Understanding the potential applications of artificial intelligence to implement smart farming in UAE TECH101

Understanding the potential applications of artificial intelligence to implement smart farming in UAE

Abstract:

Agriculture plays a significant role in enhancing the economy of any country. The world population is increasing enormously and with this increase, the demand for food and employment is also increasing. Agricultural automation has been extensively applied worldwide to solve traditional agriculture methods, climate change, employment, food security, and sustainability issues. This article investigates the integration of artificial intelligence (AI) in smart farming techniques within the United Arab Emirates (UAE). To reduce the effects of the region's arid climate and scarce arable land, there is an urgent need to transform its agriculture industry by utilizing cutting-edge technologies, especially artificial intelligence. The study proposes the significance and scope of artificial intelligence (AI) in improving crop yields by monitoring environmental conditions with the help of smart greenhouses outfitted with sensors, Internet of Things devices, and machine learning algorithms. This article discusses the effectiveness and benefits of AI-driven farming technologies which are responsible for increased crop production, decreased food spoiling, weather forecast, resource efficiency, irrigation systems, and disease and pest control. Some prospects such as Crop diversification, environmental parameter expansion, energy sustainability, and cooperative research activities have also been discussed.

Keywords: Artificial intelligence, smart farming, Machine learning, Internet of Things devices

Introduction:

It has been reported by the Food and Agriculture Organization of the United Nations that the world population will reach over 9 billion by 2050 ( Simon Y. Liu USDA-ARS Washington, DC, USA). The huge increase in population, shrinking agricultural land, declining natural resources, reduced soil profile, unpredictable climate changes, and shifting market demands have pushed the agricultural production system into a new paradigm ( Simon Y. Liu USDA-ARS Washington, DC, USA). It is very hard to control all these limitations if the region has a large area of arable land and a harsh-dry climate with very little rainfall such as the UAE.

To overcome the existing shortcomings, the new agricultural system must become more productive in output, efficient in operation, resilient to climate change, and sustainable for future generations (USDA).

The arid environment, unusual topography, less rainfall, and a small amount of fertile land have always been one of the major difficulties in the UAE for growing a variety of vegetables, irrigation, soil detection, crop scouting, and weeding. The accuracy and adaptability necessary to guarantee the best possible environment for crop growth while preserving vital resources are absent from conventional agricultural techniques. The inability of conventional farming methods to provide the best circumstances for crop growth leads to poorer yields, resource waste, and susceptibility to rotting. Furthermore, because 85% of the UAEs food supply is imported, the country is vulnerable to outside disturbances in the global food chain, underscoring the urgent need for food production autonomy (Aldababseh, Temimi, Maghelal, Branch & Wulfmeyer 2018). An important step toward achieving sustainable food production has been taken with this data-driven strategy. It is impossible to overestimate the significance of AI in the UAEs agricultural industry (Yadav, Kaushik, Sharma & Sharma 2022). Despite these challenging conditions, the UAE has recently embarked on a determined drive to achieve food security and sustainability by creating a unique plan under which the country is determined to top the Global Food Security Index by 2051 (Dubai Leading UAEs Vision to Achieve Food Security 2022), (Tanchum 2022) which shows the countrys unwavering commitment to guaranteeing its future food supply. Various research and efforts are still on the way to improve the quality and quantity of agricultural products by making them connected and intelligent through smart farming." (Mohd Javaid, Abid Haleem, 2023)

Could be long

AI and Agriculture:

Artificial Intelligence is based on the hypothesis that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. It was first announced at the Dartmouth Conference in the year 1955, by John McCarthy who proposed that a study should be carried out based on the above hypothesis ( McCarthy J, Minsky ML, 1955). Artificial intelligence is an interdisciplinary field of study that aims to replicate human intelligence in robots that bear a resemblance to human cognition and behaviors, including learning and problem-solving attitudes. With advances in computational capabilities and increased cloud penetration agriculture industry of various countries has already started to reap the benefits of AI. (Mohd Javaid, Abid Haleem 2023). AI has capabilities for better crop management systems including each aspect of farming. This idea of using AI techniques in agriculture was first proposed by McKinion and Lemmon in their paper "Expert Systems for Agriculture" in the year of 1985 (J. M. McKinion, 1985).

