Greenhousing is a technique to bridge season gap in vegetable production and has been widely used worldwide. Calculation of water requirement of crops grown in greenhouse and determination of their irrigation schedules in arid and semi-arid regions are essential for greenhouse maintenance and have thus attracted increased attention over the past decades. The most common method used in the literature to estimate crop evapotranspiration (ET) is the Penman-Monteith (PM) formula. When applied to greenhouse, however, it often uses canopy resistance instead of surface resistance. It is understood that the surface resistance in greenhouse is the result of a combined effect of canopy restriction and soil-surface restriction to water vapor flow, and the relative dominance of one restriction over another depends on crop canopy. In this paper, we developed a surface resistance model in a way similar to two parallel resistances in an electrical circuit to account for both restrictions. Also, considering that wind speed in greenhouse is normally rather small, we compared three methods available in the literature to calculate the aerodynamic resistance, which are the r a1 method proposed by Perrier (1975a, b), the r a2 method proposed by Thom and Oliver (1977), and the r a3 method proposed by Zhang and Lemeu (1992). We validated the model against ET of tomatoes in a greenhouse measured from sap flow system combined with micro-lysimeter in 2015 and with weighing lysimeter in 2016. The results showed that the proposed surface resistance model improved the accuracy of the PM model, especially when the leaf area index was low and the greenhouse was being irrigated. We also found that the aerodynamic resistance calculated from the r a1 and r a3 methods is applicable to the greenhouse although the latter is slightly more accurate than the former. The proposed surface resistance model, together with the r a3 method for aerodynamic resistance, offers an improved approach to estimate ET in greenhouse using the PM formula.

The traditional Pignistic transformation is limited in the context of ''betting'', which faces information loss and is inconvenient for multi-source information fusion. To tackle this challenge, an Enhanced Pignistic transformation is proposed for the first time. New divergence and information volume measures are tailor-made for the enhanced Pignistic probability, and a novel information fusion algorithm is developed. To further prove the fusion algorithm's advantages in conflict management, it is applied in a new semi-automatic image segmentation scheme. Two uncertain decision-support techniques named adaptive belief assignment and scalable information extraction are raised, and a fuzzy heuristic refinement algorithm is conducted, fulfilling the gap between evidential decision-making and segmentation refinement. Experimental analysis shows the proposed segmentation algorithm is superior on four metrics each and can enhance the robustness of foreground segmentation, indicating the effectiveness of the proposal in solving the decision inaccuracy of evidential segmentation schemes.

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