The Role of Artificial Intelligent in Agricultural Applications: Trend Methods
Özet
Artificial Intelligence (AI) is increasingly transforming agriculture by introducing advanced methods that enhance productivity and sustainability. Modern AI applications in agriculture encompass several key areas: Precision Farming: AI algorithms analyze data from various sources, including satellite imagery and soil sensors, to optimize planting schedules, irrigation, and fertilization, thereby improving crop yields and resource efficiency. Crop Monitoring and Disease Detection: Machine learning models and computer vision techniques enable early detection of plant diseases and pest infestations, allowing for timely interventions and reducing crop losses. Robotics and Automation: AI-powered robots are employed for tasks such as planting, harvesting, and weeding, increasing operational efficiency and reducing labor costs. Supply Chain Optimization: AI assists in forecasting demand, managing inventory, and optimizing logistics, leading to reduced waste and improved market responsiveness.
Despite these advancements, challenges persist, including data quality issues, infrastructure limitations, and the need for regulatory frameworks to ensure safe and ethical AI deployment. Addressing these challenges requires collaborative efforts among policymakers, researchers, and industry stakeholders to develop standards and invest in education and infrastructure.
This paper explores the current trends and methodologies of AI applications in agriculture, highlighting their potential to transform the sector and contribute to global food security and environmental sustainability.
