitoring of farms using AI-enhanced machinery can help farmers identify risks from the very beginning.

5G cellular technology, ensures swift and reliable transmission even of large amounts of image and video data.

The transformative 5G solutions in agriculture should go beyond the IoT area  and include Augmented Reality (AR), RT automation and remote operation.

Enables augmented reality (AR) based mobile, cloud-assisted assistance system for diagnosing and servicing complex agricultural machinery.

This trend is expected to be further enhanced by the introduction of 6G System (6GS) featuring near-to-zero latencies, Tbps data rates and advanced mechanisms supporting even the most demanding use cases.


5G will not have less impact on rural areas and developing countries who do not have access to them. Additionally, since 5G’s frequency is higher, its range is actually shorter and additional cell towers have been built to cover the existing network coverage area.”


Agricultural equipment and vehicles are often used over vast stretches of land and are deployed for days at a time. These expensive assets are frequently at risk of being mismanaged, lost or otherwise damaged.


Non-terrestrial network

This infrastructure cannot withstand in case of natural or man-made disasters. Non-terrestrial network comprising of aerial platforms and satellites can provide reliable and techno-economically feasible wireless communication solutions for a variety of reallife scenarios. This kind of network could complement the existing cellular network in sparsely populated areas. Global machine type communication and relaying of information from such devices deployed in remote regions can be possible with the assistance of UAVs and/or satellites


Airborne Vehicle

Airborne vehicles include low altitude platform and high-altitude platform providing communication service. It can be positioned from few hundreds of meters to stratosphere depending upon the applications.


HAP based network

High Altitude Platforms (HAPs) are airships or balloons placed in the stratosphere above 20 km altitude for the establishment of communication networks or performing remote sensing for civil or military applications. Such aircraft can be manned or unmanned. As per International Telecommunication Union (ITU) , HAP is defined as a communication station positioned at an altitude of 20 to 50 km and at a fixed point relative to the Earth which is quasi-stationary. HAPs are easily deployable stations and can be used to deliver broadband applications with less ground network infrastructure for broadband connectivity and disaster recovery communications.




Low Altitude Platform

Low Altitude Platforms (LAPs) can be described as smart aerial systems capable of flying at an altitude of tens of meters up to a few kilometers. It can be remotely controlled or using an on-board computer system to perform monitoring, capturing video, communication services, etc. LAPs are commonly referred to as UAVs. It can be used as wireless relays for connectivity and coverage of ground wireless devices. UAVs can dynamically move towards IoT devices, collect the IoT data, and relay it to other devices that are beyond the transmitter contact ranges.

HAP and UAV based network

This network is considered for the smart agriculture use case. In this architecture, UAVs are mainly responsible for collecting sensor data from the crop field and command and control of agribots (smart machines) based on the ground sensors input and only forward the event summary to the central station. Here HAP is acting as a base station and providing coverage in remote crop field.


LEO satellites-based network

Satellite solutions have traditionally been expensive to access and use, however with emerging Low earth orbit (LEO) satellites will soon offer reliable connectivity to remote areas and IoT applications without increasing the cost or energy consumption of those solutions.


This allows farmers to use tracking devices that can be as small as a US quarter coin to track animals and equipment quickly and accurately without having to invest thousands of dollars for maintenance, training and equipment.


Satellite based IoT

LEO satellite constellations aim to provide global connectivity environmental sensors deployed in land and sea and smart machines performing operations in agriculture field, all require connectivity to the core network.

LEO satellite constellations are being preferred for global IoT connectivity because low power sensors cannot be operable via geostationary satellites due to high propagation loss. Besides, LEO satellite constellation has the benefit of smaller propagation delay, lower propagation loss relative to conventional geostationary satellites


Network features

Although mobile networks are already implementing many of these ICT technologies to offer the mentioned agricultural benefits, 5G will enhance the impact by manifolds due to low latency, high bandwidth, and support for many simultaneously communicating sensors. However, the implementation of 5G will help speed up the entire process with machine-to-machine services. 5G’s real-time data transfer can help in the speedy functioning of these solutions, making the decision-making quick, robust, data-oriented, and real-time.


Network Reliability: Network reliability is key precision smart farming, which requires real-time access to in-field data and real-time environmental monitoring. Delays in data processing and sharing can result in suboptimal decisions being made because they’re based on incomplete information.


Network Access: Recent surveys estimate that 3.5 billion people are still not connected to the Internet due to a lack of communication infrastructure. It is evident that pure terrestrial networks cannot provide 100% coverage over the globe and not feasible to fulfill the increasing capacity demands owing to economical and geographical rationale.


Cyber threats and Security

Farmers are adopting and integrating technologies such as precision agriculture, GPS-guided tractors to artificial intelligence, 5G Networks, satellite imagery, internet-connected sensors, and other technologies to farm more efficiently. While these practices could help increase crop yields and reduce costs, the cyber enabled technology behind the practices is creating opportunities for terrorists, and hackers to attack this equipment. A significant disruption of grain production could impact the entire food chain since grain is not only consumed by humans but also used for animal feed.


