4 research outputs found
Automation and AI in Precision Agriculture: Innovations for Enhanced Crop Management and Sustainability
No full textPrecision agriculture is one of the ways to achieve food security and sustainability through better resource-use optimization and crop productivity dealing with the challenges posed by the growing population and addressing environmental concerns. The study offers an in-depth look at the most recent developments in artificial intelligence (AI) and automation in precision agriculture (PA), with a particular emphasis on important technologies such as drones, autonomous tractors, AI-driven irrigation systems, and predictive analytics for crop management. The accuracy of crop monitoring and health assessments has increased by 30–50 percent as a result of AI-powered solutions, which have improved resource-based decision-making. Systems for precision irrigation and fertilization have increased crop yields by 5–15 percent when using 25–40 percent less water and 30-40 percent less fertilizer, respectively. Robotic harvesters and sprayers are examples of automation technologies that have reduced labor expenses by 20–40 percent and increased operational efficiency by 35 percent. Additionally, AI-based prediction models have reduced pest damage by 20–25 percent and reached an accuracy of 85–90 percent for crop yield forecasts and pest control. Despite these developments, issues of scalability, affordability for small farms, and data privacy still exist, which can hinder technology adoption among farmers. The evaluation follows by outlining ideas for future research, such as 5G, blockchain, and AI integration with cloud and edge computing. These technologies could improve decision-making and transparency in precision agriculture by enabling real-time data transmission, secure data management, and enhanced traceability, thus addressing current limitations and fostering trust among stakeholders
Advances in Vertical Farming: The Role of Artificial Intelligence and Automation in Sustainable Agriculture
No full textVertical farming has emerged as a sustainable agricultural method, resolving the issues of land scarcity, environmental consequences, and food security in urban and highly populated areas. The inclusion of artificial intelligence (AI) and automation into vertical farming systems improves their efficiency, production, and adaptability. The study highlights recent breakthroughs in AI-driven systems, spanning data analytics, predictive modeling, and autonomous control, which enhance critical parameters such as light, temperature, humidity, and nutrient delivery. Significant advancements in agricultural automation, including robotic technologies for planting, monitoring, and harvesting, are emphasized for their capacity to decrease labor expenses and enhance yield accuracy. Further, research evaluates the environmental effect, scalability, and practicality of automated vertical farming systems, examining the contribution of renewable energy and optimal use of resources to the development of resilient food production methods. This discussion addresses future directions and issues seeking to shed light on how AI and automation are shifting vertical farming into an important aspect of sustainable agriculture
Climate-Smart Agriculture: AI-Based Solutions for Enhancing Crop Resilience and Reducing Environmental Impact
No full textClimate change poses significant challenges to global food security, necessitating the use of AI-based climate-smart agriculture (CSA) technologies to improve crop resilience, reduce environmental impact, and optimize resource use. AI-based interventions can reduce carbon emissions by 30–50% and boost agricultural productivity by up to 25%. Machine learning approaches can forecast crop yields with 90% accuracy, facilitating climate adaptation. AI insect surveillance can reduce pesticide application by 30%, and artificial irrigation systems can save up to 40% water. IoT sensors and remote sensing improve soil health monitoring and carbon sequestration practices, increasing soil organic carbon stocks by 20–35%. AI-powered predictive analytics can provide early alerts for storms, reducing agricultural losses by 15–20%. Automation and robotics can reduce post-harvest losses by up to 35%. Blockchain and AI can ensure transparency in sustainable agricultural supply chains and carbon credit markets. This blending of AI and CSA can significantly reduce climate change implications. The use of AI in smallholder agriculture faces challenges such as inflated implementation costs, reduced digital literacy, and concerns around data privacy. Fixing these issues requires economical solutions, agricultural training initiatives, localized artificial intelligence models, and legislative changes
Advances in Vertical Farming: The Role of Artificial Intelligence and Automation in Sustainable Agriculture
No full textVertical farming has emerged as a sustainable agricultural method, resolving the issues of land scarcity, environmental consequences, and food security in urban and highly populated areas. The inclusion of artificial intelligence (AI) and automation into vertical farming systems improves their efficiency, production, and adaptability. The study highlights recent breakthroughs in AI-driven systems, spanning data analytics, predictive modeling, and autonomous control, which enhance critical parameters such as light, temperature, humidity, and nutrient delivery. Significant advancements in agricultural automation, including robotic technologies for planting, monitoring, and harvesting, are emphasized for their capacity to decrease labor expenses and enhance yield accuracy. Further, research evaluates the environmental effect, scalability, and practicality of automated vertical farming systems, examining the contribution of renewable energy and optimal use of resources to the development of resilient food production methods. This discussion addresses future directions and issues seeking to shed light on how AI and automation are shifting vertical farming into an important aspect of sustainable agriculture