Some early research for expert systems for improving crop management has been proposed such as in 1987, POMME for the management of apple plantations (J. Roach, R. Virkar, 1987), COTFLEX and COMAX for cotton crop management (N. D. Stone, T. W. Toman , H. Lemmon 1990), multi-layered feed-forward artificial neural network- to protect citrus crops from frost damage in Sicily island of Italy (C. Robinson, 1997).

In general, AI has two main core parts, machine learning and DL (Patel et al., 2020a, 2020b; Pandya et al., 2019; Sukhadia et al., 2020). While AI is the science of making smart machines and programs, ML is the ability to acquire something without being explicitly programmed and DL is the understanding of deep neural networks (Kodali and Sahu, 2016; Kulkarni and Deshmukh, 2013). Machine learning (ML) focuses on teaching computers to do tasks better by leveraging data and prior knowledge. To improve results, this discipline uses a variety of approaches, such as supervised, unsupervised, and reinforcement learning (reference). By using the keywords "machine learning," "artificial intelligence," and "neural networks" in conjunction with the terms "farming" or "agriculture," keyword searches were used to find the evolution of ML techniques in tackling agricultural difficulties over time, as shown in Figure 1. ML-based AI is suitable for systems where frequently training the system is not a constraint and higher accuracy is desired, which is quite true for agri-food systems. (N. N. Misra , Yash Dixit et al)

Artificial intelligence holds out the possibility of increasing crop yields while preserving vital resources, expediting farming operations, and improving resource allocation. AI-driven solutions can transform irrigation techniques and ensure that water is used effectively and sparingly in a setting where every drop of water is valuable (Javaid, Haleem, Singh & Suman 2022). AI has applications in precision agriculture, where it can improve planting and harvesting while decreasing waste and raising productivity in general. It has been observed that Artificial intelligence technology has enhanced crop production with improved real-time monitoring, harvesting, processing, and marketing (Yanh et al., 2007).

The latest technologies of automated systems using agricultural robots and drones have made a tremendous contribution to the agro-based sector( Tanha Talaviya a, Dhara Shah). Agricultural Robots are autonomous machines that are faster and more efficient than human work. These robots are used for jobs like the quick harvesting of large quantities of crops. Examples include the "See & Spray Robot," which keeps an eye on cotton plants and sprays pesticides when necessary to prevent herbicide resistance, and the "Harvest CROO robot," which can take the place of up to 30 human laborers and effectively handle tasks like strawberry picking and packing while harvesting up to 8 acres per day (Faggella 2020). The robots are responsible for performing various agricultural operations unconventionally such as weeding, irrigation, guarding the farms, ensuring that adverse environmental conditions do not affect production, increasing precision, and managing individual plants. (Tanha Talaviya a , Dhara Shah, 2020). Another essential component of AI-powered agriculture is remote sensing which offers timely information on crop health and pest control. It quickly identifies impending problems by examining satellite imagery and sensor data, empowering farmers to take preventative action and lessen possible losses. Additionally, Remote Sensing with the use of UAVs for image capturing, processing, and analysis is making a huge impact on agriculture. (Abdullahi et al., 2015). Drones are being implemented in agriculture for monitoring crop health, irrigation equipment, weed identification, and also disaster management (Veroustraete, 2015; Ahirwar et al., 2019; Natu and Kulkarni, 2016).

