A NEW DECISION SUPPORT MODEL BASED ON IMPACT FACTOR ANALYSIS FOR OPTIMIZED SCHEDULING OF TOMATO HARVESTING
In fructescence, abnormal weather has great effects on tomatoes' economic benefits. For example, heavy rain will bring great losses. Generally speaking, picking fruits in advance can reduce the losses. However, the market value of fruit is closely related to the maturity. This indicates that a dedicated system supporting decisions that affect tomato quality is required. In this work, a novel model designed for the scheduling of the tomato harvest is proposed. What is more, a feasibility study is carried out to evaluate the functionality and viability of a potential integrated decision support system. Besides, we establish factors' hierarchical relationships to analyse the inner relationship between the factors and the effect to the economic benefit. Meanwhile, we study tomatoes' ripeness algorithm and the corresponding performance evaluation methods to provide harvest scheduling for farmers, especially those making harvest plans in advance to avoid abnormal weather. Preliminary results show that the use of a dedicated decision support model for the tomato harvest has the potential of significantly reducing the costs and improving the quality of the harvest. In addition, simulation test platform proves the effectiveness of the algorithm and historical data is used to verify the proposed model's accuracy and feasibility. To a certain extent, our model can improve tomatoes' economic benefit.
fuzzy color histogram dynamic bayesian network optimal harvest time