Survey on Advancing Multimodal Remote Sensing in Precision Agriculture

Modern technology, such as artificial intelligence and multimodal remote sensing, is driving the rapid advancement of precision agriculture. The review article examines the ways in which resource management and agricultural monitoring can be improved by the combination of machine learning (ML) and deep learning (DL) techniques with remote sensing modalities. This research investigates how recent advancements in sensor technology, including LiDAR, hyperspectral, and multispectral imaging, have transformed agricultural practices. Further discussed in the paper with reference to precision farming are the challenges of data fusion, algorithmic complexity, and the need for dependable, scalable solutions. The study discovers major trends and possible future paths for the field, including the combination of real-time analytics with AI-driven decision support systems, through a detailed examination of case studies and current research. The results set the stage for future investigations and advancements in this crucial field by emphasizing the role that ML and DL play in maximizing agricultural sustainability and productivity.