Issues related to soil and irrigation management are very crucial in agriculture. Improper irrigation and soil management are ultimately responsible for crop loss and degradation. Crop vitality, production amount, and quality are all affected by soil micronutrients and macronutrients, which are the fundamental needs for good quantity and quality of crop production. Earlier, the quality of the soil and crop health used to be measured by human sight and judgment, but nowadays AI can be very useful in monitoring crops and soil. To diagnose soil issues and provide soil analysis services, machine learning is essential. We can now employ drone technology to capture aerial picture data and train computer and sense of direction models to use it. Drones with deep learning capabilities can check the health of the soil and crops also (ref). "Plantix," a program that uses deep learning to find nutrient imbalances in soil, is an example of drone technology (reference). With the use of their smartphone cameras, users may take pictures of soil imperfections, and the software then suggests workable fixes for restoring soil health. Previously, Brats et al. (V. F. Bralts, 1993) reported a rule-based expert system for the assessment of the design and performance of microirrigation systems. The other artificial neural network-based system for the assessment of soil moisture in paddy was designed by Arif et al. (C. Arif, et al 2013). Some of the other popular systems which have used artificial neural networks for the management of soil and irrigation include Broner and Comstock, 1997, Song and He (H. Song et al 2005), Zhai et al. (Zhai et al, 2006), Patil et al. (Patil et al 2009), Hinnell et al. (2010), Junior et al. (J. da Silva, et al., 2016) and Antonpoulos et al. (2017).

Prediction analysis for crop yield is very beneficial for marketing strategies and crop cost estimation. Machine learning enabled Predictive Analytics (PA), has the potential to revolutionize agriculture. To increase agricultural productivity, PA entails forecasting crop diseases, controlling pests, crop yield estimation, and crop sustainability analysis utilizing environmental information including temperature, humidity, solar radiation, and wind speed, as well as improving plant nutrition (Derguech, Bruke & Curry 2014). Some of the primary research was done by Liu et al. (2005), which was based on the artificial neural network model employing a backpropagation learning algorithm to predict yield from the different soil parameters. Other remarkable works that are primarily based on machine learning algorithms include Kaul et al. (2005), Uno et al., (2005), Ji et al.,(2007), Zhang et al.(2008), Russ et al. (2008), Singh RK, (2008), Alvarez (2009) and Rahaman and Bala (2010). The applications for improving weather forecasting accuracy include the use of support vector and regression algorithms (Ni, Zhang & Ji 2014). Furthermore, Nave Bayes algorithms have been used in applications for food safety in smart agriculture. (Han, Gu, Zhang & Zheng 2014). Smart agriculture systems enabled by the Internet of Things (IoT) have used support vector machines to improve data classification and real-time prediction. By creating a smarter environment where data is automatically transmitted from individually identified items, animals, or persons over the Internet, IoT plays a key role in transforming agriculture. Sensors, processing networks, data analysis tools, and system monitoring units are just a few of the many components that make up IoT systems. IoT is used in agriculture for precise weather monitoring, data collecting on soil conditions, silo and tank level monitoring, and protection of outlying farms (Mahdavinejad et al. 2017). This method improves agricultural productivity while saving time, energy, and money.

Current Complications Related to AI in UAE

Should be in text

Although AI presents immense opportunities in agriculture applications, there still prevails a deficiency in familiarity with advanced high-tech machine learning solutions in farms around the world. (V. Dharmaraj* and C. Vijayanand 2018). GCC has to use advanced technologies and innovations to promote sustainable farming methods to meet the global trend toward precision agriculture, remote sensing, and data-driven decision-making. However, there are still several research gaps and difficulties that need to be resolved such as

1. A) Youth Participation and Skill Development- The UAEs food security strategy seeks to boost the national economy by a significant $6 billion shortly (ref link) by developing the skills of its youth and producing a new generation of agri-technologists. While efforts are being made to encourage young people to work in agriculture and give them the skills they need, some specific programs are required to effectively engage and develop young Emiratis as agri-technologists. It is a very important need to identify the precise educational and training requirements of young people. This involves investigating cutting-edge strategies to motivate and train people for careers in AI-driven agriculture.


2. B) Obstacles to AI adoption- Previous reports reveal that 74 percent of employees report regular use of AI tools in the workplace. However, there is a critical gap observed, as 91 percent of companies want to provide training on AI tools to their employees, while only 84 percent currently receive such training. (https://www.sme10x.com/technology/artificial-intelligence/navigating-the-ai-era-uae-leads-butchallengespersist) The cost of deploying AI systems, integrating AI with current farming techniques, and having the technical know-how necessary to operate AI-driven solutions efficiently, are some of the most prominent obstacles in this industry.


