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Exploring the IoT and AI Technologies in Energy-Efficient Sustainable Agriculture

M. Muthumalathi1*, Ponnarasi Loganathan1, P.B. Pankajavalli1┼ and A. Priya Dharshini2

1Department of Computer Science, Bharathiar University, Coimbatore, India

2School of Management, Kumaraguru College of Liberal Arts and Science, Coimbatore, India

Abstract

Agriculture plays a major role in a country's economy, and it is significant for its social structure. The increasing demand for food production leads traditional farming approaches to smart farming by incorporating various modern technologies like the Internet of Things (IoT) and artificial intelligence (AI). This sort of change enhances the efficiency, sustainability, and resilience in the agricultural domain. In agriculture, various challenges are faced by the farmers, like irrigation management, soil monitoring, pesticide application, and disease prediction in crops. The incorporation of modern technologies like IoT and AI enhances modern agriculture by creating an intelligent and robust ecosystem. The usage of smart technology in agriculture majorly depends on the IoT, which enables farmers to decrease manual labor work and, at the same time, increase productivity and enables farmers to remotely monitor their fields. IoT devices can collect agricultural data such as soil conditions, crop health, and weather patterns. AI technology helps to analyze data by implementing data-driven decision-making and improving agricultural methods in order to produce practical insights. IoT and AI will have a greater influence on traditional farming practices through various applications in smart farming. This chapter explores the effective utilization of IoT and AI in agriculture.

Keywords: Internet of Things, artificial intelligence, IoT device, agriculture and smart farming, sensor technologies

1.1 Introduction to the Internet of Things and Artificial Intelligence

The Internet of Things (IoT) is a collection of interconnected devices that are enabled with sensors, actuators, software, and other technologies that help to extract data from each connected device. The devices are used for household appliances, industry machines, vehicles, wearables, and infrastructure components [1]. In essence, the IoT is a system that allows physical objects to seamlessly integrate into the digital world, in a way that allows them to communicate, share data, and operate intelligently without the need for human intervention. Applications of IoT are shown in Figure 1.1.

AI involves developing a system that can carry out tasks that typically require human intelligence. In this field, algorithms and models are developed that simulate intellectual functions such as problem-solving, learning, reasoning, perception, and decision-making. AI is aimed at enhancing efficiency, productivity, and problem-solving across a wide range of industries and domains by replicating or surpassing human capabilities.

Figure 1.1 Applications of IoT.

AI is categorized into two types: narrow/weak AI and general/strong AI. The narrow artificial intelligence is designed to perform specific tasks within predetermined parameters. A few examples include virtual assistants, such as image recognition software, recommendation systems, speech recognition software, autonomous vehicles, and Alexa. Alternatively, general AI refers to machines capable of comprehending, learning, reasoning, and performing tasks across a range of domains. These sorts of AI levels are still theoretical and remain a work in progress. Using data, AI systems are able to learn and improve. A subset of AI known as machine learning allows systems to recognize patterns, make predictions, and adapt to new information without explicit programming. A subset of machine learning, i.e., deep learning, employs neural networks that are modeled based on the human brain's structure and function to process large volumes of data and derive meaningful insights.

There are many applications of AI across a variety of industries, including healthcare, finance, education, automotive, entertainment, and so on. In addition to innovations in medical diagnostics, personalized recommendations, fraud detection, autonomous vehicles, natural language processing, and robotics, AI has led to innovations in a number of other fields. Figure 1.2 shows the application of AI.

Human survival has been dependent on agriculture for thousands of years. Over the past few decades, agricultural practices have evolved from traditional, time-consuming methods [2]. In view of the projected population of 8.9 billion by 2050, there is a pressing need to align supply and demand by utilizing advanced technologies [3] to increase food production [4, 5]. A total of 70% of the global freshwater supply is derived from agriculture. As a result of climate change and limited resources, it will be increasingly difficult to produce adequate and high-quality food [6].

Figure 1.2 Applications of AI.

As a result of smart agriculture, resources are conserved and sustainable farming practices are promoted [7]. Numerous studies have emphasized the uptake and utilization of IoT in farming, irrigation, and agriculture. Research on agricultural technologies is conducted worldwide by private companies and organizations. These initiatives involve not only mechanical and economic considerations but also engineering, retail food, and computer technology. It is crucial to note that several issues are related to agricultural processes, such as fragmentation, the administration of intelligent machinery, the sharing and administration of data, and the examination and retention of data. Consequently, it is important to foster collaboration between stakeholders, systems, and technologies in order to develop standards for smart agriculture [8].

The integration of IoT and AI can bring about a constructive revolution in traditional agriculture [3], including (a) enhancing data collection using smart agricultural devices; (b) monitoring and regulating the internal processes of the smart farming environment, such as harvesting and storing crops; (c) reducing waste and costs; (d) improving business efficiency through automation of conventional methods; and (e) cultivating product excellence and productivity [9]. A major challenge is the provision of accurate and timely information to farmers [10]. Consequently, artificial intelligence can be used to address urgent agricultural issues. The challenges faced by traditional agriculture can therefore be addressed by artificial intelligence. In recent years, the exploration and implementation of artificial intelligence in various fields have been considered, including (a) intelligent farming, (b) robotics, (c) optimizing agriculture practices, (d) automated processes, (e) knowledge-driven agricultural systems, (f) agricultural expert systems, and (g) decision-making assistance systems.

Smart sustainable agriculture (SSA) lacks research and development and is complicated by the challenges stemming from fragmented agricultural processes. These challenges encompass managing IoT and AI equipment, managing and sharing data, interoperability, and the storage and analysis of large datasets. The objective of this study is to address fragmentation in conventional agricultural practices and promote global research and development in smart agriculture by creating a smart, sustainable agriculture platform. To accomplish this goal, the study aims to identify current IoT/AI technologies utilized in SSA and establish a technical architecture for IoT/AI to bolster SSA platforms.

1.2 Essentials of IoT and AI Applications in Agriculture

IoT and AI have shown remarkable potential for transforming agriculture, revolutionizing traditional farming practices, and optimizing crop production.

The following are the essential applications of AI and IoT in agriculture:

1.2.1 IoT Applications in Agriculture

1.2.1.1 Monitor and Sense

• IoT equipment like sensors, drones, and satellite imagery are utilized to gather real-time data on different agricultural variables like humidity, temperature, soil moisture, and crop health.
• Using IoT to monitor and manage crop conditions across large fields allows farmers to make precise decisions.

1.2.1.2 IoT-Enabled Precision Agriculture

• In precision farming, data from sensors helps to optimize resource usage like water, fertilizers, and pesticides based on the crop requirements.
• Importance: Highlighting the role of automated irrigation systems guided by IoT data in preserving resources and enhancing crop yields.

1.2.1.3 Monitor Livestock

• Monitoring livestock health, activity levels, and location using IoT-enabled wearables and tracking devices.
• Impact: Using real-time monitoring to detect diseases early, ensuring better livestock management.

1.2.1.4 Managing the Supply Chain

• IoT technology ensures food safety, quality control, and efficient logistics by tracking and tracing produce from farm to market.
• Benefits: IoT maintains traceability and transparency throughout a supply chain.

1.2.2 AI Applications in Agriculture

1.2.2.1 Soil Management

A key factor for successful agriculture is the soil, which is the source of all nutrients, including...