AI and Machine Learning in Precision Agriculture: The Future of Agricultural Precision Agriculture
While the Internet of Things enables machine learning and artificial intelligence by collecting real-time data from operations, the real breakthrough will come from machine learning algorithms that can predict outcomes, maintain standards, and work on the farm.
Abstract
Agriculture is rapidly transforming with the integration of technologies such as machine learning (ML) and artificial intelligence (AI) to solve critical issues such as food security, climate change, and sustainable agriculture. Precision agriculture uses these technologies to increase yields, improve resource utilization, and reduce environmental impact. Machine learning techniques, particularly deep learning models such as convolutional neural networks (CNNs), have been successful in studying plant diseases, enabling early detection and reduction of crop losses. AI models improve decision-making by analyzing a wide range of agricultural data to predict crop yields, optimize irrigation schedules, and manage fertilization. These intelligent systems provide rapid insights, helping farmers make informed decisions and increase productivity and sustainability. While the Internet of Things enables machine learning and artificial intelligence by collecting real-time data from operations, the real breakthrough will come from machine learning algorithms that can predict outcomes, maintain standards, and work on the farm. Challenges such as high technology costs, complex data management, and implementation processes are only limited by time, but continuous advances in technology and research have the potential to transform agriculture by providing simple, effective, and practical solutions to today’s agricultural sector.