## 4 Discussion on the Results--Conclusions  
The present literature review surveyed a wide variety of ICT solutions for PA supporting one or multiple data lifecycle stages. These solutions were found to underpin PA applications that in turn contribute to substantial environmental and economic benefits (e.g., higher quality crops with higher yield, prevention of pesticides and fertilizers overuse, optimized used of water and other resources, prevention of soil degradation). Among the different data lifecycle stages in Table 1, data analysis and AI stage encompassed the largest number of different technological categories, i.e., 22 subcategories. Being a focus point of 21 different solutions, computer vision was the most popular technological subcategory of data analysis and AI lifecycle stages. Computer vision tools and technologies were mainly used to facilitate classification and detection of crops, to extract useful knowledge for the condition and quality of the crops, as well as to enable effective CPS-based operations. Technologies revolving around decision-support and predictive models were also very popular among the data analysis and the AI solutions. These technologies offer valuable insights into actions farmers should take to increase productivity and efficiency as well as to prevent losses or destruction of crops due to diseases or natural phenomena. Despite the fact that data collection and IoT stages of the data lifecycle model compassed only four technological subcategories (WSN was the most popular subcategory), it was the focus point of several solutions. Sensors of various kinds (mostly temperature, moisture, CO\({}_{2}\), and light sensors) were of vital importance for monitoring crops condition and served as inputs to subsequent data analytics/integration/storage/sharing operations. Multispectral satellite imagery was used in many computer vision techniques, and satellite navigation was extensively used for the navigation of the majority of CPSs. Regarding the papers focusing on storage and distribution lifecycle stages, sharing platforms and cloud storage platforms were very popular, facilitating secure storage and distribution of agricultural  
\begin{table}
\begin{tabular}{c c c} \hline
**General Categorization** & **Subcategorization** & **Solution(s)** \\ \hline \multirow{4}{*}{Data Collection and IoT} & IoT & [21; 22; 23; 39; 62; 69; 80; 97] \\  & Satellite Imagery and GIS & [19; 28; 40; 44; 46; 57; 58] \\  & Satellite Navigation & [18; 39; 40; 99; 59; 98; 100; 101; 102; 103; 104; 105; 106; 107; 108; 109; 110; 111; 112; 113; 114; 115; 1  
data as well as of valuable experiences and knowledge. Blockchain and smart contracts were found to have a great potential in PA, providing immutable and secure data storage and sharing. Among solutions supporting multiple data lifecycle stages, CPSs were by far the most frequently occurring in the examined bibliography. The use of CPSs in the context of PA can radically change the form of modern farming activities. The reviewed PA solutions enabled several tasks (e.g., seeding, transplanting, weeding, tilling) to be conducted autonomously with minor or no human interaction. Other solutions revolved around dangerous (e.g., due to exposing the farmer to harmful chemicals) and time-consuming activities, which were carried out by CPSs, thus decreasing labor costs and minimizing the risks of health problems. The proposed categorization method can be used for future research on PA from a single (or multiple) data lifecycle stage(s) perspective(s). Future research may also perform a cost-benefit analysis regarding the use of certain PA-related collection/analysis/storage/distribution technologies described in this review. The developed data lifecycle model can be used in its current form for research works around PA or other technologies encompassing multiple data lifecycle stages. It may also be adapted to meet the specific needs of business or academic members. In conclusion, ICT solutions act as pillars and facilitators for data management in PA.