Multimodal Artificial Intelligence in Precision Agriculture : Practices, Challenges, and Applications

The text presents various methods for applying machine learning and deep learning techniques to heterogeneous agricultural data and incorporating advanced Internet of Things-enabled technologies for web and mobile based applications for crop lifecycle tracking.

This book highlights the integration of cloud, edge, and network computing for sustainable farming and future trends in smart farming, including the role of UAVs, 5G, and advanced artificial intelligence. It covers the use of real-time environmental data for forecasting and tracking agricultural products and livestock health monitoring. Key FeaturesOffers a detailed exploration of the integration of Internet of Things, artificial intelligence, and multimodal intelligence in precision agriculture, covering a wide range of applications from crop management to livestock monitoringIdentifies and discusses the challenges of using artificial intelligence and multimodal data in agriculture, providing solutions and techniques to overcome these obstaclesDiscusses advanced technologies like multispectral and hyperspectral imaging, Internet of Things sensors, and data fusion techniquesHighlights emerging trends and future directions in smart farming, including UAVs, 5G, cloud-edge continuum integration, and federated learningIncludes case studies and practical examples demonstrating successful applications of multimodal artificial intelligence in precision farmingThe text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science engineering, agricultural engineering, and information technology.