Abstract
Digital farming approach merges new technologies and sensor data to optimize the quality of crop monitoring in agriculture. The successful fusion of technology and data is highly dependent on the parameter collection, the modeling adoption, and the technology integration being accurately implemented according to the specified needs of the farm. This fusion technique has not yet been widely adopted due to several challenges; however, our study here reviews current methods and applications for fusing technologies and data. First, the study highlights different sensors that can be merged with other systems to develop fusion methods, such as optical, thermal infrared, multispectral, hyperspectral, light detection and ranging and radar. Second, the data fusion using the internet of things is reviewed. Third, the study shows different platforms that can be used as a source for the fusion of technologies, such as ground-based (tractors and robots), space-borne (satellites) and aerial (unmanned aerial vehicles) monitoring platforms. Finally, the study presents data fusion methods for site-specific crop parameter monitoring, such as nitrogen, chlorophyll, leaf area index, and aboveground biomass, and shows how the fusion of technologies and data can improve the monitoring of these parameters. The study further reveals limitations of the previous technologies and provides recommendations on how to improve their fusion with the best available sensors. The study reveals that among different data fusion methods, sensors and technologies, the airborne and terrestrial LiDAR fusion method for crop, canopy, and ground may be considered as a futuristic easy-to-use and low-cost solution to enhance the site-specific monitoring of crop parameters.
Highlights
Crop monitoring supports management techniques to optimize agricultural production according to field parameters [1]
Spatiotemporal data fusion is based on combining data of fine spatial and coarse temporal resolution with fine temporal resolution and coarse spatial resolution to achieve the objective of creating fine spatiotemporal resolution data
Thermal infrared, multispectral, hyperspectral, Light detection and ranging (LiDAR), and radar sensors are widely adopted for crop N, chlorophyll, leaf area index (LAI), and aboveground biomass (AGB) estimation
Summary
Crop monitoring supports management techniques to optimize agricultural production according to field parameters [1]. The latest research shows that fusion of the diverse sensor systems to acquire crop and canopy structure, texture, and spectral and thermal information significantly improves the data related to plant trait determination in a diverse agricultural system [48]. These systems provide efficient data, with their integration at the ground-based platform, such as tractors. Space-borne platforms such as satellites (Sentinel-2/Sentinel-1 10–60 m, MODIS 30 m and Landsat 250 m) provide real-time crop information and agrometeorological data for global food production estimates [54] They provide early warnings for crop monitoring and food supply by assessing crops, farming activities, and rural developments.
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