Prediction of Air Temperature and Relative Humidity in Greenhouse via a Multilayer Perceptron Using Environmental Factors

Temperature and relative humidity are important factors in crop cultivation and should be properly controlled for improving crop yield and quality. In order to control the environment accurately, we need to predict how the environment will change in the future. The objective of this study was to predict air temperature and relative humidity at a future time by using a multilayer perceptron (MLP). The data required to train MLP was collected every 10 min from Oct. 1, 2016 to Feb. 28, 2018 in an eight-span greenhouse (1,032 m2) cultivating mango (Mangifera indica cv. Irwin). The inputs for the MLP were greenhouse inside and outside environment data, and set-up and operating values of environment control devices. By using these data, the MLP was trained to predict the air temperature and relative humidity at a future time of 10 to 120 min. Considering typical four seasons in Korea, three-day data of the each season were compared as test data. The MLP was optimized with four hidden layers and 128 nodes for air temperature (R2 = 0.988) and with four hidden layers and 64 nodes for relative humidity (R2 = 0.990). Due to the characteristics of MLP, the accuracy decreased as the prediction time became longer. However, air temperature and relative humidity were properly predicted regardless of the environmental changes varied from season to season. For specific data such as spray irrigation, however, the numbers of trained data were too small, resulting in poor predictive accuracy. In this study, air temperature and relative humidity were appropriately predicted through optimization of MLP, but were limited to the experimental greenhouse. Therefore, it is necessary to collect more data from greenhouses at various places and modify the structure of neural network for generalization.N

Growth and Photomorphogenesis of Cucumber Plants under Artificial Solar and High Pressure Sodium Lamp with Additional Far-red Light

Plant growth and morphology are affected by light environments. The morphogenesis and growth of the plants growing in plant factories are different from those grown under sunlight due to the effect of far-red light included in sunlight. The objective of this study was to compare the morphogenesis and growth of cucumber plants grown under artificial sunlight, high pressure sodium lamp (HPS), and HPS with additional far-red light (HPS+FR). The artificial solar (AS) with a spectrum similar to sunlight was manufactured using sulfur plasma lamp, incandescent lamp, and green-reducing optical film. HPS was used as a conventional electrical light source and far-red LEDs were added for HPS+FR. The optical properties of each light source was analyzed. The morphogenesis, growth, and photosynthetic rate were compared in each light source. The ratio of red to far-red lights and phytochrome photostationary state were similar in AS and HPS+FR. There were significant differences in morphology and growth between HPS and HPS+FR, but there were no significant differences between AS and HPS+FR. SPAD was highest in HPS, while photosynthetic rate was higher at AS and HPS. Although the photosynthetic rate in HPS+FR was lower than HPS, the growth was similar in AS. It was because canopy light interception was increased by longer petioles and larger leaf areas induced by FR. It is confirmed that the electrical light with additional far-red light induces similar photomorphogenesis and growth in sunlight spectrum. From the results, we expect that similar results will be obtained by adding far-red light to electrical light sources in plant factories.N

Theoretical and experimental analysis of nutrient variations in electrical conductivity-based closed-loop soilless culture systems by nutrient replenishment method

In closed-loop soilless culture systems, variation in nutrients can lead to instability in the nutrient management and forced discharge of nutrients and water. Total nutrients absorbed by plants are replenished in an electrical conductivity-based closed-loop system, and fluctuation in electrical conductivity within a certain range around the initial value can be expected. However, this is not always observed in systems using conventional nutrient-replenishment methods. The objectives of this study were to analyze nutrient variation in a closed-loop soilless culture system based on a theoretical model and derive an alternative nutrient-replenishment method. The performance of the derived alternative method was compared with a conventional nutrient-replenishment method through simulation analysis. A demonstration experiment using sweet peppers was then conducted to confirm whether the theoretical analysis results can be reproduced through actual cultivation. The average amounts of injected nutrients during the experimental period of four months in the conventional and alternative methods were 2257 and 1054 g, respectively. There was no significant difference in the yield of sweet peppers between the two methods. The substrate electrical conductivity in the alternative method was maintained at 2.7 dS.m(-1) +/- 0.5 within the target electrical conductivity value, while that in the conventional method gradually increased to 5.0 dS.m(-1) +/- 1.2. In a simulation study, results similar to the demonstration experiment were predicted. Total nutrient concentrations in the alternative method showed fluctuations around the target value but did not continuously deviate from the target value, while those in the conventional method showed a tendency to increase. As a whole, these characteristics of the alternative method can help in minimizing nutrients and water emissions from the cultivation system.OAIID:RECH_ACHV_DSTSH_NO:T201917057RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A002344CITE_RATE:2.259DEPT_NM:식물생산과학부EMAIL:[email protected]_YN:YY

