Long short-term memory for a model-free estimation of macronutrient ion concentrations of root-zone in closed-loop soilless cultures

Background Root-zone environment is considered difficult to analyze, particularly in interpreting interactions between environment and plant. Closed-loop soilless cultures have been introduced to prevent environmental pollution, but difficulties in managing nutrients can cause nutrient imbalances with an adverse effect on crop growth. Recently, deep learning has been used to draw meaningful results from nonlinear data and long short-term memory (LSTM) is showing state-of-the-art results in analyzing time-series data. Therefore the macronutrient ion concentrations affected by accumulated environment conditions can be analyzed using LSTM. Results The trained LSTM can estimate macronutrient ion concentrations in closed-loop soilless cultures using environmental and growth data. The average training accuracy of six macronutrients was R2 = 0.84 and the test accuracy was R2 = 0.67 with RMSE = 1.48 meq L−1. The used values of input interval and time step were 1 h and 168 (1 week), respectively. The accuracy was improved when the input interval became shorter, but not improved when the LSTM consisted of a multilayer structure. Regarding training methods, the LSTM improved the accuracy better than the non-LSTM. The trained LSTM showed relatively adequate accuracies and the interpolated ion concentrations showed variations similar to those seen during traditional cultivation. Conclusions We could analyze the nutrient balance in the closed-loop soilless culture, the model showed potential in estimating the macronutrient ion concentrations using environmental and growth factors measured in greenhouses. Since the LSTM is a powerful and flexible tool used to interpret accumulative changes, it is easily applicable to various plant and cultivation conditions. In the future, this approach can be used to analyze interactions between plant physiology and root-zone environment.This work was supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (IPET) through the Agriculture, Food and Rural Afairs Research Center Support Program funded by the Ministry of Agriculture, Food and Rural Afairs (MAFRA; 717001-07-1-HD240)

Forecasting Root-Zone Electrical Conductivity of Nutrient Solutions in Closed-Loop Soilless Cultures via a Recurrent Neural Network Using Environmental and Cultivation Information

In existing closed-loop soilless cultures, nutrient solutions are controlled by the electrical conductivity (EC) of the solution. However, the EC of nutrient solutions is affected by both growth environments and crop growth, so it is hard to predict the EC of nutrient solution. The objective of this study was to predict the EC of root-zone nutrient solutions in closed-loop soilless cultures using recurrent neural network (RNN). In a test greenhouse with sweet peppers (Capsicum annuum L.), data were measured every 10 s from October 15 to December 31, 2014. Mean values for every hour were analyzed. Validation accuracy (R2) of a single-layer long short-term memory (LSTM) was 0.92 and root-mean-square error (RMSE) was 0.07, which were the best results among the different RNNs. The trained LSTM predicted the substrate EC accurately at all ranges. Test accuracy (R2) was 0.72 and RMSE was 0.08, which were lower than values for the validation. Deep learning algorithms were more accurate when more data were added for training. The addition of other environmental factors or plant growth data would improve model robustness. A trained LSTM can control the nutrient solutions in closed-loop soilless cultures based on predicted future EC. Therefore, the algorithm can make a planned management of nutrient solutions possible, reducing resource waste

Observation of Kondo condensation in a degenerately doped silicon metal

When a magnetic moment is embedded in a metal, it captures itinerant electrons to form the Kondo cloud1,2, which can spread out over a few micrometres3,4. For a metal with dense magnetic impurities such that Kondo clouds overlap with each other, correlated ground states are formed. When the impurities form a regular lattice, the result is a heavy fermion or anti-ferromagnetic order depending on the dominant interaction5,6. Even in the case of random impurities, overlapping Kondo clouds are expected to form a coherent ground state. Here, we examine this issue by performing electrical transport and high-precision tunnelling density-of-states (DOS) spectroscopy measurements in a highly P-doped crystalline silicon metal where disorder-induced localized magnetic moments exist7. We detect the Kondo effect in the resistivity of the Si metal below 2 K and an exotic pseudogap in the DOS with gap edge peaks at a Fermi energy below 100 mK. The DOS gap and peaks are tuned by applying an external magnetic field and transformed into a metallic Altshuler-Aronov gap8 in the paramagnetic disordered Fermi liquid (DFL) phase. We interpret this phenomenon as the Kondo condensation, the formation of a correlated ground state of overlapping Kondo clouds, and its transition to a DFL. The boundary between the Kondo condensation and DFL phases is identified by analysing distinct DOS spectra in the magnetic field-temperature plane. A detailed theoretical analysis using a holographic method 9 , 10 , 11 reproduces the unusual DOS spectra, 1, supporting our scenario. Our work demonstrates the observation of the magnetic version of Bardeen-Cooper-Shrieffer (BCS) pair condensation and will be useful for understanding complex Kondo systems.Comment: 34 pages,5+6 figures, accepted in nature physic

Association of Polymorphisms in Monocyte Chemoattractant Protein-1 Promoter with Diabetic Kidney Failure in Korean Patients with Type 2 Diabetes Mellitus

