Knowledge and integrated data management model for personalized intercropping in rubber plantation

Abstract

Selection and allocation of space for intercropping in rubber plantations to maximize yield and minimum costs for individual farmers involves Multi-Criteria Decision Making (MCDM) and several conditions. The problem is that the information is scattered in many related agencies, there are separate stores and some data is redundant. In addition, the format of the data varies depending on the purpose of the data. The knowledge of selecting plants to grow in the rubber plantation is the tacit knowledge acquired from the experience of successful farmers in rubber plantations and from agricultural experts. Therefore, this research involves an Integrated Ontology-based knowledge and Multi-Objective Optimization model for intercropping Decision Support Systems (DSS). This article presents the knowledge and integrated data management model for developing the Intercropping in Rubber Plantations Ontology by using the Triangulation in the method to verify the accuracy of the data and results. Moreover, propose ways to create recommendation rules that are easy to rule update and maintenance. Using an ontology for DSS helps to recommended plants according to the appropriate environment of the farmer area by rule-based inference to represent logical reasoning. It could also be applied to another domain that requires Intelligent DSS for MCDM.

IoT-oriented software platform applied to sensors-based farming facility with smartphone farmer app

“…Afterwards, the WSN was tested on -field during the performed measurement campaigns, verifying the correctness of acquired data and their transmission between different types of nodes and to the central unit. The composition of fertilizing solutions is set on the base of agronomic models related to cultivated crops [11]. Each model has as inputs the weather information given by weather service and, mostly, the data directly detected on-field.…”

“…Typically, a DSS consists of two main parts: An integrated system for the real-time monitoring of interest parameters related to the cultivated products and the memorization of related data; whereas, the second part consists of an application available on-line which, analyzing these data with advanced modeling techniques, provides real-time alerts and information for supporting the decision-making process as shown in Figure 1. Then common users or farmers use these informations for agronomic precision crop management; data on cultivation operations also enter in the on-cloud databases, in order to generate a continuous flow of updated information between the crop, DSS, and the farmers [10,11]. An example of DSS to be cited is vite.net based on a network of weather stations, a server repository able to store weather data and site-specific information on cultural management (such as the list of treatments applied), a set of mathematical models, that use the real-time data collected in fields as input data to produce useful information, by the developed DSS, about the tactical management of treatments and other cultural practices [12].…”

A Knowledge Management System Conceptual Model for the Sorong COVID-19 Task Force

“…Ontology explains concepts and relations between one domain with another domain [40], [41], [49], [50], [51], [52], [53]. Therefore, it makes the knowledge exploration process related to the Covid-19 handling easier and faster.…”

“…KMS is the right solution and has become the center of managing, saving and discussing both old and new knowledge [1], [33], [41]. The benefits and strengths of KMS are improving services for the citizens, optimizing the effectiveness of work process and procedure, increasing knowledge assets, making information and knowledge search easily, accurately and fast, providing collaborative means for experts, and sharing knowledge and experience possessed by each user.…”

Implementing Knowledge Management System to Improve Effectiveness of Faculty Activities

“…Using an ontology for DSS can help provide recommendations about suitable crops planted around the farmer's area according to rules-based inference that acts as logical reasoning. [10].…”