## 1 Introduction
The key technologies that are particularly significant for Agriculture 4.0, according to the guidelines for the operative implementation of Industry 4.0 in the agricultural sector [14], along with their capabilities and effects for the benefit of sustainability, are briefed alphabetically in Table 1.  
Beyond the actual involvement of advanced technologies, the most significant challenge of Agriculture 4.0 toward sustainable growth resides in the competency to deliver dynamically integrated PF systems that (a) enact more sophisticated and effective automated agricultural operations (such as cultivation and irrigation) at lower costs, (b) provide safer and more efficient operating conditions  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Technology** & **Capability** & **Effect** \\ \hline Artificial Intelligence and Machine Learning [16; 17; 18]1 & Provide intelligent software applications and systems that can perform knowledge work & - & Reduce prediction costs on decision making \\
16; 18; 19]1 & Operations involving subtle judgments and unstructured commands & - & Enable efficient risk management \\ \hline Big Data and Analytics [19; 20; 21]1 & Enable the acquisition and comprehensive evaluation of large amounts of heterogeneous data from numerous sources & - & Promote real-time decision support \\
17; 21; 22]1 & Provide hardware, software, and storage computing resources delivered as a service over a network or the Internet & - & Enhance efficient resources administration \\ \hline Cyber-Physical Systems (CPS) [25; 26; 27]1 & Enable the connection of the physical world agricultural operations with computing and Information Communication Technologies (ICT) infrastructures by integrating innovative applications via networking & - & Produce higher quality yield at a lower cost \\ \hline Internet of Things (IoT) [28; 29; 30]1 & Benefits field devices with sensory and computational support for communicating and interacting with each other via a highly distributed public network based on & - & Decentralizes analytics enabling decision support for real-time responses \\
19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30] & Distributed public network based on & - & Optimizes assets management \\
17; 22; 23; 24; 25; 26; 27; 28; 29; 30] & Random protocols & - & Enhances production process performance \\ \hline Autonomous Machinery [31; 32; 33]1 & Perform various field tasks such as sowing, pruning, phenotyping, targeted fertilization, harvesting, and sorting in automated or near-automated mode & - & Ease the workload on farmers \\
17; 22; 23; 24; 25; 26; 27; 28; 29; 30] & Program various field tasks such as sowing, pruning, phenotyping, targeted fertilization, harvesting, and sorting in automated or near-automated mode & - & Increase production rates \\
18; 19; 20; 21; 22; 23; 25; 26; 27; 28; 29; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 6  
both for the environment and stakeholders (involving farmers, agronomist engineers, policy makers, development cooperation professionals, etc. ), and (c) increase the synergies among all stakeholders offering them the ability to make decisions even on issues that have ordinarily been outside their areas of expertise [23; 36; 37]. For this, a generalized concept of whole-farm management, as in [13], based on the potent cross-industry cooperation of stakeholders, infrastructures, technologies and applications is tending to be applied in accordance with the objectives of the IoT trend (Figure 1). Provided that the development of a dynamically integrated PF system is regarded to be significantly based on the ability to acquire, process and exchange data at a higher level (i.e., applications) in order to generate knowledge regarding the optimization of production efficiency, the fact that as in [13] the average farm is predicted to generate several 4.1 million data points per day by 2050, represents an excessive challenge.