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AI and Emerging Technologies for Precision Agriculture: A Survey

Brajesh Kumar Khare

Department of Computer Science and Engineering, Harcourt Butler Technical University, Kanpur, Uttar Pradesh, India

Abstract

The urgent need for sustainable agriculture practices is increasingly recognized in light of escalating global food demands and environmental pressures. This paper examines the role of artificial intelligence (AI) and other emerging technologies including Internet of Things, blockchain, robotics and automated machinery, and vertical farming in optimizing agricultural processes to achieve sustainability goals. These technologies promise enhanced efficiency, reduced resource consumption, improved crop yields, and minimized environmental impacts. Robotics and automated machinery represent transformative tools in reducing labor costs and increasing precision, whereas vertical farming offers a sustainable solution to urban food production challenges by minimizing land use and optimizing water and nutrient cycles. Additionally, this paper discusses the significant barriers to technology adoption, such as data privacy concerns, lack of infrastructure, and the need for farmer training and technological literacy. This paper also explores future research directions in the field of agriculture. Key areas include AI-driven precision irrigation and nutrient management to optimize resource use, climate resilience modeling to mitigate climate change impacts, and data integration platforms utilizing big data and blockchain for transparency and efficiency. Enhanced remote sensing techniques will provide detailed insights into crop and soil health, whereas user-friendly AI interfaces and farmer training programs will ensure widespread adoption.

Keywords: Agriculture, Internet of Things (IoT), blockchain, machine learning (ML), sensor

1.1 Introduction

Farming, essential for human survival since ancient times, now faces significant hurdles due to environmental shifts, rising populations, and growing food needs. To address these challenges, a new approach called precision agriculture has emerged, blending AI with advanced technologies. This innovative field aims to revolutionize traditional farming methods through research and technological progress. Precision agriculture strives to maximize crop yields by harnessing sophisticated technologies to collect and analyze data on diverse elements, including soil characteristics, meteorological conditions, crop vigor, and pest infestations. By combining this data with AI algorithms, precision agriculture can provide farmers with actionable insights and recommendations tailored to their specific fields and crops. The need to address the many issues confronting the global agricultural sector-such as the need for sustainable practices, resource constraints, climate change, and increased food production to feed a growing population-is the foundation of emerging technologies in agriculture. The United Nations projects that the global population will hit 9.7 billion by 2050. This figure represents a significant increase from the current world population and highlights the pressing need to address challenges related to food security, resource management, and sustainable development to support the growing number of people inhabiting our planet over the next few decades [1]. This population growth places tremendous pressure on agriculture to produce more food, often referred to as the "food security challenge." Farming activities require vast quantities of land, water, and energy resources. As the availability of these vital resources diminishes over time, it is becoming increasingly crucial to adopt more efficient and environmentally friendly agricultural methods that promote sustainability and conservation [2]. The phenomena of climate change are having a profound impact on agricultural practices around the world. Rising global temperatures, shifts in rainfall and precipitation patterns, as well as an escalation in the occurrence of severe weather events are all factors contributing to disruptions in the agricultural sector. These climate-related challenges pose significant threats to crop yields, food production, and the overall sustainability of farming activities on a global scale [3]. This necessitates adaptation strategies and mitigation efforts within the agricultural sector [4]. Modern information technologies are being leveraged in the concept of "smart farming" to enhance and optimize the intricate systems involved in agricultural operations. Information and communication technologies are being integrated into agricultural production systems with the goal of improving overall efficiency and yields. Agriculture is one of the key industries in the field of production, given its critical role in sustaining human populations.

AI techniques, play a pivotal role in precision agriculture. These techniques enable the analysis of vast amounts of data including remote sensing devices and soil sensors. AI algorithms analyze agricultural data to identify patterns, enabling predictions and recommendations for crop yields, irrigation, fertilizer use, and pest control. This data-driven approach helps potentially improving agricultural productivity and sustainability.

Emerging technologies, such as Internet of Things (IoT) devices, drones, and robotics, are also revolutionizing precision agriculture. IoT sensors embedded in fields can continuously monitor Environmental conditions, providing real-time data for AI algorithms to process. Unmanned aerial vehicles, commonly known as drones, outfitted with advanced multispectral imaging cameras, possess the capability to capture exceptionally high-resolution visual data of crop fields. This technological application allows for the timely identification of early warning signs that crops may be experiencing stress or deficiencies in essential nutrients. Moreover, robotic systems designed specifically for agricultural applications can automate and take over labor-intensive tasks such as planting seeds, removing unwanted vegetation, and harvesting mature crops. The integration of these robotic solutions into farming operations has the potential to significantly reduce labor costs while concurrently boosting overall operational efficiency.

As AI and emerging technologies continue to evolve, their potential applications in precision agriculture will expand, paving the way for a more efficient, sustainable, and productive agricultural sector globally.

1.2 Precision Agriculture

Precision agriculture represents an innovative and technologically advanced approach to farming that aims to optimize multiple facets of agricultural productivity. This methodology employs data-driven strategies and cutting-edge technologies to precisely manage and allocate critical resources like pesticides and seeds according to the specific conditions present within different areas of a cultivated field. The overarching objectives of precision agriculture are to boost crop yields, minimize the costs associated with agricultural inputs and resources, and reduce the environmental impacts of farming activities, all while promoting the long-term sustainability of agricultural practices. By precisely tailoring resource allocation to the unique needs of localized sections within a field, precision agriculture techniques strive to maximize efficiency, profitability, and environmental stewardship across the entire farming operation [5]. Data collected from diverse sources, including Global Positioning System (GPS), soil sensors, and drones. These data are meticulously analyzed to comprehend field variability, assess crop conditions, and inform decision-making [6]. Variable rate technology (VRT) facilitates the pesticides and irrigation based on specific field conditions. This tailored approach ensures efficient resource utilization and minimizes wastage [7]. GPS technology is integral for precise field mapping, navigation, and the georeferencing of data. It enables the accurate tracking of field operations and the creation of detailed field maps [8]. At the core of precision agriculture lies the collection and analysis of data from remote sensing devices, weather stations, soil sensors, and yield monitors. This data is then processed and interpreted using specialized software and algorithms, often incorporating machine learning (ML) and AI techniques. Precision agriculture relies heavily on zone-based field management, a technique that segments farmland into distinct areas according to various agricultural parameters including soil composition, landscape features, and past production records. This approach recognizes that even within a single field, there can be significant variations in soil fertility, moisture levels, and other factors that affect crop growth and productivity. By accurately mapping and analyzing these variations, precision agriculture allows for the precise application of inputs such as fertilizers, pesticides, and water. VRT enables tailored input application based on each field zone's unique requirements. VRT systems are often integrated with GPS-guided machinery and automated control systems, ensuring accurate and efficient application of inputs.

Another critical aspect of precision agriculture is cropping monitoring and yield mapping. Advanced sensing technologies, such as multispectral and hyperspectral imaging systems mounted on drones or satellites, can provide detailed information about crop health, nutrient status, and potential yield. Precision data collection identifies field areas needing specific treatments. Yield mapping...