3. C) Sustainability measures: Recognition of problems and their proper solutions should be provided for a successful transition to an AI-empowered agriculture market. Although sustainable agriculture is a priority in the UAE, but to assess the environmental, economic, and social sustainability of AI-integrated farm systems, criteria and indicators must be developed. (Ref)


4. D) AI-enabled disease/climatic factors/pest identification- Insect pest infestation is one of the most alarming complications in agriculture especially in the UAE and leads to heavy economic losses. The various challenges of IoT devices for pest detection in the field include high power consumption, network issues, inadequate security, service expiration, physical hardware defects, software failure, and changes in ambient conditions, which are quite frequent across the globe due to global warming (Kiobia, D. O., et al, 2023). Over so many years researchers have tried to alleviate these problems by developing computerized systems such as Pasqual and Mansfield (G. M. Pasqual, J. Mansfield), SMARTSOY of Batchelor et al.,( W. D. Batchelor, R. W. McClendon), CORAC of Mozny et al. (M. Mozny et al). However, research on AIs function in managing pests and diseases needs to be conducted in greater depth. This entails enhancing the precision of algorithms for detecting pests and diseases, investigating AI-driven methods for environmentally benign and targeted pest control, and evaluating the long-term consequences of reduced pesticide use on crop health and yield. (ref)


5. E) Privacy & Security: Regarding the usage of AI in agriculture and other sectors, there are no rules and regulationsso far that can raise legal issues and concerns in smart agriculture farming which are still unanswered. Additionally, Security threats like data leakage and Cyberattacks make it difficult to trust the software for their farming activities.

Research Objective

The main issue raised by this research is the lack of an integrated, AI-driven solution specifically designed for vegetable cultivation in the difficult agricultural environment of the UAE. The inability of current methods to actively manage environmental factors including temperature, humidity, light, and soil moisture leads to subpar growing conditions, resource waste, and the possibility of rotting. A lack of complete frameworks that use IoT and machine learning technologies to continuously monitor, evaluate, and adapt is another issue. Additionally, there aren't many comprehensive frameworks that use IoT, and machine learning to continuously track, evaluate, and adjust to environmental changes, maximizing yield and quality while consuming the fewest resources possible.

The main objective of this project is to build a greenhouse in the form of a sustainable building and then integrate it with other aspects of an innovative AI system to strengthen agricultural practices. This system will consist of a diverse implementation of sensors that will constantly monitor key environmental factors like humidity, pH value, temperature, light, and moisture. It has a special integration of an AI-based mechanism that controls air conditioning, lighting, and irrigation systems. This model also emphasizes data collection and processing which is done with the integration of various advanced machine-learning techniques. This could be beneficial in prediction analysis which will ultimately help in decision making and improvement of the greenhouse. This initiative and commitment taken by the UAE to improve the way of farming practices by integrating AI and innovation can tackle a lot of food security challenges. This can also bring a great boom in the agricultural sector.

