**3. AI and IoT technologies**

AI refers to a group of technologies that allow computers to do a wide range of complex tasks, such as seeing, comprehending and translating spoken and written language, analyzing data, making suggestions, and more. It revolutionized

agriculture by making farming more efficient, sustainable, and profitable. In addition, many businesses are embracing IoT, which offers simple means to collect and evaluate technical data to identify and improve the performance of numerous daily operations. The technological revolution reveals new challenges and issues with our current IoT technologies. New technologies, such as AI, 5G, and blockchain, promise to solve these challenges. We can create intelligent machines with the help of IoT and AI integration. These innovative automation technologies not only make monotonous tasks more accessible but they also make smart decisions without human assistance. The IoT and AI are two of the most significant technologies in the computing industry, completely transforming how we communicate with machines and our environment. It is speculated that ca. 64 billion AI and IoT devices will be available by 2025. AI and IoT technologies work effectively together and are at the top of the latest innovations influencing the information technology sector. The industrial and agribusiness sectors have benefited from the duet's redesign of traditional solutions [74, 75].

Machine learning (ML) is a subset of AI that involves training algorithms to make predictions or decisions based on training data. ML is a rapidly growing field with numerous applications and opportunities for scientific innovation. It is a branch of AI that involves the development of algorithms and statistical models that allow computer systems to automatically improve their performance on a specific task as they gain experience. ML aims to develop intelligent systems capable of learning from data and making predictions or judgments without being specifically programmed to do so. In IoT, ML is a technique that can be applied to analyze sensor data and forecast future events. Deep learning has become a potent ML method in recent years. Deep learning includes putting multiple-layer neural networks through training to learn how to represent data hierarchically. As a result, developments have been made in fields such as speech recognition, natural language processing, and image recognition. Numerous industries, including agriculture, healthcare, finance, marketing, and transportation, use ML in various ways. ML in agriculture has the potential to be applied in a variety of ways with exceptional results from weed, pests and disease detection, crop yield, and quality prediction, to data collection, providing insights, and livestock production forecasting. It can be used in the healthcare industry to find novel medicines and disease diagnoses. It can be applied to finance to assess risk and detect fraud. It can be used in marketing for personalized advertisements and customer segmentation. It can be applied to autonomous driving and traffic forecasting in the transportation sector [29, 76–81].

Natural language processing (NLP) is a field of AI that applies to the interaction between computers and humans using natural language. NLP is vital to contemporary AI systems because it enables machines to understand, interpret, and generate human language. The development of smart homes, smart cities, and other intelligent systems now has more opportunities since integrating NLP in IoT devices. Users can communicate more effectively and easily with IoT devices by adding voice recognition and natural language understanding capabilities. One of the most significant applications of NLP in IoT is voice assistants such as Amazon's Alexa, Google Assistant, and Apple's Siri to turn on/off lights, play music, set alarms, or order groceries online. Weather data analysis is an example of how NLP is used in agriculture. Farmers can decide on crop planting and harvesting dates, irrigation requirements, and controlling pests and diseases by evaluating weather patterns and forecasts. Farmers may track crop growth and development rates, as well as soil moisture levels with the aid of NLP algorithms. In order to help the farmers choose the best crops to cultivate and

#### *Applications of AI and IoT for Advancing Date Palm Cultivation in Saudi Arabia DOI: http://dx.doi.org/10.5772/intechopen.113175*

the best times to sell, NLP models can also be used to assess market trends and forecast demand. Farmers may find new customers and bargain for higher prices with the aid of NLP algorithms. Improved consumer-farmer communication is another area, where NLP can be employed. Farmers can learn more about consumer preferences by examining customer feedback analysis. Using NLP algorithms, farmers can communicate more effectively with each other, sharing their best agriculture practices and working together on research initiatives [82–86].

Computer vision is a field of AI that enables computers and machine systems to extract useful information from visual data (digital images and videos) and then take actions or make recommendations. This technology is widely used in IoT to enable devices to see and interpret the physical entity through object, facial, and gesture recognition, thus making them more intelligent and responsive. With the help of deep learning algorithms, computer vision systems can identify objects in real time and classify them into different categories. This capability has been used in various IoT applications, such as autonomous vehicles, security cameras, and smart home devices. Similarly, computer vision systems can extract valuable environmental information by analyzing images captured by cameras or sensors. For example, they can detect anomalies or changes in a scene, monitor traffic flow, or track the movement of people or objects. IoT has also used this technology for gesture recognition and human-computer interaction. Computer vision systems can enable users to interact with devices more naturally and intuitively by analyzing hand movements and gestures. This capability has been used in various applications, such as gaming consoles, virtual reality systems, and smart home devices. Many agricultural activities are being automated and optimized using computer vision technology. Crop monitoring, yield prediction, and insect and disease detection are a few applications of computer vision technology in agriculture [87–91].