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

BackgroundRoot-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.ResultsThe 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=1.48meqL(-1). The used values of input interval and time step were 1h and 168 (1week), 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.ConclusionsWe 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.OAIID:RECH_ACHV_DSTSH_NO:T201907247RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A002344CITE_RATE:4.269DEPT_NM:식물생산과학부EMAIL:[email protected]_YN:YY

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

Nondestructive and Continuous Fresh Weight Measurements of Bell Peppers Grown in Soilless Culture Systems

Fresh weight is a direct index of crop growth. It is difficult to continuously measure the fresh weight of bell peppers grown in soilless cultures, however, due to the difficulty in identifying the moisture condition of crops and growing media. The objective of this study was to develop a continuous and nondestructive measuring system for the fresh weight of bell peppers grown in soilless cultures considering the moisture content of growing media. The system simultaneously measures the trellis string's supported weight and gravitational weight using tensile load cells. The moisture weight of growing media was calibrated during the growth period using changes in moisture content before and after the first irrigation of the day. The most stable time period for the measurement, from 03:00 to 06:00, was determined by analyzing the diurnal change in relative water content. To verify the accuracy of the system, the fruits, stems, leaves, and roots' fresh weights were measured manually. The fresh weights measured by the developed system were in good agreement with those manually measured. The results confirm that our system can reliably and accurately measure fresh weights of bell peppers grown in soilless cultures. This method can be applied to continuous growth data collection for other crops grown in soilless cultures.OAIID:RECH_ACHV_DSTSH_NO:T201917059RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A002344CITE_RATE:2.259DEPT_NM:식물생산과학부EMAIL:[email protected]_YN:YY

Time Change in Spatial Distributions of Light Interception and Photosynthetic Rate of Paprika Estimated by Ray-tracing Simulation

To estimate daily canopy photosynthesis, accurate estimation of canopy light interception according to a daily solar position is needed. However, this process needs a lot of cost, time, manpower, and difficulty when measuring manually. Various modeling approaches have been applied so far, but it was difficult to accurately estimate light interception by conventional methods. The objective of this study is to estimate the spatial distributions of light interception and photosynthetic rate of paprika with time by using 3D-scanned plant models and optical simulation. Structural models of greenhouse paprika were constructed with a portable 3D scanner. To investigate the change in canopy light interception by surrounding plants, the 3D paprika models were arranged at 1×1 and 9×9 isotropic forms with a distance of 60 cm between plants. The light interception was obtained by optical simulation, and the photosynthetic rate was calculated by a rectangular hyperbola model. The spatial distributions of canopy light interception of the 3D paprika model showed different patterns with solar altitude at 9:00, 12:00, and 15:00. The total canopy light interception decreased with an increase of surrounding plants like an arrangement of 9×9, and the decreasing rate was lowest at 12:00. The canopy photosynthetic rate showed a similar tendency with the canopy light interception, but its decreasing rate was lower than that of the light interception due to the saturation of photosynthetic rate of upper leaves of the plants. In this study, by using the 3D-scanned plant model and optical simulation, it was possible to analyze the light interception and photosynthesis of plant canopy under various conditions, and it can be an effective way to estimate accurate light interception and photosynthesis of plants.N

Development of a coupled photosynthetic model of sweet basil hydroponically grown in plant factories

