Estimation of Solar Radiation for Tomato Water Requirement Calculation in Chinese-Style Solar Greenhouses Based on Least Mean Squares Filter

• Main Authors: Dapeng Zhang, Tieyan Zhang, Jianwei Ji, Zhouping Sun, Yonggang Wang, Yitong Sun, Qingji Li
• Format: Article
• Language: English
• Published: MDPI AG 2019-12-01
• Series: Sensors
• Subjects: csg; tomato water requirement calculation; lms filter; solar radiation estimation
• Online Access: https://www.mdpi.com/1424-8220/20/1/155

The area covered by Chinese-style solar greenhouses (CSGs) has been increasing rapidly. However, only a few pyranometers, which are fundamental for solar radiation sensing, have been installed inside CSGs. The lack of solar radiation sensing will bring negative effects in greenhouse cultivation such as over irrigation or under irrigation, and unnecessary power consumption. We aim to provide accurate and low-cost solar radiation estimation methods that are urgently needed. In this paper, a method of estimation of solar radiation inside CSGs based on a least mean squares (LMS) filter is proposed. The water required for tomato growth was also calculated based on the estimated solar radiation. Then, we compared the accuracy of this method to methods based on knowledge of astronomy and geometry for both solar radiation estimation and tomato water requirement. The results showed that the fitting function of estimation data based on the LMS filter and data collected from sensors inside the greenhouse was y = 0.7634x + 50.58, with the evaluation parameters of R² = 0.8384, rRMSE = 23.1%, RMSE = 37.6 Wm^−2, and MAE = 25.4 Wm^−2. The fitting function of the water requirement calculated according to the proposed method and data collected from sensors inside the greenhouse was y = 0.8550x + 99.10 with the evaluation parameters of R² = 0.9123, rRMSE = 8.8%, RMSE = 40.4 mL plant^−1, and MAE = 31.5 mL plant^−1. The results also indicate that this method is more effective. Additionally, its accuracy decreases as cloud cover increases. The performance is due to the LMS filter’s low pass characteristic that smooth the fluctuations. Furthermore, the LMS filter can be easily implemented on low cost processors. Therefore, the adoption of the proposed method is useful to improve the solar radiation sensing in CSGs with more accuracy and less expense.