Optimizing Agricultural Decision-Making with Artificial Intelligence: A COPRAS-Based Evaluation

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

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.

With the continuous growth of the global population, agricultural production faces unprecedented challenges. Traditional agricultural methods exhibit numerous deficiencies in food production, quality control, and environmental protection, necessitating urgent intervention by modern technologies. This paper investigates the application of artificial intelligence (AI) technology in the agricultural sector, exploring its significant contributions to enhancing agricultural production efficiency, precision, and sustainability. Drawing on the research findings of several scholars, this paper demonstrates specific applications of AI in soil moisture classification, unmanned aerial vehicle (UAV) thermal imaging for nest detection, and crop health monitoring. Additionally, this paper discusses the crucial role of AI technology in the modernization of agriculture in China, particularly its potential in cultivating intelligent agricultural talents and promoting high-quality agricultural development. In summary, AI is injecting new vitality into modern agriculture with its unique advantages, driving agriculture towards green and sustainable development. Through efforts in intelligent machinery, precision agriculture, and talent development, AI presents a promising outlook for its application in agriculture, offering new perspectives and solutions to address global agricultural challenges.

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'.

The world's expanding population and dwindling agricultural land resources pose a serious threat to future food security. Conventional farming practices are losing their capacity to maintain productivity in an unpredictable climate. Artificial Intelligence has the capability to transform many of industries, including soil analysis, crop management, irrigation optimization, pest surveillance, weed detection, and post-harvest processing. This review evaluates the effects of recent advancements in AI-driven agricultural solutions on input efficiency, sustainability, and profitability. Because AI enabled systems outperform conventional statistical methods as of speed, accuracy and scalability, they are crucial tools for modern precision farming. The integration of AI with IoT, satellite imaging, cloud computing, and deep learning models holds great promise for developing intelligent, real-time agricultural ecosystem frameworks. This review also highlights various applications of Artificial Intelligence in agriculture field.

Artificial Intelligence (AI) is emerging as a transformative force in the agricultural sector by improving productivity, optimizing resource utilization, and promoting sustainable farming practices. The integration of AI technologies such as machine learning, computer vision, robotics, Internet of Things (IoT), and big data analytics has enabled precision agriculture, smart irrigation systems, yield prediction models, pest and disease detection, and supply chain optimization. In countries like India, where agriculture remains a major source of livelihood, AI has the potential to address structural issues such as low productivity, climate variability, fragmented landholdings, and market inefficiencies. AI-powered platforms assist farmers in real-time decision-making by analyzing weather data, soil conditions, crop health, and market trends. Despite its transformative potential, the adoption of AI in agriculture faces several challenges including high initial costs, inadequate digital infrastructure, lack of technical awareness, data privacy concerns, and limited access to quality datasets. This research paper examines the role of AI in agriculture, evaluates its economic and social implications, and identifies the challenges associated with its implementation. The study concludes that effective policy support, public-private partnerships, capacity-building initiatives, and digital infrastructure development are crucial to maximizing the benefits of AI in agriculture.

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.

Given the rapidity with which artificial intelligence is gaining momentum in clinical medicine, current physician leaders have called for more incorporation of artificial intelligence topics into undergraduate medical education. This is to prepare future physicians to better work together with artificial intelligence technology. However, the first step in curriculum development is to survey the needs of end users. There has not been a study to determine which media and which topics are most preferred by US medical students to learn about the topic of artificial intelligence in medicine. We aimed to survey US medical students on the need to incorporate artificial intelligence in undergraduate medical education and their preferred means to do so to assist with future education initiatives. A mixed methods survey comprising both specific questions and a write-in response section was sent through Qualtrics to US medical students in May 2021. Likert scale questions were used to first assess various perceptions of artificial intelligence in medicine. Specific questions were posed regarding learning format and topics in artificial intelligence. We surveyed 390 US medical students with an average age of 26 (SD 3) years from 17 different medical programs (the estimated response rate was 3.5%). A majority (355/388, 91.5%) of respondents agreed that training in artificial intelligence concepts during medical school would be useful for their future. While 79.4% (308/388) were excited to use artificial intelligence technologies, 91.2% (353/387) either reported that their medical schools did not offer resources or were unsure if they did so. Short lectures (264/378, 69.8%), formal electives (180/378, 47.6%), and Q and A panels (167/378, 44.2%) were identified as preferred formats, while fundamental concepts of artificial intelligence (247/379, 65.2%), when to use artificial intelligence in medicine (227/379, 59.9%), and pros and cons of using artificial intelligence (224/379, 59.1%) were the most preferred topics for enhancing their training. The results of this study indicate that current US medical students recognize the importance of artificial intelligence in medicine and acknowledge that current formal education and resources to study artificial intelligence-related topics are limited in most US medical schools. Respondents also indicated that a hybrid formal/flexible format would be most appropriate for incorporating artificial intelligence as a topic in US medical schools. Based on these data, we conclude that there is a definitive knowledge gap in artificial intelligence education within current medical education in the US. Further, the results suggest there is a disparity in opinions on the specific format and topics to be introduced.