Process of Implementing Smart Farming Through AI

Heading topic can be changed

The idea of an AI-driven smart greenhouse with built-in air conditioning systems for the best crops cultivating culture is a sophisticated framework meant to handle the unique difficulties of upholding perfect circumstances for vegetable growth in a controlled setting. A network of sensors that are constantly monitoring numerous environmental parameters important for cultivation is at the heart of this novel data collection platform (Chang, Huang & Chen 2022). These sensors include ones for temperatures that are dispersed throughout the greenhouse, for the humidity that gauges atmospheric moisture levels, for light that gauges light intensity for ideal photosynthesis, for soil moisture that is placed carefully in the soil beds, and for cameras that take pictures for visual inspection of plant health. The Internet of Things (IoT) technology is used to transfer sensor data in real-time to a central control system, resulting in the creation of a dynamic and interconnected ecosystem. Advanced AI algorithms are responsible for controlling processing and interpreting the data once it enters the central control system. These AI algorithms are built to make decisions in real time depending on the data that is received, guaranteeing that the greenhouse atmosphere is still favorable for maximum and effective growth of the crops. Threshold analysis is one of the basic capabilities of an AI system. In this process, particular thresholds are defined for important factors like temperature, humidity, light levels, and soil moisture to align them with the exact needs of cultivating culture. For instance, it is reported that a critical threshold temperature for tomatoes is 28 degrees Celsius, after which there is a risk of tomato spoiling. The air conditioning system gets activated by the AI system as a countermeasure to lower the temperature and maintain an ideal environment for tomatoes when it notices that the ambient temperature has risen above this predetermined threshold (JPAM Doctoral Dissertation Listing 2016 2017). It may be beneficial to incorporate this technique for several other crops, vegetables, and fruits. However, the system's intelligence goes beyond simple threshold-based regulation. By analyzing previous temperature data and incorporating weather projections, it also uses predictive analytics to foresee potential temperature changes. The AI system can actively regulate the cooling system to preventatively counteract unfavorable situations by anticipating temperature spikes, reducing the danger of spoiling, and optimizing growth conditions. The AI system controls not only temperature but also other essential elements of the greenhouse environment. Using information from specialized sensors placed in the vegetable/soil beds, it continuously checks the soil moisture levels. The AI system turns on the irrigation system to hydrate the plants and ensure their health and productivity if these sensors detect a soil moisture level below the range that is optimal for cultivation. Additionally, the system has control over the greenhouse's artificial lighting. The AI system can automatically manage artificial lighting to augment the necessary light for photosynthesis when natural light levels are insufficient, such as on overcast days or at night, ensuring consistent growth conditions. The development of a feedback mechanism as shown in Figure 2 that promotes a continual learning process is a key component of this architecture. Machine learning algorithms have been incorporated into the AI system, which actively learns from the data gathered, the system's responses to various circumstances, and the results of development. This repeated feedback loop enabled the system to improve its adaptability and efficacy over time by fine-tuning its decision-making and control strategies. The framework also includes a thorough alarm and notification mechanism to inform important parties. A user-friendly mobile app or dashboard provides operators and farmers with timely information about the status of greenhouse gases as well as vital alarms when necessary. To guarantee that any unusual circumstances or emergencies receive rapid attention, the system can send alerts via SMS or email.

Mineral content and othe things should be added.

Material And Methods

Heading should be changed all the things should be in text.

Hardware Requirements

• Network of Sensors (Temperature, Humidity, Light, Soil Moisture, Cameras)


• IoT Devices for Data Transmission


• Central Control System (Server/Computer)


• Data Storage Servers


• Air Conditioning System


• Irrigation System


• Artificial Lighting Control System


• Mobile Devices (for user interface)

Software Requirements

• AI Algorithms and Machine Learning Models


• Real-time Data Processing Software


• IoT Communication Protocols


• Database Management System (DBMS)


• Mobile App Development Platform


• Alert and Notification Software

DETAILS OF THE DATABASE SYSTEM

The framework relies on a strong database system to store historical sensor data, AI model parameters, and user interactions. The information below is contained in the database:

• Sensor Information: Records of the greenhouse sensors' measurements of temperature, humidity, light intensity, soil moisture, and camera images.


• AI Model Parameters: Details concerning AI algorithms and machine learning models, such as training data, model weights, and optimization parameters.


• User Interaction Data: Records of how users have interacted with a mobile app or dashboard, including changes to settings, notifications received, and responses given.


• Environmental Thresholds: Preset environmental thresholds for variables like temperature, humidity, and soil moisture, along with accompanying responses and actions.


• Historical Performance Metrics: Information on crops yield, resource use (water and energy), and reaction times to environmental changes.


• Alert and Notification Logs: Records of alerts issued to users through SMS, email, or the mobile app, along with timestamps and information on the triggering events.


• Security and Access Control: User authentication and authorization information to ensure secure access to the system.

CONCLUSION WITH FUTURE DIRECTIONS

As we know agriculture is a difficult task in general, especially in UAE keeping various factors into consideration like Temperature, Soil, Water etc. Smart Farming is the only way to enhance farming across the world. Water scarcity, a major worry given the areas arid climate, adds to the list of difficulties. Effective water management becomes essential in such a setting, especially when it comes to agriculture. The UAE is aware that ensuring food security necessitates both increasing local output and conserving limited water supplies. The UAE has set lofty objectives to handle these complex issues. Along with achieving self-sufficiency, the country wants to boost average farm income by 10% and hire 5% more people in the agricultural sector. These goals demonstrate a dedication to not just overcoming obstacles but also encouraging economic development and job possibilities within the agricultural industry. Additionally, the UAE places a high priority on minimizing irrigation water use, matching its objectives with the requirement of sustainable resource management.

