Water Demand Pattern and Irrigation Decision-Making Support Model for Drip-Irrigated Tomato Crop in a Solar Greenhouse

Abstract

The knowledge of crop water requirements is critical for agricultural water conservation, especially for accurate irrigation decision making in the greenhouse. Investigating the water demand pattern of the tomato in the solar greenhouse environment and constructing an appropriate irrigation decision-making model are urgently needed to improve irrigation water use efficiency. We designed four irrigation-level treatments: 100% ET0 (T1), 85% ET0 (T2), 70% ET0 (T3), and 55% ET0 (T4), and conducted a two-vegetation-season tomato planting trial under drip irrigation conditions in a solar greenhouse. The Pearson’s correlation coefficient method analyzed the intrinsic linkage and influence between soil–crop–environment and tomatoes’ water demand patterns. Indicators suitable for irrigation decision making in greenhouse tomatoes were selected, and regression functions were constructed for environmental and crop physiological parameters by combining path analysis and multiple regression methods. Finally, a fusion irrigation decision-making model was constructed by introducing a distance function in the Dempster–Shafer (D–S) theory primary probability assignment (BPA) synthesis algorithm and combining it with a triangular affiliation function. The results showed that: (1) the soil coefficient of variation was shallow > middle > deep, and tomatoes absorbed water mainly in the 0–60 cm soil layer; (2) the crop stem flow rate, net photosynthetic rate, and transpiration rate were positively correlated with irrigation water and had the highest correlation with net radiation, relative humidity, and relative humidity, with correlation coefficients of 0.9441, 0.9441, and 0.7679, respectively; (3) the constructed decision model had a significantly lower value of uncertainty than other methods, while the highest decision value could reach over 0.99, which achieved the best decision accuracy compared to other algorithms.

A Systematic Literature Review: Internet of Things on Smart Greenhouse

“…The use of capacitive soil moisture sensors in measuring soil moisture is more resistant to rust than resistive soil moisture [51]. While using a model based on long short term memory, the error value is 0.72% [52] and if using a synthesis algorithm and combining it with a triangular affiliation function 0.99% [53]. By controlling soil moisture in the planting medium in the greenhouse automatically the estimation results of the algorithm used can save both, water irrigation and plant nutrients [54].…”