Monocyte chemoattractant protein-1 (MCP-1) is suggested to be involved in the progression of diabetic nephropathy. We investigated the association of the -2518 A/G polymorphism in the MCP-1 gene with progressive kidney failure in Korean patients with type 2 diabetes mellitus (DM). We investigated -2518 A/G polymorphism of the MCP-1 gene in type 2 DM patients with progressive kidney failure (n=112) compared with matched type 2 DM patients without nephropathy (diabetic control, n=112) and healthy controls (n=230). The overall genotypic distribution of -2518 A/G in the MCP-1 gene was not different in patients with type 2 DM compared to healthy controls. Although the genotype was not significantly different between the patients with kidney failure and the diabetic control (p=0.07), the A allele was more frequent in patients with kidney failure than in DM controls (42.0 vs. 32.1%, p=0.03). The carriage of A allele was significantly associated with kidney failure (68.8 vs. 54.5%, OR 1.84, 95% CI 1.07-3.18). In logistic regression analysis, carriage of A allele retained a significant association with diabetic kidney failure. Our result shows that the -2518 A allele of the MCP-1 gene is associated with kidney failure in Korean patients with type 2 DM

Observation of Kondo condensation in a degenerately doped silicon metal

When a magnetic moment is embedded in a metal, it captures nearby itinerant electrons to form a so-called Kondo cloud. When magnetic impurities are sufficiently dense that their individual clouds overlap with each other they are expected to form a correlated electronic ground state. This is known as Kondo condensation and can be considered a magnetic version of Bardeen–Cooper–Schrieffer pair formation. Here, we examine this phenomenon by performing electrical transport and high-precision tunnelling density-of-states spectroscopy measurements in a highly P-doped crystalline silicon metal in which disorder-induced localized magnetic moments exist. We detect the Kondo effect in the resistivity of the Si metal at temperatures below 2 K and an unusual pseudogap in the density of states with gap edge peaks below 100 mK. The pseudogap and peaks are tuned by applying an external magnetic field and transformed into a metallic Altshuler–Aronov gap associated with a paramagnetic disordered Fermi liquid phase. We interpret these observations as evidence of Kondo condensation followed by a transition to a disordered Fermi liquid

Forecasting root-zone electrical conductivity of nutrient solutions in closed-loop soilless cultures via a recurrent neural network using environmental and cultivation information

In existing closed-loop soilless cultures, nutrient solutions are controlled by the electrical conductivity (EC) of the solution. However, the EC of nutrient solutions is affected by both growth environments and crop growth, so it is hard to predict the EC of nutrient solution. The objective of this study was to predict the EC of root-zone nutrient solutions in closed-loop soilless cultures using recurrent neural network (RNN). In a test greenhouse with sweet peppers (Capsicum annuum L.), data were measured every 10 s from October 15 to December 31, 2014. Mean values for every hour were analyzed. Validation accuracy (R-2) of a single-layer long short-term memory (LSTM) was 0.92 and root-mean-square error (RMSE) was 0.07, which were the best results among the different RNNs. The trained LSTM predicted the substrate EC accurately at all ranges. Test accuracy (R-2) was 0.72 and RMSE was 0.08, which were lower than values for the validation. Deep learning algorithms were more accurate when more data were added for training. The addition of other environmental factors or plant growth data would improve model robustness. A trained LSTM can control the nutrient solutions in closedloop soilless cultures based on predicted future EC. Therefore, the algorithm can make a planned management of nutrient solutions possible, reducing resource waste.OAIID:RECH_ACHV_DSTSH_NO:T201815032RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A002344CITE_RATE:3.678DEPT_NM:식물생산과학부EMAIL:[email protected]_YN:YY

Development of Growth Estimation Algorithms for Hydroponic Bell Peppers Using Recurrent Neural Networks

As smart farms are applied to agricultural fields, the use of big data is becoming important. In order to efficiently manage smart farms, relationships between crop growth and environmental conditions are required to be analyzed. From this perspective, various artificial intelligence algorithms can be used as useful tools to quantify this relationship. The objective of this study was to develop and validate an algorithm that can interpret the crop growth rate response to environmental factors based on a recurrent neural network (RNN), and to evaluate the algorithm accuracy compared to the process-based model (PBM). The algorithms were trained with data from three growth periods. The developed methods were used to measure the crop growth rate. The algorithm consisted of eight environmental variables days after transplanting and two crop growth characteristics as input variables producing weekly crop growth rates as output. The RNN-based crop growth rate estimation algorithm was validated using data collected from a commercial greenhouse. The CropGro-bell pepper model was applied to compare and evaluate the accuracy of the developed algorithm. The training accuracies varied from 0.75 to 0.81 in all growth periods. From the validation result, it was confirmed that the accuracy was reliable in the commercial greenhouse. The accuracy of the developed algorithm was higher than that of the PBM. The developed algorithm can contribute to crop growth estimation with a limited number of data

Accurate Imputation of Greenhouse Environment Data for Data Integrity Utilizing Two-Dimensional Convolutional Neural Networks

Greenhouses require accurate and reliable data to interpret the microclimate and maximize resource use efficiency. However, greenhouse conditions are harsh for electrical sensors collecting environmental data. Convolutional neural networks (ConvNets) enable complex interpretation by multiplying the input data. The objective of this study was to impute missing tabular data collected from several greenhouses using a ConvNet architecture called U-Net. Various data-loss conditions with errors in individual sensors and in all sensors were assumed. The U-Net with a screen size of 50 exhibited the highest coefficient of determination values and the lowest root-mean-square errors for all environmental factors used in this study. U-Net50 correctly learned the changing patterns of the greenhouse environment from the training dataset. Therefore, the U-Net architecture can be used for the imputation of tabular data in greenhouses if the model is correctly trained. Growers can secure data integrity with imputed data, which could increase crop productivity and quality in greenhouses