AI and sustainable agriculture have emerged as two intersecting fields with the potential to revolutionize food production and address pressing environmental challenges. This chapter explores the integration of AI technologies into agricultural practices to enhance efficiency and productivity while considering the ethical implications associated with this transformation. The application of AI in sustainable agriculture offers numerous benefits, including improved crop yield, resource optimization, and enhanced pest and disease management. Nevertheless, there are also moral issues with the use of AI in agriculture. Data privacy, security, and equitable access to AI technologies are essential to prevent data exploitation and uphold an equitable agricultural environment. The ethical concerns brought up by the application of AI in agriculture must be addressed in order to create an equitable, inclusive, and ecologically responsible agricultural system.
Top1. Introduction
Artificial intelligence (AI) is one of the rapidly developing digital technologies in agriculture (Ryan et al., 2023). A promising partnership between artificial intelligence (AI) and the agriculture industry has evolved, and it has the potential to change how resources are managed and how food is produced around the world (Hesham, 2023). As we struggle to produce more food with fewer resources, artificial intelligence has the ability to lead an agricultural revolution (Vijayakumar et al., 2022). The primary goal of artificial intelligence (AI) is to develop machinery that functions identically to the human brain. With the aid of AI, it is feasible to collect immense quantities of data from public and open-access websites, analyze it, and provide farmers with solutions to a variety of intricate issues as well as a more intelligent method to handle inputs by taking into account all the field's variations, which results in a higher yield (Vijayakumar et al., 2022). As a result, farmers will be able to use inputs more effectively and maximize agricultural returns. AI may also collect market trends, annual production, and customer wants data. The automated irrigation system will cut down on the amount of labor and time needed for irrigation. It also greatly aids in water conservation (Vijayakumar et al., 2022).
AI research has expanded, investments in AI solutions have surged, processing power has greatly increased, and computer and cloud technologies are now more widely available and reasonably priced. AI is becoming a technology that is within reach for agricultural businesses (Ryan et al., 2023). Between 2020 and 2026, the compound annual growth rate (CAGR) is anticipated to climb by 25.5%. (Ryan et al., 2023), it is anticipated that the agricultural sector will see substantial investments in AI in the upcoming years. AI has the potential to alter how agribusinesses are structured, contend, and participate in the food chain. By making proactive efforts to advance technology and inspiring companies and farmers to reap the gains of AI (higher output levels, decreased pesticide use, and reduced environmental impact), these will be made possible (Ryan et al., 2023). Along with tackling labor shortages and the compelling need to increase output while reducing damaging environmental outputs, AI will also be able to handle some of society's most glaring problems (Ryan et al., 2023).
Agricultural technology answers to our environmental and developmental issues are nothing new with the widespread usage of precision agriculture technologies like GPS guidance, VRT, and yield mapping (Franzen & Mulla, 2015). A new era of precision farming has begun as a result of AI's integration in agriculture (Hesham et al., 2023). By improving managerial chores and making decisions based on data, smart farming expands precision agriculture (Wolfert et al., 2014). To give farmers useful insights, machine learning algorithms analyze a variety of datasets, including as satellite imaging, weather predictions, and soil samples (Hesham et al., 2023). These practical observations support decision-making and farm management, raising precision agriculture to a new level and advancing it toward digital/smart agriculture (Shepherd et al., 2020). These findings cover everything from predicting crop illnesses and irrigation schedule optimization to picking the best times to plant (Hesham et al., 2023). Increased output, decreased resource waste, and improved environmental sustainability are the end results. Crop monitoring and pest control are additional applications of AI. Crop health can be evaluated in real time using remote sensing technology like drones and satellites with AI-enabled picture recognition. Farmers are better equipped to respond quickly to dangers thanks to this proactive strategy to monitoring (Hesham et al., 2023). Additionally, AI-driven pest detection systems offer early intervention, reducing the requirement for exorbitant pesticide use (Hesham et al., 2023). Precision agriculture will be enhanced by digital/smart farming, which will incorporate cutting-edge technological advancements such artificial intelligence (Ryan et al., 2023).