Research Paper On- Artificial Intelligence in Agriculture: Opportunities, Challenges and Policy Implications

In this intervention, I examine artificial intelligence (AI) in agriculture through a political ecology lens, analysing how promises of productivity, efficiency, and sustainability take shape across uneven postcolonial landscapes. Building on feminist and critical agrarian perspectives, I focus on the material relations of farming to show that AI in agriculture, often portrayed as immaterial, relies on deeply material infrastructures—sensors, data centres, energy systems, and extractive supply chains. Tracing how AI‐driven digital agriculture and smart agriculture reconfigure relations between farmers, land, and technology, I argue that care for agroecosystems is increasingly displaced by care for digital infrastructures, with dire consequences for the social and ecological relations that sustain rural labour, land, and livelihoods. Focusing on emergent applications of AI in agriculture in the Global South, I show how these technologies consolidate corporate power, erode agrarian knowledges, and reproduce postcolonial inequalities under neoliberal capitalism. I conceptualise AI as a socioecological fix, stressing the need to re‐spatialise and historicise technological change in the context of agrarian change, by attending to its infrastructural and ecological entanglements. I situate this analysis within debates on economies of repair, arguing that understanding AI in agriculture requires attention not only to breakdown and failure but also to the everyday practices of repair—technical, ecological, and social—that sustain digital infrastructures and reveal the uneven burdens of maintaining them across agrarian worlds. This intervention contributes to the Themed Intervention, 'Geographies of Responsibility, Care and Repair in Digital Worlds of AI'.

[1]. Artificial intelligence (AI) in agriculture has the potential to revolutionize farming practices, improving productivity, sustainability, and decision-making. This study explores the application of AI in agriculture, focusing on how AI-driven tools and systems can optimize various farming processes, such as crop management, pest control, and yield prediction. The research evaluates the role of AI in addressing challenges faced by farmers and its impact on agricultural efficiency. The findings highlight the significant benefits of AI technologies in modernizing agriculture and creating sustainable farming solutions. Research Significance: The significance of this research lies in its ability to showcase the transformative potential of artificial intelligence in agriculture. AI applications can enhance precision farming, reduce resource wastage, and improve crop yields, all of which contribute to better sustainability and profitability for farmers. The study explores how AI technologies can address key challenges in agriculture, such as labor shortages, unpredictable weather patterns, and the need for optimized resource use. The findings offer valuable insights into how AI can drive agricultural innovation and help ensure food security in an increasingly volatile global environment. Methodology: COPRAS The COPRAS (Complex Proportional Assessment) methodology is utilized to assess and compare various AI-driven agricultural solutions and technologies. By evaluating multiple alternatives based on their effectiveness in improving agricultural practices, COPRAS provides a structured approach to selecting the most suitable AI solutions. The methodology takes into account factors such as cost-effectiveness, efficiency, and scalability, helping to identify the most impactful AI technologies for different agricultural needs. Alternative: AI-based crop monitoring systems, AI-driven irrigation technologies, AIpowered pest detection tools, AI-enhanced yield prediction models, AI-assisted supply chain management. Evaluation Parameters: Product price (C1), Company rating (C2), Delivery time (C3), Transportation costs (C4). Result: The results of the COPRAS analysis demonstrate that AI-driven technologies have the potential to significantly improve agricultural outcomes. AIpowered systems that offer efficient crop monitoring, precise irrigation, and early pest detection provide high value to farmers. Technologies with lower costs and faster deployment times, such as AI-based irrigation systems, tend to rank higher in the evaluation, making them more attractive to farmers seeking affordable solutions. However, the effectiveness and scalability of the solution are also crucial factors influencing the overall ranking

The integration of Artificial Intelligence (AI) in agriculture has emerged as a transformative approach to enhance productivity, sustainability, and resource efficiency. This study focuses on the optimum utilization of AI technologies such as machine learning, computer vision, and data analytics in modern farming practices. AI enables precise monitoring of crop health, soil conditions, weather patterns, and pest infestations, thereby supporting data-driven decision-making. Techniques like predictive analytics help in yield forecasting, irrigation management, and disease detection, reducing losses and operational costs. Furthermore, the use of AI-powered automation, including smart irrigation systems and autonomous machinery, minimizes human effort while maximizing output. The paper highlights the challenges in implementation, such as high initial investment, lack of technical awareness, and data accessibility issues, particularly in developing regions. It also explores potential solutions to overcome these barriers for widespread adoption. Overall, the optimum utilization of AI in agriculture can significantly contribute to food security, environmental conservation, and sustainable development.

Revolutionizing agriculture: A comprehensive review on artificial intelligence applications in enhancing properties of agricultural produce.

