Intelligent Remote Sensing for Precision Agriculture

Agriculture-related remote sensing applications are rapidly becoming the norm in precision agriculture. Accurate, consistent and reliable information on field conditions during the growing season optimizes field management for precision agriculture, ensuring sustainable agricultural production and desirable environmental outcomes. 

Recent advances in Earth observation technology, especially as more and more available, affordable, and compatible platforms (e.g., Unmanned Aerial Vehicles - UAVs) and sensors (e.g., lightweight multispectral, hyperspectral, thermal, and LiDAR) , can obtain images of unprecedented high spatial, spectral and temporal resolution for precision agriculture practices. 

Data collection, processing and analysis based on artificial intelligence (AI) and quantitative modeling have shown some intelligence in precision agriculture, but still face many technological challenges in preprocessing, data extraction and synthesis, quantitative analysis, information transmission, etc. Due to multi-scale, multi-sensor and multi-platform, multi-temporal Earth observation.

Therefore, new research is needed to develop improved image acquisition and transmission techniques, address various issues related to image preprocessing and cross-sensor integration, simplify data processing for field-level plant condition retrieval, and implement artificial intelligence to support decision-making .