According to FBI, cyber threat actors will continue to exploit network, system, and application vulnerabilities within the FA sector. It has recommended many measures including regular back up of data, air gap, Network Segmentation, multifactor authentication, and focus on cyber security awareness and training.


Security over data and processes is vital. In relocating traffic and services from one central cloud closer to the end user at the edge of the network, MEC enables added security and control. The ability to secure all traffic flows and monitor traffic management at the device level will help provide agriculturists with the assurance that their operations are completely secure.



Case Study

Indian Researchers led by Meenakshi L. Rathod and others describe in the Journal of nanomaterials proposed a design, build, and deploy a ZigBee-based wireless sensor network that is connected to a central node and then to a Central Monitoring Station (CMS) through GPRS or GSM technologies. The technology also collects GPS values from the field and feeds them to a Central Monitoring Station. This technology is anticipated to assist farmers in assessing soil conditions and taking appropriate action.


The variables for monitoring were soil moisture (% volumetric water content), humidity, ambient temperature, dew point, and soil temperature.  Raspberry Pi is connected to DHT11 Humidity Temperature Sensor and Soil Moisture Sensor to read, collect, and store the data of external factors like temperature, humidity, and moisture.


A “sensor data” dataset/directory is created in the Raspberry Pi in which the humidity, temperature, and moisture readings from sensors are stored in the format of .csv (comma separated values) file. For one minute, one data of each is uploaded to csv file, named as “Sensordatas_date.csv.” Similarly, for one day, 1440 humidity, temperature, and moisture readings are uploaded to csv file.


The real-time data collected from sensors are uploaded to “HTM_DATA” dataset and also fed to SmartFarm AgriTech Application using HTTP protocol over Internet or via Local Area Network (LAN) simultaneously


This system  helps the farmers to monitor the fields and protect and maintain the crops using the approach of Internet of Things (IOT) as well as object detection techniques. The DC Pump is automatically switched ON/OFF by the Raspberry Pi and Relay, based on the soil moisture and the temperature level.


Intrusion detection mechanism is used to protect the crop from animal or any theft. This is done by object detection technique which detects the type of animal attack and then notifies the farmer by an SMS and an image sent via mail to the farmer, so we can take the necessary steps to prevent higher extent of damage. CSI (Camera Serial Interface) port of the Raspberry Pi is connected directly to Raspberry Pi High Quality Camera (PiCam). The Dataset is created in Raspberry Pi where User’s frontal face is trained and stored using Python Programming Language and OpenCV module. The algorithm is developed for the faces stored in Dataset using tensor flow and Keras modules from python such that whenever the unknown persons or animals are recognized the Buzzer receives the oscillating signal from raspberry pi producing sound


The cloud serves as a central digital data store for audio, video, image, text, and digital maps, which are collected in large quantities and diversity from various sources. The Amazon Web Services Internet of Things (AWS IoT) is one of the services provided by the AWS which enables the bidirectional communication between the Smart Farm AgriTech System and AWS cloud. The MQTT protocol is configured on AWS IOT to establish the connection between the Raspberry Pi and the Agritech Application for controlling motor and the relay-switch.


Relay is an electrically operated switch connected to Raspberry Pi which is initially at Normally Open (the circuit is always open and does not conduct electricity unless user send a signal from Raspberry Pi to relay switch) configuration. User operates the relay switch with the help of server/cloud created using AWS (Amazon Web Services). Once the relay switch receives the signal from Raspberry Pi through AWS cloud computing technique, it starts conducting electricity to Generator. From Generator, water gets pumped to Farm Land through Drip Irrigation System .


Trickle irrigation is a cost-effective method of conserving water and nutrients by allowing water to drip gently to the plant’s roots. The interface between Raspberry Pi and Arduino board is made to display the uploaded sensors data and to operate the Fertilizer and Pesticides Tank. 5-11% of organic natural fertilizers and pesticides (for example, Jeevamrutham organic fertilizer) mixed with correct quantity of water is stored in Fertilizers and Pesticides Tank. The user operates the motor driver by sending the signal to raspberry pi through the server that was created using Amazon Web Services (AWS), which drives the Servo motor from 0° to 180° opening the Fertilizers and Pesticides Tank to the Farm. According to the growth of the crops and convenience, User can ON the Tank to spray the fertilizers to Agricultural Land for irrigation once at an interval of 7-15 days.


SmartFarm AgriTech Application is a Graphical User Interface (GUI) application created using Tkinter Module and Python. This application enables the user to visualize and analyze the real-time data coming from Raspberry Pi with the help of ThingSpeak Application Programming Interface (API). The ThingSpeak API is the messenger that delivers the HTTP Request from the GUI application to the Raspberry Pi requesting sensor’s data. Once the Raspberry Pi receives the request from the GUI application, it delivers the HTTP Response back to the AgriTech Application sending sensor’s data.


To effectively identify the data, machine learning models based on Artificial Intelligence (AI) are utilised, such as the Support Vector Machine (SVM), which is one of many categorization types


References and Resources also include









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