Currently, the primary focus of every country worldwide is to enhance the efficiency of daily human life by embracing smart technology. Achieving a smart country status involves leveraging the internet, which enables various forms of smart work, predominantly through automation. Automation can be accomplished using Wireless Communication, Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (a subset of AI). Artificial Intelligence's role is constantly growing in human life and exerting a significant influence on society. In the context of Indian farming, Artificial Intelligence is progressively making its way and playing a vital role. In recent times, numerous countries have adopted machine learning and Artificial Intelligence in agriculture to predict and forecast crop yields, grade food, implement automated irrigation systems, and enhance the quality and quantity of crop production. This paper presents a comprehensive review of prior research conducted by various scholars, aiming to provide an overview of the applications and significance of Artificial Intelligence in agriculture, paving the way for automated and smart agricultural systems.

The integration of Artificial Intelligence (AI) in supply chain management has emerged as a transformative strategy to enhance efficiency, resilience, and adaptability in complex global logistics networks. This proposes a comprehensive model for AI integration in supply chain optimization, drawing comparative insights from the United States and Nigeria two economies with contrasting technological infrastructures, regulatory frameworks, and economic dynamics. The model emphasizes a multi-tiered approach incorporating predictive analytics, real-time decision-making, and intelligent automation. In the U.S., AI adoption is characterized by advanced infrastructure, data availability, and investment in digital transformation, enabling real-time inventory management, demand forecasting, and autonomous logistics. Conversely, Nigeria presents a context of infrastructural challenges and data limitations, yet exhibits increasing potential for AI-driven solutions through mobile technology, cloud computing, and emerging policy support. The proposed model comprises four core pillars: (1) AI-readiness assessment focusing on digital infrastructure and data maturity; (2) phased AI adoption strategy tailored to sector-specific needs; (3) stakeholder collaboration for technological transfer and capacity building; and (4) policy alignment to foster ethical AI use and cross-border interoperability. Through comparative analysis, the model identifies key enablers and barriers within each country, highlighting opportunities for technology leapfrogging in Nigeria and continuous innovation in the U.S. This argues that while the scale and mode of AI integration may differ, the strategic alignment of technology, talent, and governance is essential for effective supply chain optimization in both regions. This research contributes to the discourse on inclusive AI deployment by illustrating how adaptable frameworks can bridge technological disparities and drive supply chain resilience. Ultimately, it underscores the importance of localized strategies within a global model, fostering both economic growth and digital equity. The findings serve as a guide for policymakers, industry leaders, and technology developers seeking to harmonize AI integration across diverse socio-economic contexts.

Evaluation of a classroom-based medical imaging artificial intelligence educational intervention in Ghana: A pre-test/post-test study design.

This book is a platform for anyone who wishes to explore Artificial Intelligence in the field of agriculture from scratch or broaden their understanding and its uses. This book offers a practical, hands-on exploration of Artificial Intelligence, machine learning, deep Learning, computer vision and Expert system with proper examples to understand. This book also covers the basics of python with example so that any anyone can easily understand and utilize artificial intelligence in agriculture field. This book is divided into two parts wherein first part talks about the artificial intelligence and its impact in the agriculture with all its branches and their basics. The second part of the book is purely implementation of algorithms and use of different libraries of machine learning, deep learning and computer vision to build useful and sightful projects in real time which can be very useful for you to have better understanding of artificial intelligence. After reading this book, the reader will an understanding of what Artificial Intelligence is, where it is applicable, and what are its different branches, which can be useful in different scenarios. The reader will be familiar with the standard workflow for approaching and solving machine-learning problems, and how to address commonly encountered issues. The reader will be able to use Artificial Intelligence to tackle real-world problems ranging from crop health prediction to field surveillance analytics, classification to recognition of species of plants etc.

Digital technology innovations, particularly in artificial intelligence (AI), have the potential to significantly benefit agriculture. Agriculture is increasingly being held accountable for ensuring food security and safety while also taking into account environmental concerns. AI in the agricultural sector has the potential to feed a continuously growing global population while also contributing to achieving the UN's Sustainable Development Goals (SDGs). This was the right time to analyse and explore the artificial intelligence in agriculture. The present study was conducted in Dindigul district of Tamil Nadu. The findings indicated that farmers had moderately favourable level of attitude towards AI in agriculture. Farmers had positive attitude towards AI in agriculture because AI-enabled systems make weather predictions, monitor agricultural sustainability, and assess farms for the presence of diseases or pests and undernourished plants using data like temperature, precipitation, wind speed, and sun radiation in conjunction with photographs taken by satellites and drones.

