# A Smart Decision System for Digital Farming
###### Abstract  
New technologies have the potential to transform agriculture and to reduce environmental impact through a green revolution. Internet of Things (IoT)-based application development platforms have the potential to run farm management tools capable of monitoring real-time events when integrated into interactive innovation models for ferritin. Their capabilities must extend to flexible reconfiguration of programmed actions. IoT platforms require complex smart decision-making systems based on data-analysis and data mining of big data sets. In this paper, the advantages are demonstrated of a powerful tool that applies real-time decisions from data such as variable rate irrigation, and selected parameters from field and weather conditions. The field parameters, the index vegetation (estimated using aerial images), and the irrigation events, such as flow level, pressure level, and wind speed, are periodically sampled. Data is processed in a decision-making system based on learning prediction rules in conjunction with the Drools rule engine. The multimedia platform can be remotely controlled, and offers a smart farming open data network with shared restriction levels for information exchange oriented to farmers, the fertilizer provider, and agricultural technicians that should provide the farmer with added value in the form of better decision making or more efficient exploitation operations and management. s _arxiv 12.6 January  
These features typically include intelligent assistance with the implementation of technology and its maintenance and use. The concept of smart farming is summarized in Figure 1, together with the management cycle as a cyber-physical system, leading eventually to smart online devices capable of controlling a range of farming systems. [PERSON] [2] reported rapid developments in the IoT and cloud computing, applied in this paper to a field known as smart farming. Algorithmic training processes for decision-making support are highly complex tasks, with many problematic issues found in models that are automatically induced from data through big data applications [3]. Perhaps the most problematic is the value chain that refers to the sequence of activities from data capture to decision making, involving data marketing, decision trees, and rule injection procedures. The advantage of these models is that they consist of individual units of knowledge (rules) that can be separately viewed, evaluated, processed, deleted, and even edited. In a business process [4], for example, work laid out in a set of logically related tasks can be performed to achieve a defined business outcome. Big data represents the information assets that are characterized by high volumes, high-speed processing and a variety of inputs. Processing such information requires specific technology and analytical methods [5] for their transformation into a value chain. This paper presents a complete implementation of a smart decision support system prototype, which automatically learns decision rules from data, thereby permitting the domain expert to edit the resulting rule base, which can apply the rules to achieve incoming objects. As agriculture consumes about 70% of all fresh water resources, one important aspect of green farming is economical and efficient use of water [6]. Water consumption percentages can be decreased through efficient water management, where irrigation needs are adjusted to supply. Decision trees generate rules to enhance the use of irrigation water, fertilizers, pesticides, etc. A web platform can convert the results of data-based decision making into user-friendly public information that is only posted following a decision maker review process. The major advantage is integration into a unique platform to control farming decisions such as irrigation system control, fertilization, pesticides, and an open data network, all in one. In addition, our system includes rules based on variable-rate irrigation (VRI) [7].