# Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring
###### 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. Agriculture 5.0; artificial intelligence; biomass; chlorophyll; IoT; leaf area index; nitrogen; robot; sensors +
Footnote †: journal: _agronomy_  
a need for a developed site-specific crop monitoring solution capable of addressing such serious agronomic, environmental, and economic issues. These issues can be addressed by the proper estimation of crop status, which depends on different factors, such as climate, soil, crop nutrients, growing cycles, pests, and diseases. Crop status and plant health indirectly influence productivity, which is the focus of the current study. Nitrogen (N), chlorophyll, leaf area index (LAI), and aboveground biomass (AGB) are some of the most important factors for monitoring crop status and health. The management of N to meet production aims is difficult, as the crop response to N is highly variable due to changes in soil properties [8]. The potential to simultaneously estimate N dynamics and environmental damage could be a significant step in increasing crop production [9; 10; 11; 12; 13]. Chlorophyll pigment is a major parameter for quantifying the photosynthesis rate and productivity [14]. The retrieval of chlorophyll information relies on the relationship between spectral reflectance and biochemical contents [15]. While its pigment manages the spectral reflectance of the leaves and canopy, other parameters, e.g., canopy architecture, encompass both the spatial distributions of vegetation components, LAI, and background reflectance, which also contribute to it [14; 16; 17]. The LAI is an important index that provides crop growth condition by regulating water and predicting yield [18]. Therefore, accurate estimation of LAI can be used to precisely forecast crop-related factors, such as crop coefficient (kc) and crop yield factor (ky) [19]. The AGB content is strongly correlated with crop yield. Accurate and timely monitoring of AGB is important for developing site-specific management strategies [20]. Due to this direct correlation, AGB is of high importance to crop production during the crop growth stage [21].