# Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring
###### Abstract
Crop monitoring in precision farming is segmented into technologies (e.g., guidance system, remote sensing, and variable rate technology (VRT)), solutions (e.g., hardware, software, and services), and applications (e.g., yield monitoring, field mapping, and crop scouting). Crop monitoring can be supplied with multiple sensors estimating environmental features, plant canopy, leaves indices using the internet of things (IoT). Due to the huge set of data collected, this technique is required to develop the types of data to analyze crop growing conditions and development. Moreover, big data fusion has experienced significant progress, and when used for agriculture, it would have a high impact on crop monitoring. Due to which different multi-sensors and IoT-based big data fusion techniques have been used in agriculture for crop monitoring purpose [22]. Monitoring tools using IoT and big data have led to extensive research in the field of digital agriculture. Studies have been proposed on the use of big data methods for agriculture mainly based on one of the several extracted data, such as IoT [23]. Other studies have addressed different crop monitoring problems, particularly, acquisition techniques using multispectral sensors, thermal sensors, hyperspectral sensors and satellite sensors [24]. Precision agriculture challenges require big solutions through advanced technologies, and Agriculture 5.0 is considered to be the one for the initial phase of the 21 st century. Agriculture 5.0 is the implementation of Precision Agriculture systems and equipment utilization that consists of intelligent operations and autonomous decision support systems. Therefore, Agriculture 5.0 is considered as the implementation of robots and artificial intelligence (AI) [25]. Generally, crop fields need high labor for crop harvesting and high productivity. However, society has changed, now, with more people living in urban environments; ultimately, fields are facing the problems of a labor shortage. A potential solution is the implementation of agricultural 5.0 tools such as robots and sensors integrated with AI features. These systems replace the labor workforce and manage the crop growth and harvest with a more reliable pace [26]. An important method for crop monitoring is the use of optical sensors [27]. Optical sensors are positioned either in contact with or near the crop [27; 28]. They use indirect, nondestructive, less laborious, and nutrient-efficient methods for crop monitoring [29]. However, they are sensitive to weather conditions, are time consuming, and provide low temporal resolution. Thermal infrared (TIR) collects the variation in leaf and canopy dynamics for leaf temperature estimation using thermal imagery [30]. It is a noncontact, less laborious, and nondestructive method; however, the system shows inaccuracies in yield estimation, crop water stress, and plant growth determination [31; 32; 33; 34]. A multispectral system integrated with visible bands collects the digital imagery and provides crop monitoring analysis [34]. However, the system only uses qualitative data, and within-field color variations do not allow accurate quantitative estimation of crop parameters. Multispectral sensors are widely adopted for deriving crop vegetation indices (VIs) to predict crop yield [35; 36]. However, the system is not able to provide rapid variations that occur in leaf chlorophyll [37]. Light detection and ranging (LiDAR) construct the crop into 3D images [38], geometrical characteristics, and water and nutrient management [39; 40; 41; 42; 43]. However, LiDAR has not been extensively tested for crop studies, which may lead to problems in canopy estimation [44], higher costs, and complexity in data management in diverse weather conditions [45]. In this aspect, synthetic aperture radar (SAR) efficiently delivers crop geometrical properties using its highly sensitive microwave signals under all weather conditions [46]. However, topsoil moisture estimation is not always accurately acquired [47].