The UAE government has made significant strides toward putting AI-based real-time advisory services into place to strengthen this objective. In areas with historically low output levels, it is extremely important. This project demonstrates the UAEs unwavering dedication to using technology to better agriculture. This AI-driven advice system equips farmers to make knowledgeable decisions, maximize crop yields, and successfully traverse the intricacies of contemporary agriculture by providing real-time insights and recommendations. An important step toward achieving sustainable agriculture has been made with the creation and application of the AI-driven smart greenhouse framework, especially in harsh conditions like the dry regions of the UAE. To solve the difficulties faced by crops, vegetables or fruits producers in the UAE, this comprehensive solution makes use of cutting-edge technology including the IoT, AI, and machine learning. The key to this framework's success is its capacity to build an interconnected ecosystem that constantly checks and modifies crucial environmental factors to ensure development is as efficient as possible while lowering resource consumption and spoiling risk. As we wrap up our exploration of this novel framework, it is crucial to consider its importance, accomplishments, and potential.

Successes and Importance

The use of the AI-driven smart greenhouse architecture has produced notable successes and effects, including:

Superior Yield: The framework has shown a significant improvement in yield per square meter by actively managing environmental factors including temperature, humidity, light, and soil moisture. This accomplishment is essential for addressing the UAE's food security issues and lowering reliance on food imports.

Resource Efficiency: The frameworks careful management of resources, particularly water and energy use, has demonstrated incredible efficiency improvements. Water is used cautiously because it is a limited resource in arid settings, in line with sustainable practices. The UAEs dedication to environmental sustainability is in line with the decrease in energy use.

Reduced Food Spoilage: The framework has successfully decreased the danger of food spoilage by maintaining ideal conditions and aggressively preventing unfavorable situations. In addition to ensuring increased yields, this accomplishment also significantly reduces food waste, improving food security.

Predictive Analytics: The system is now able to foresee and proactively handle environmental changes thanks to the application of predictive analytics. This skill has broad ramifications, including improved agricultural resilience and climate change adaptation.

Continuous Learning: The frameworks inclusion of machine learning has made it possible to continuously learn and improve. Over time, the system's adaptability and performance will increase as it fine-tunes its decision-making processes in response to new data and user interactions.

User-Friendly Interface: Operators and farmers have real-time knowledge, notifications, and control over the greenhouse environment thanks to the user-friendly smartphone app and dashboard. This interface makes it easier to make well-informed decisions and react quickly to urgent situations.

According to data from the Dubai Chamber, the food and beverage industry in the United Arab Emirates, which is closely related to agriculture, experienced significant development and reached a significant $20 billion in the first nine months of 2021 by implementing AI in various sectors (ref). (should be in the success section)..

Future Perspectives

AI technologies have delivered innovative and specific solutions to major agricultural issues that are challenging for farmers worldwide. (jamia paper). It is expected to come more exhilarating discoveries related to the use of AI in smart agriculture techniques. AI is helping farmers automate their farming and is also moving toward precision cultivation for improved crop output and quality while utilizing fewer resources. (jamia paper). Some prospects of the present research proposal could be more useful to make smart farming more useful and intelligent with a lot of other benefits such as

1- Crop Diversification. The proposed framework might be modified to support the growth of other crops, even if it is currently best suited for the growing of vegetables. The UAE's ability to grow a range of crops would be expanded, improving food security there.

Expansion of Environmental Parameters: Including more environmental factors, such as air quality and carbon dioxide concentrations, can give a more complete picture of greenhouse conditions and improve crop growth.

Energy Sustainability: Investigating renewable energy options, such as solar energy, to meet the greenhouse's energy needs can increase sustainability and save operating expenses.

Collaborative Research: Working with academic institutions and industry professionals in agriculture can result in ongoing innovation and advancements in AI-driven agriculture. Sharing best practices and expertise is essential for the frameworks long-term viability.