Multipurpose optimization of yield production in agriculture using fuzzy self-reproducing automata theory

Abstract

In determining the irrigation depth of barley, it is inevitable to find the best periods to increase the efficiency of water consumption and also to achieve the highest yield of the product. A multi-objective optimization model has been presented to improve irrigation planning and the allowable amount of irrigation during the growth period using genetic algorithm based on nondominated sorting (NSGAII) and cellular automata. Under this concept, the structure of the optimal water supply allocation model is included in the form of two main objective functions. Therefore, the first objective function is to minimize water allocation and the second objective function is to maximize the total income from the cultivation pattern compared to its costs. The latest data related to the cultivation pattern and economic information such as product sales price and production costs in the planting and harvesting stages were collected for 1 year of study. The daily data of river flow, rainfall and climatic data of Hulunbuir district in Inner Mongolia province were converted into 10-day periods. It shows the optimal irrigation planning results of winter barley in three different scenarios. In ten periods of growth, the rainfall is enough to provide most of the plants’ water needs. HIGHLIGHTS Algorithm of multi-objective self-reproducing automata has been used to develop agricultural water supply model.; Estimated solutions were validated by fuzzy bounding by domination concept.; The results showed that the use of supplementary irrigation in dry areas was a sustainable solution in food security.;

Keywords

• crowding distance
• determination of irrigation depth
• non-domination