Artificial intelligence (AI) is revolutionizing the agriculture sector by tackling key challenges such as climate change, resource scarcity, and the necessity for sustainable food production. The incorporation of AI technologies, like machine learning, IoT, and AIoT, offers transformative opportunities to enhance agricultural productivity, efficiency, and sustainability. Precision farming, predictive analytics, and agricultural robotics are examples of AI applications in agriculture that optimize resource utilization, enhance decision-making, and reduce reliance on labor. However, implementing artificial intelligence in agriculture poses several challenges. Issues such as data quality, connectivity, cost, privacy, and user acceptance need to be addressed before AI can fully unlock its potential. Additionally, ethical considerations and trust-building among farmers are crucial for the successful integration of AI technologies. Future research should focus on building advanced AI algorithms, increasing interoperability, and exploring AI's role in climate resilience and resource management. By tackling these challenges, AI can significantly contribute to the resilience and food security of agriculture.

The effect of medical artificial intelligence innovation locus on consumer adoption of new products

The Internet of Things (IoT) has ushered in a new era of innovative agriculture research. Because IoT is still in its early stages, it must be widely tested before it can be widely deployed in many agricultural applications. In this paper, I look at different prospective IoT applications, as well as the unique concerns and constraints connected with IoT deployment for better farming. The gadgets and wireless communication technologies linked with IoT in agricultural and farming applications are thoroughly researched to focus on the unique requirements. Sensor-enabled IoT systems that deliver intelligent and smart services for smart agriculture are being investigated.
 Several case studies are offered to investigate the existing IoT-based solutions implemented by various organizations, individuals, and groups depending on their deployment criteria. The difficulties in these solutions are addressed, as are the factors for improvement and the future roadmap of work with IoT. Smart farming is a trend that emphasizes the use of information and communications technology (ICT) in machinery, equipment, and sensors in network-based hi-tech farm supervision cycles. Innovative technologies such as the Internet of Things (IoT) and cloud computing are expected to spur growth and kickstart the usage of robotics and artificial intelligence in agriculture. Such radical departures are upsetting established agricultural practices while also posing several obstacles. This study explores the techniques and equipment utilized in wireless sensor applications in IoT agriculture, as well as the predicted problems encountered when integrating technology with traditional farming practices. Furthermore, this technical knowledge is useful to growers during crop times ranging from planting to harvest, and applications in packing and transportation are being researched.

The Internet of things (IoT) integrates technological systems using virtual network communication and cloud data bases. IoT applications are about to revolutionize the agricultural industry by new technologies of monitoring, operating and organizing the value cycles of crop and life-cattle production. Although a broad variety of individual IoT system for agriculture is available, so far, no industry-wide standard integrating several applications has established. This contribution presents a concept for a comprehensive artificial intelligence (AI) system which spans the whole farming value chain and operates on independent AI modules that are interconnected by a comprehensive cloud network. The approach builds a network of commercial system users, agricultural technology providers and IT experts and promises efficiency and sustainability improvements in agriculture.

The aim of the work is to provide an overview of the potential application of artificial intelligence in forensic medicine and related sciences, and to identify concerns related to providing medico-legal opinions and legal liability in cases in which possible harm in terms of diagnosis and/or treatment is likely to occur when using an advanced system of computer-based information processing and analysis. The material for the study comprised scientific literature related to the issue of artificial intelligence in forensic medicine and related sciences. For this purpose, Google Scholar, PubMed and ScienceDirect databases were searched. To identify useful articles, such terms as "artificial intelligence," "deep learning," "machine learning," "forensic medicine," "legal medicine," "forensic pathology" and "medicine" were used. In some cases, articles were identified based on the semantic proximity of the introduced terms. Dynamic development of the computing power and the ability of artificial intelligence to analyze vast data volumes made it possible to transfer artificial intelligence methods to forensic medicine and related sciences. Artificial intelligence has numerous applications in forensic medicine and related sciences and can be helpful in thanatology, forensic traumatology, post-mortem identification examinations, as well as post-mortem microscopic and toxicological diagnostics. Analyzing the legal and medico-legal aspects, artificial intelligence in medicine should be treated as an auxiliary tool, whereas the final diagnostic and therapeutic decisions and the extent to which they are implemented should be the responsibility of humans.

The rapid digital development of the recent era has revolutionized the overall network of business and management as well as other sectors. The book discusses how emerging technologies, artificial intelligence (AI), blockchain, big data analytics, cloud computing and the Internet of Things (IoT), have a high level of impact on different industries. With more and more businesses turning towards these techs, it is extremely important to really understanding their relation and use in order to remain at the cutting edge of technology while emerging as an innovator. Chapter 1 is a detailed introduction to the digital transformation, driven by AI, blockchain, IoT and other technologies are changing Business & Management processes. This paves the way for diving deeper into targeted topics where these innovations are starting to have a big influence. In chapter two we delve into the increasing popularity of generative AI, ChatGPT takes center stage and how it is impacting range of industries. In this research, we review generative AI applications and opportunities as well as challenges and the outlook, for future development. Also touched on in the book are the customer front-exporting portions of digital renovation. Chapter 3 - Overall Vision: How AI, Machine Learning and related technologies help in higher customer satisfaction and loyalty right into the services industry to build exceptional service quality.