Hybrid Ontology-based knowledge with multi-objective optimization model framework for Decision Support System in intercropping

Abstract

The problem of multi-criteria or multi-objective intercropping crop planning makes it vital to consider all related factors under the constraints that will produce the highest revenue and minimum cost. Principles of intercropping elements including soil type, plant area, plant diseases, planting and harvesting time and economics factors (e.g. price, cost) are some of the factors in making decisions. Intercropping is important in the situation such as during inability to harvest main crops, co-cultivation to increase productivity, or even to increase extra revenue. Therefore, the decision-making system requires a wise decision support system, which can advise farmers on economics matters. In this article, we present the decision support system (DSS) model framework for planting rubber with intercropping by a hybrid approach using ontology-based knowledge consuming rule concepts and relationships for intercropping with integrated multi-objectives optimization to recommend the crop to be planted and the suitable proportion of planting areas or planting co-suited to the rubber plantation of farmers. This approach could be applied as a guideline for another field, such as production problems or other resource allocation issues.