## 3 Information and Communication Technology Solutions for Precision Agriculture
### Data Analysis and Artificial Intelligence
#### Other Data Analysis Tools  
Several other data analysis tools based on fuzzy logic, semantic technologies, decomposition/denoising algorithms, etc. have useful applications in PA. Fuzzy logic can be defined as a many-valued logic with special properties, which is used to model the vagueness phenomenon and the meaning of natural language utilizing a graded approach [60]. The main idea behind semantic technologies is to aid computers in understanding data by providing tools and methodologies for representation, integration, and acquisition of knowledge [61]. [PERSON] et al. propose a semantic framework for IoT-based PA applications, which provides real-time reasoning over heterogeneous sensor data streams. In addition to this, the proposed framework achieves semantic integration of information from different sources, including social media, sensor data, government regulations/alerts, connected farms, etc. Some benefits of the aforementioned solution are increased productivity, better product quality, resource use optimization, faster reactions to unpredictable events, transparency, etc. [62] [PERSON] et al. propose an inexpensive semantic monitoring solution for vineyards. This solution takes input data from low-cost cameras, sensors, and processing boards mounted on robotic systems. This data is then semantically labeled in order to pinpoint which locations need to be monitored. Thus, farmers can save time and labor. The proposed solution can also be integrated into existing UGVs [63].  
Non-linearity and low control accuracy are two shortcomings of several hydraulic transplanting robot systems, caused by interference from external factors. To overcome these problems, [PERSON] et al. simulated a hydraulic transplanting robot control system by combining fuzzy control theory with proportional-integral-derivative (PID) control theory. The simulated control system helped overcome problems related to low accuracy and non-linearity and improved the overall stability and the performance of the robotic system [64]. [PERSON] et al. present a novel method for the optimization of a variable-rate fertilizer. This method is based on several techniques including: general regression neural networks (GRNN), multi-objective evolutionary algorithms, and decomposition algorithms. The proposed method was found to contribute to a more accurate and uniform fertilization application as compared to other existing methods [65]. [PERSON] et al. propose a model for retrieving crop phenology data which utilizes synthetic aperture radar polarimetric decomposition techniques as well as neural networks, random forest, regression, and k-nearest neighborhood algorithms. This model was used in different crop types including canola, corn, soybean and wheat and was found to provide robust phenology retrieval results [66].  
Data analysis methods such as harmonic analysis of time series (HANTS) algorithms can be used for reconstructing NDVI-related data. In satellite-derived time series regarding the NDVI, adverse atmospheric components frequently result in data gaps. [PERSON] and [PERSON] propose a moving offset method (MOM), which can be used for prefilling time series prior to applying HANTS algorithms. The proposed method was found to improve the quality of NDVI reconstruction in regions where frequent seasonal obstructions are frequent [67]. Another data reconstruction solution particularly useful for PA is proposed by [PERSON] et al. [68]. In this paper, a 4D reconstruction approach is demonstrated, which contributes to crop monitoring at high spatio-temporal resolution. A spatio-temporal model of dynamic scenes is utilized in order to monitor growing crops, the appearance of which is constantly changing. A robust data association algorithm is also developed to tackle problems related to big changes to the appearance of crops, caused by scenes from different angles, at different times. The proposed model was found to offer qualitatively correct and quantitatively accurate monitoring of crops.