Robotics technology is a branch of engineering that entails creating and programming robots and is crucial to advancing AI and the IoT. It is used in IoT to create intelligent machines that can exchange data and connect. Robotics is using machines to carry out jobs that are either harmful or too difficult for humans. How we interact with machines has changed dramatically because of the inclusion of robotics technology in AI and IoT, improving their efficiency, dependability, and autonomy. These robots' 24/7, nonstop operation, which is also utilized for data entry and processing tasks, minimize errors, and boost efficiency. Automation of robotic processes has been widely used in several sectors, including agriculture, manufacturing, healthcare, logistics, and finance. The development of autonomous robots is another way that robotics technology is used in AI. Robots operating independently of humans may use sensors, cameras, and other cutting-edge technologies to navigate their environment. Robotic technology is being employed more frequently in agriculture to improve the productivity and efficiency of farming operations. Many benefits are reported from using robots in agriculture, such as improved crop yields, lower labor costs, and increased accuracy [15, 92–95].

Edge computing represents a decentralized computing model that moves computation and data storage nearer to the requirement point, enhancing response speed and minimizing bandwidth consumption. In IoT, edge computing can process sensor data in real time at the network's edge. ML models used in AI are substantially trained using massive volumes of data. This training can be carried out locally *via* edge computing, reducing the need to send large amounts of data to centralized servers. By storing sensitive data on local devices rather than transferring it over the internet, edge computing can decrease network congestion, enhance privacy and security, and

increase reliability. Edge computing can also be utilized to run AI models in real time, enabling rapid response and decision-making. Edge computing, for instance, can be utilized to analyze sensor data from a localized area in real time and identify possible problems before they escalate [96–101].

Cloud computing technology plays a crucial role in developing and deploying AI and IoT applications. It provides an infrastructure that allows organizations to store, manage, and process large amounts of data generated by IoT devices and AI applications. Within the cloud computing framework, the vendor hosts and provides infrastructure, data, and software as a service to the user. Scalability is one of the most significant benefits of cloud computing for AI and IoT. Because cloud-based services are easily scaled up or down according to demand, businesses and organizations can manage the massive amounts of data and processing power needed for AI and IoT applications.

Additionally, cloud computing offers an affordable solution for users who need to store and process large amounts of data. By utilizing cloud-based ML platforms, cloud computing also enables the development of AI and IoT applications. These platforms give programmers access to robust machine-learning algorithms, resources, and libraries that they can utilize to develop sophisticated applications.

Additionally, developers can scale up or down their ML models in response to demand using cloud-based ML platforms. Additionally, cloud computing offers a safe environment for processing and storing private data produced by IoT and AI applications. To safeguard customer data from cyber threats, cloud service providers have implemented several security measures such as firewalls, access controls, and encryption [102–106].

Blockchain technology is a decentralized and distributed ledger system that securely and transparently records transactions across multiple computers. It enables secure transactions between parties without the need for intermediaries. It can be used for secure device authentication, data sharing, and supply chain management. This technology can enhance AI and IoT systems' security, privacy, and interoperability. Increased security is one of the main benefits of adopting blockchain in AI and IoT. It secures data and transactions using cryptographic algorithms, making it difficult for hackers to tamper with or steal data. Blockchain can also be used to develop secure digital identities for users and devices, which can help restrict unauthorized access. Improved privacy is a benefit of using blockchain in AI and IoT. Users can choose who can access their data and maintain control over it. This technology is essential when sensitive data needs to be protected, such as in healthcare and financial applications.

Additionally, blockchain can improve the interoperability of various IoT and AI systems. It enables different methods to communicate with each other more easily by establishing a shared decentralized ledger of transactions. This can make it simpler for developers to create new applications and lessen the difficulty of integrating various systems [107–111].

## **4. Applications of AI and IoT in agriculture**

By 2025, it is anticipated that global spending on intelligent, interconnected agricultural technology and systems, including AI and IoT, will triple in size, reaching \$15.3 billion. Understanding how factors such as sunlight, weather, animal, bird, and insect movements, crop use of specific fertilizers and pesticides, and planting

#### *Applications of AI and IoT for Advancing Date Palm Cultivation in Saudi Arabia DOI: http://dx.doi.org/10.5772/intechopen.113175*

and irrigation cycles affect crop production is a perfect subject for machine learning. Excellent data has never been vital for determining a crop cycle's profitability. For this reason, farmers and the agricultural sector are stepping up their data-centric strategies and broadening the scope and scale of the application of AI and IoT technology to improve crop yields and quality [112–116].

