# Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring
## 4 Data Fusion by Platforms
### Ground-Based Platforms  
Tractors are an important part of automated crop monitoring and can accurately operate for longer durations than a manual resource. Using a tractor, the operator is able to analyze both the vehicle and the harvester. The fusion of machine vision with the tractor could potentially improve its guidance capacity, as shown in Table 2. Its long range provides support for rapid decision making before a difficult turn is to be made. This would give the tractor the ability of rapid observation in detecting variations along its way [141]. However, the challenge in detecting the centimeter-level variation at a close-range path boundary or an obstacle is still present. The fusion of LiDAR is useful in addressing minute variations. However, noisy data are generated if there are slight differences at the path boundary. Therefore, the fusion of an autonomous vehicle guidance system integrated with machine vision is a good option for improving the quality of collected data.  
An important aspect of automatic guidance is the ability to recognize the path between crop rows. The fusion of machine vision with the autonomous vehicle and its GPS is a good option for improving the guidance system. As crop rows are developed at field conditions, a significant step toward this objective is the development of a row recognition system. However, the weak strength of the GPS signal may lose the track, particularly at high amounts of plants/trees. To address this problem, the fused data given by an inertial measurement unit (IMU) are sent to strengthen the GPS signals in a short period (Table 1). The few drawbacks, such as limited estimation potential, charging issues, and weather restrictions, still need to be addressed. The fusion of stationary platforms with the autonomous vehicle could provide high precision, resolution, and calibration [142]; however, in general, the system has high cost and only allows operations over a limited area. Therefore, movable systems are considered to be the most efficient solution but are not compatible for all crops and environments.  
A study conducted by [PERSON] et al. [143] reported the fusion of a robot vehicle with other technologies for the improvement of crop monitoring. A ranging device, an inertial measurement unit (IMU) device, and a color camera were used in fusion mode to better understand the natural positions of the objects in real-time field conditions. Soil variations impacted autonomous navigation in the field, while the IMU data supported the precise positioning of the robot. A fusion method is applied between the corrected points and a color camera in order to recognize every single point [143].  
The fusion method was performed between a ranging device and a color camera in different tests to estimate tree trunks and to guide the autonomous vehicle in the field [144]. The method improved the detection of the source target with the combination of a diverse and colorful illustration, delivered in the form of images.  
The fusion of a 3D laser with a robot on its arms provides better flexibility in crop monitoring. However, data from behind the leaves could not be collected (Table 1) [145], while its fusion with the robotic manipulator is able to position the camera at the best viewpoint for the collection of data from behind the leaves [146]. However, this support is further challenged by the dependence of its performance on the software application. The utilization of a relevant software application could address this challenge in a timely manner.