## 3 Information and Communication Technology Solutions for Precision Agriculture
### Data Analysis and Artificial Intelligence
#### 3.2.2 Predictive Models and Decision Support  
Decisions regarding farm management can have large environmental economic and environmental footprints. Therefore, predictive and decision-support technologies are particularly useful for PA. Technologies and tools which can support decision-making, include: big data analytics, machine learning (encompassing deep learning, neural networks, and support vector machines (SVMs), among other technologies), predictive models, satellite imagery, sensors measuring various data, granulation techniques, IoT, etc. The term big data describes large-volume, complex, and ever-growing datasets from diverse and sometimes autonomous sources. Several definitions refer to data variety, velocity, and volume (also known as \"3 Vs\") as constituent parts of big data. A similar \"4V\" model complements the \"3V\" model with data veracity as the fourth \"V\", while the \"5V\" model also uses data value as the fifth \"V\" [30]. Machine learning refers to tools and methodologies aiming to enable computational applications to modify and/or adapt their actions so that they become more accurate and closer to the desired result [31]. [PERSON] et al. demonstrate a rainfall prediction model using big data analytics and neural networks. Based on the results of the research, the authors emphasize that the specific model has the potential of improving the prediction accuracy as well as of facilitating the decision-making of farmers about crop pattern and water management [32]. [PERSON] et al. propose a novel predictor for big data in PA cyber-physical systems (CPSs). A CPS can be defined as a system integrating computation with physical processes. Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) are two popular examples of CPSs [33]. Utilizing granulation techniques, genetic algorithms, and SVMs, this solution addresses the problem of reduced efficiency of similar predictors in handling large-scale data. The proposed predictor can facilitate decision-making for management and/or planting issues. Testing of the solution indicated satisfactory computation efficiency and prediction accuracy [34]. [PERSON] et al. demonstrate a novel forecasting method which can be used for predicting agricultural crop yield by integrating agroclimate and remotely-sensed indices. The integration is conducted within a probabilistic Bayesian framework, incorporating data and model structural uncertainty. The particular method utilizes random forest-tree machine learning techniques as well as Markov chain Monte Carlo simulation. The proposed technique offers an accurate, statistically robust, and flexible tool for yield forecast at a regional scale [35]. [PERSON] and [PERSON] propose a framework which can be used for predicting future weather conditions based on big data from weather stations. Neural network techniques were utilized, and a time series-based decomposition technique was also developed. The predicted results can be used for evaluating the forecasting performance of weather stations as well as for preventing yield loss caused by extreme weather events [36]. [PERSON] et al. demonstrate a model for yield and protein prediction which utilizes deep neural network techniques. This model can be used for the optimized application of fertilizers which can increase profits and reduce the environmental impact. The proposed deep learning-based methodology was found to offer increased accuracy compared to other methods used for protein and yield prediction (e.g., multiple regression, shallow feed-forward networks) [37]. Risk management systems are strongly interrelated with decision-support systems and mainly refer to processes which identify existing risks and plan actions to mitigate these risks [38]. [PERSON] et al. propose an IoT-based risk management system for solar greenhouses.