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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 R 2 = 0.84 and the test accuracy was R 2 = 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.

Two experiments were conducted to evaluate the efficacy of dietary FERMKIT, a commercial toxin binder consisting of probiotic-fermented natural product containing chitin, chitosan and chitosan oligosaccharides (FERMKITO  , EASY-BIO SYSTEM, Inc., Korea), in binding aflatoxin (AF) and zearalenone (ZEN) and ameliorating their mycotoxicity in meat type ducks. FERMKIT was supplemented to AF contaminated diets (at 120 ppb) at either 0.3 or 0.6% in experiment 1 and to ZEN contaminated diets (at 150 ppb) at 0.6% in experiment 2. In experiment 1 body weight gains were reduced by 37% and mortality was increased by 18% in ducks fed diet contaminated with AF at 120 ppb compared to ducks fed control diet (<10 ppb AF) for the 4-wk experimental period. However, dietary FERMKIT supplementation effectively alleviated overall toxicity induced by AF. The significant treatment-related changes in feather growth, web-toe hemorrhage, leg deformity, liver paleness, organ weights, hematological values and serum biochemical values, as compared to the control, were observed. The FERMKIT supplementation significantly diminished the adverse effects of AF and restored all the parameters measured back (<0.05) toward the control values. These findings indicated that FERMKIT, when added at the levels of 0.3 or 0.6% in the 120 ppb AF diets, could modulate the toxicity of AF with percentage sorption capacity of 52.70% at the level 0.3% and 79.85% at the level 0.6% of the diets (experiment 1). In experiment 2, FERMKIT, when added at 0.6% to the 150 ppb ZEN diets for the 4-wk experimental period, diminished the toxicity as shown by body weight gain, weights of testicles, oviducts, Bursa of Fabricius and cloaca eversion score as compared with the controls (<10 ppb ZEN) and 150 ppb ZEN diet with no added FERMKIT. The findings indicated that FERMKIT could be protective against the effects of ZEN in young growing ducks with percentage sorption capacity of 67.11% as evaluated from toxicity index parameter measured when added at 0.6% of the diets containing 150 ppb ZEN.

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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.

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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.