The development of variable system-based internet of things for the solar greenhouse and its application in lettuce.

The east-west ridge orientation has recently become an important agronomic method to improve mechanization in solar greenhouses. However, these ridge orientation changes shape differences between different ridges in crop water consumption, and there is a lack of research on the regulation and adaptation of water consumption. Therefore, this study introduces a variable irrigation decision-making method based on the Internet of Things management and control system for an east-west ridge orientation. To replenish water on demand, this study seting the variable irrigation decision-making (VRI) methods and traditional average irrigation decision-making (URI) methods in the system, and lettuce cultivation experiments were conducted to verify the effectiveness of the model and system. The results show that the difference of accumulated photosynthetically active radiation is the most significant between different ridges of the east-west ridge orientation, and the coefficient of variation is 43.77 %, which can be used as an activating factor for VRI methods. The irrigation water consumption, yield, water-use efficiencies, and irrigation water utilization of lettuce at different levels of irrigation were 307.12 L/m2, 5854.07 kg·ha-1, 1391.47 kg·ha-1·mm-1, and 7.63 kg·cm-3, respectively. Compared with the URI methods, the VRI method saved 10.02 % of water, increased yield by 9 %, and enhanced water use efficiency and irrigation water use efficiency by 12 % and 21.32 %, respectively. This study provides a new approach for improving crop production efficiency under an east-west ridge orientation.

Effect of nanobubble concentrations on fouling control capacity in biogas slurry wastewater distribution systems.

Nanobubble (NB) represents a promising practice for mitigating fouling in biogas slurry distribution systems. However, its anti-fouling effectiveness and optimal use dosage are unknown. This study investigated the NB anti-fouling capacity at six concentrations (0 %-100 %, denoting the ratio of maximum NB-infused water; particle concentrations in 0 % and 100 % ratios were 1.08 × 107 and 1.19 × 109 particles mL-1, respectively). Results showed that NB effectively mitigated multiple fouling at 50 %-100 % ratios, whereas low NB concentration exacerbated fouling. NB functioned both as an activator and a bactericide for microorganisms, significantly promoting biofouling at 5 %-25 %, and inhibiting biofouling at 50 %-100 %. Owing to an enhanced biofilm biomineralization ability, low NB concentration aggravated precipitate fouling, whereas high NB doses effectively mitigated precipitates. Additionally, higher NB concentrations demonstrated superior control efficiency against particulate fouling. This study contributes insights into NB effectiveness in controlling various fouling types within wastewater distribution systems.

Deep Neural Networks for the Classification of Pure and Impure Strawberry Purees.

In this paper, a comparative study of the effectiveness of deep neural networks (DNNs) in the classification of pure and impure purees is conducted. Three different types of deep neural networks (DNNs)-the Gated Recurrent Unit (GRU), the Long Short Term Memory (LSTM), and the temporal convolutional network (TCN)-are employed for the detection of adulteration of strawberry purees. The Strawberry dataset, a time series spectroscopy dataset from the UCR time series classification repository, is utilized to evaluate the performance of different DNNs. Experimental results demonstrate that the TCN is able to obtain a higher classification accuracy than the GRU and LSTM. Moreover, the TCN achieves a new state-of-the-art classification accuracy on the Strawberry dataset. These results indicates the great potential of using the TCN for the detection of adulteration of fruit purees in the future.

Data-Driven Calibration of Soil Moisture Sensor Considering Impacts of Temperature: A Case Study on FDR Sensors.

Commercial soil moisture sensors have been widely applied into the measurement of soil moisture content. However, the accuracy of such sensors varies due to the employed techniques and working conditions. In this study, the temperature impact on the soil moisture sensor reading was firstly analyzed. Next, a pioneer study on the data-driven calibration of soil moisture sensor was investigated considering the impacts of temperature. Different data-driven models including the multivariate adaptive regression splines and the Gaussian process regression were applied into the development of the calibration method. To verify the efficacy of the proposed methods, tests on four commercial soil moisture sensors were conducted; these sensors belong to the frequency domain reflection (FDR) type. The numerical results demonstrate that the proposed methods can greatly improve the measurement accuracy for the investigated sensors.

Research on soil moisture prediction model based on deep learning.

Soil moisture is one of the main factors in agricultural production and hydrological cycles, and its precise prediction is important for the rational use and management of water resources. However, soil moisture involves complex structural characteristics and meteorological factors, and it is difficult to establish an ideal mathematical model for soil moisture prediction. Existing prediction models have problems such as prediction accuracy, generalization, and multi-feature processing capability, and prediction performance must improve. Based on this, taking the Beijing area as the research object, the deep learning regression network (DNNR) with big data fitting capability was proposed to construct a soil moisture prediction model. By integrating the dataset, analyzing the time series of the predictive variables, and clarifying the relationship between features and predictive variables through the Taylor diagram, selected meteorological parameters can provide effective weights for moisture prediction. Test results prove that the deep learning model is feasible and effective for soil moisture prediction. Its' good data fitting and generalization capability can enrich the input characteristics while ensuring high accuracy in predicting the trends and values of soil moisture data and provides an effective theoretical basis for water-saving irrigation and drought control.