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.

This paper gives an idea in regards with the impact of Artificial Intelligence in Agrarian Sector. As India is an Agrarian Country, our economy is uncommonly dependent upon the yielding of harvests and its most prominent effectiveness. Most of people in our country depends upon cultivation for their everyday requirements. The usage of Artificial Intelligence close by its capacity of learning can give an enormous impact on the country sector.AI development helps in distinctive sickness in plants, vermin and sad sustenance of residences. Man-created insight sensors can recognize and target weeds and subsequently pick which herbicide to apply inside the area. This assistants in lessened utilization of herbicides and cost save reserves. Various mechanical associations make drobots, which use PC vision and man-made awareness to screen what's more, unequivocally shower on weeds. This paper moreover discusses the new developments that opens the way to a Smart Agriculture .This paper gives an idea in regards with the impact of Artificial Intelligence in Agricultural Sector.

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.

Sustainability is a holistic goal that can be effectively achieved through the combined efforts of agriculture and its allied sectors. Artificial intelligence (AI) plays a transformative role in this endeavour by bridging sector-specific solutions and integrating them to promote environmental protection and food security. AI is revolutionizing sustainable agriculture, ensuring both food security and environmental protection. The main objective of this article is to comprehensively review the various applications of AI in agriculture and its interlinked sectors like fishery, animal husbandry, forestry, agricultural engineering, horticulture and food science by compiling several previous studies to highlight their role in achieving sustainability and identify research gaps. The literature review was done through databases like Scopus and Google Scholar. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) framework was used to identify, screen and select articles. This article explores the various applications of AI in pest and disease management, weed management, weather forecasting, soil management, greenhouse farming, precision agriculture and yield management. AI has numerous advantages, such as data-driven decision-making, resource management and reduced environmental impacts. This review highlights the implementation of inclusive strategies to achieve sustainability by pointing out the gaps in research, policy and implementation of technologies. The review concludes that integrating AI into agriculture and its allied sectors offer significant benefits that outweigh potential drawbacks, thereby fostering sustainable practices and environmentally friendly innovations.

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.

Agriculture is playing vital role for the survival of mankind but due to lack of knowledge and decision support will leading us toward less production in our country. Data are analyzed and got clustered according to the requirement of the user which predict the better scenario for the production of the respective crop. Artificial Intelligence (AI) entered into Indian Agriculture and it is affecting our society at large. Even though machine learning (which is a subset of AI) has been used for classifications and prediction purposes for, to cite a few, food grading and crop yield forecasting, recently, the new set of deep learning and data science algorithms shows that the possibilities of taking the research and applications of AI to much higher levels and with much more accuracy. In the same way, other AI techniques are showing ways in all areas including agriculture. AI will help the common man and also transform his thoughts, attitude and mind set towards the benefits that it may bring, there are some disadvantages of such sophisticated technologies as well. In this paper, we will discuss about using modern day AI to solve traditional farming problems, life cycle agriculture, challenges faced by farmers by using AI and applications of AI in agriculture.

This review explores the application of AI technology in agriculture to address challenges such as declining manual labor, limited arable land, and the growing disparity between food production and the increasing global population. AI is presented as a promising solution, with advancements driven by scientists worldwide. Artificial Intelligence (AI) plays a crucial role in optimizing farming practices by analysing data from sensors, satellites, and drones. Applications include monitoring soil health, crop growth, and weather patterns to enhance yields while minimizing resource utilization. AI-powered tools, such as advanced irrigation systems and fertilizer management, ensure efficient use of inputs. The use of robots in agriculture has significantly improved productivity, making farming more efficient and widely adopted. AI techniques offer real-time data, reducing human error and enhancing decision-making. The research highlights that modern AI technology and methods outperform traditional farming methods with minimal human intervention and in a shorter time frame. The review also delves into the development of agricultural robots, highlighting various examples of robots designed for specific tasks within the agricultural industry. The review discusses the challenges faced in applying agricultural robots, particularly the unpredictability of real-world environments. Despite these challenges, it underscores significant advancements in this field and the promising prospects for the future of agricultural robotics.