Crop yields are being increasingly optimized using AI and IoT. Farmers can understand more about the health of their crops and make intelligent decisions about water and fertilizer requirements and disease and pest control by utilizing data from sensors and other connected devices. Precision farming is a significant area, where AI and IoT are used in agriculture. This entails creating precise field maps and real-time crop growth monitoring using data from sensors, drones, and other sources. Farmers can identify areas that need water, fertilizer, or disease/pest control by examining this data and areas that are vulnerable to disease or pest infestation. Due to their increased ability to deploy resources effectively and efficiently, crop yields increase and costs decrease [117–120].

Predictive analytics is yet another approach AI and IoT use in agriculture. Farmers can use machine learning algorithms to accurately predict future yields by analyzing historical data on weather patterns, soil conditions, and crop performance. This enables them to plan for planting and harvesting crops and make smart decisions about pricing and marketing. Before a vegetation cycle even begins, it is now possible to know the potential yield rates of a field using AI and IoT technology. The potential crop yield can be estimated using machine learning techniques to analyze 3D mapping, sensors' soil analysis data, and drone-based soil color data. Farmers can automate the irrigation schedule of their crops based on real-time data on soil moisture content *via* remotely connected devices, such as smart sprinklers and soil sensors. Satellite-based thermal-infrared imaging remote sensing AI and IoT technology also monitor irrigation rates and crop water requirements. Automating tasks, such as fertilization is another possible application of AI and IoT in crop farming. Farmers can apply fertilizer more precisely based on real-time data on soil nutrient levels *via* GPS-connected fertilizer spreaders. The technology is used to apply fertilizer variably on most needy soil areas.

Similarly, the normalized difference vegetation index images recorded through drones or satellites can be used to apply variable nitrogen application at different crop growth stages. Variable seed rates of different crops can be estimated by scanning the electric conductivity of the field. The seed spreader is then connected to the GPS-kit with the field electric conductivity map, guiding the spreader to apply seed variably. Large-scale agricultural firms turn to robotics when they cannot find enough skilled workers. Self-propelled robotics machinery that can be programmed to apply seed and fertilizer evenly along each row of crops lowers operational costs and increases crop yield. Farmers can detect symptoms of disease or stress before they are noticeable to the naked eye, for instance, by employing computer vision algorithms to examine photographs of plants. This enables them to take corrective action before the problem worsens, resulting in healthier plants and better-quality produce. Farmers employing AI and IoT technologies can predict and detect disease/pest infestations before they occur by combining drone infrared camera data with sensors on fields that can monitor plants' relative health.

Moreover, the AI and IoT systems can identify disease/pest-affected areas by combining intelligent sensor data with visual data streams from drones. Farmers can gain more knowledge about the health of their crops and decide how to allocate resources and control pests/diseases by analyzing data from sensors and other

connected devices. Crop quality can also be improved as well with the use of AI and IoT [121–131].

All agricultural supply chains have adopted track and traceability, and this trend is expected to continue. A well-managed track-and-trace system increases visibility and control throughout supply chains, which reduces inventory shrinkage. Modern AI and IoT-based track-and-trace systems can distinguish between batch, lot, and containerlevel material assignments in inbound shipments. Most cutting-edge track-and-trace systems rely on sophisticated sensors to record data for each shipment. Agricultural supply chains and shipments are increasingly using AI and IoT sensors. AI and IoT systems show different marketing scenarios to farmers to get the maximum return on their produce. When deciding on pricing strategies for a particular crop, price forecasting for crops based on yield rates that help forecast total volumes produced is crucial. Understanding yield rates and quality standards enables agricultural businesses to negotiate effectively for the best harvest price. The pricing strategy is determined by analyzing the total demand for a crop to determine whether the price elasticity curve is inelastic, unitary, or highly elastic [7, 118, 132–135].

One of the fastest-growing applications of AI and IoT in agriculture is monitoring livestock health, including daily activity and food intake. Various aspects of livestock management and monitoring, such as behavior, detection, counting, identification, grazing tracking, health issues, estimating the herd distribution, etc., can be achieved using AI and IoT technologies. The best way to care for livestock over the long term is to understand how each livestock responds to diet and boarding conditions. Producing more milk requires AI and IoT to comprehend what keeps cows happy and contented daily. AI and IoT technologies reduce the chances that domestic and wild animals may accidentally destroy crops or commit a break-in or burglary at a remote farm. Farmers can secure the perimeters of their fields and buildings through image analysis powered by AI and machine learning algorithms [136–141].