For the production of plants in controlled environments such as greenhouses and plant factories, crop modeling and simulations are effective tools for configuring the optimal growth environment. The objective of this study was to develop a coupled photosynthetic model of sweet basil (Ocimum basilicum L.) reflecting plant factory conditions. Light response curves were generated using photosynthetic models such as negative exponential, rectangular hyperbola, and non-rectangular hyperbola functions. The light saturation and compensation points determined by regression analysis of light curves using modified non-rectangular hyperbola function in sweet basil leaves were 545.3 and 26.5 A mu mol center dot m(-2)center dot s(-1), respectively. The non-rectangular hyperbola was the most accurate with complicated parameters, whereas the negative exponential was more accurate than the rectangular hyperbola and could more easily acquire the parameters of the light response curves of sweet basil compared to the non-rectangular hyperbola. The CO2 saturation and compensation points determined by regression analysis of the A-C-i curve were 728.8 and 85.1 A mu mol center dot mol(-1), respectively. A coupled biochemical model of photosynthesis was adopted to simultaneously predict the photosynthesis, stomatal conductance, transpiration, and temperature of sweet basil leaves. The photosynthetic parameters, maximum carboxylation rate, potential rate of electron transport, and rate of triose phosphate utilization determined by Sharkey's regression method were 102.6, 117.7, and 7.4 A mu mol center dot m(-2)center dot s(-1), respectively. Although the A-C-i regression curve of the negative exponential had higher accuracy than the biochemical model, the coupled biochemical model enable to physiologically explain the photosynthesis of sweet basil leaves.OAIID:RECH_ACHV_DSTSH_NO:T201619383RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A002344CITE_RATE:.812DEPT_NM:식물생산과학부EMAIL:[email protected]_YN:YN

Synthesis and photophysical properties of blue-emitting fluorescence dyes derived from naphthalimide derivatives containing a diacetylene linkage group

Four naphthalimide-based dyes with a diacetylene linkage at the 3- or 4-position were synthesized to improve the thermal stability of the fluorescence dye as well as the efficiency of fluorescent emission at blue region. The absorption and fluorescence properties of the synthesized dyes were also investigated. The geometries and molecular orbitals of the dyes prepared were simulated using by density functional theory and time-dependent density functional theory using Gaussian 09. Furthermore, the suitability of the dyes for application in light conversion films was examined. N-Phenyl groups were found to have a greater effect on the fluorescence of naphthalimide-based dyes than analogue containing an N-alkyl group. In addition, investigation of the effect of diacetylene linkages at the 3- or 4-positions of naphthalimide-based dyes showed that the fluorescence was influenced by the electron-donating effect of the diacetylene linkage which could afford more conjugation of pi orbitals of the dyes. Four blue fluorescence dyes derived from 1,8-naphthalimide containing a diacetylene linkage were synthesized and then coated in PE film. The photophysical properties were analyzed using density functional theory (DFT) calculations, and excitations from the highest occupied molecular orbitals (HOMOs) to the lowest unoccupied molecular orbitals (LUMO5) of the dyes were simulated at the B3LYP/6-31G level of theory via the Gaussian 09 suite of programs. Using these methods, the influences of the position of the ethynyl linkages, alkyl groups, and phenyl groups on the electronic and fluorescence properties of the dyes were also clarified.OAIID:RECH_ACHV_DSTSH_NO:T201815031RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A002344CITE_RATE:3.767DEPT_NM:식물생산과학부EMAIL:[email protected]_YN:YN

Interpolation of greenhouse environment data using multilayer perceptron

For analysis of greenhouse environments using big data, measuring data should be continuously collected without data loss caused by sensing and networking problems. Recently, deep learning approach has been widely used for precision agriculture. However, in order to use deep learning methods, the enormous amount of reliable data is necessary. The objective of this study is to compare the interpolation accuracy of greenhouse environment data using multilayer perceptron (MLP) with existing statistical and machine learning methods. Linear and spline interpolations were selected as statistical methods, and linear models, MLP and random forest (RF) were selected as machine learning methods. The raw data used for interpolation were greenhouse environment data collected from October 2, 2016 to May 31, 2018 where Irwin mango (Mangifera indica L cv. Irwin) trees were cultivated. As a result, the linear interpolation method showed the highest R-2 (average 0.96) in short-term data loss conditions, but the MLP showed R-2 = 0.95. However, in long-term data loss conditions, the accuracies of the linear, spline, and regression interpolations decreased, but the accuracies of the MLP and RF remained stable. However, MLP showed better accuracies than RF. Therefore, the MLP was better suited to interpolating greenhouse environment data because short- and long-term data loss actually occurred simultaneously when collecting greenhouse environment data. The trained MLP showed the high accuracy in both short- and long-term data interpolations, indicating that MIT can also be complementally used with existing methods. The trained MLP accurately estimated the missing data in the greenhouse and will contribute to the analysis of big data collected from greenhouses.OAIID:RECH_ACHV_DSTSH_NO:T201917055RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A002344CITE_RATE:3.171DEPT_NM:식물생산과학부EMAIL:[email protected]_YN:YN