• Journal of Bio-Environment Control
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• v.22 no.2
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• pp.182-187
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• 2013

In closed plant production system like plant factory, changes in environmental factors should be identified for conducting efficient environmental control as well as predicting energy consumption. Since high relative humidity (RH) is essential for crop production in the plant factory, transpiration is closely related with RH and should be quantified. In this study, four varieties of lettuces (Lactuca sativa L.) were grown in a plant factory, and the leaf areas and transpiration rates of the plants according to DAT (day after transplanting) were measured. The coefficients of the simplified Penman-Monteith equation were calibrated in order to calculate the transpiration rate in the plant factory and the total amount of transpiration during cultivation period was predicted by simulation. The following model was used: $E_d=a*(1-e^{-k*LAI})*RAD_{in}+b*LAI*VPD_d$ (at daytime) and $E_n=b*LAI*VPD_n$ (at nighttime) for estimating transpiration of the lettuce in the plant factory. Leaf area and transpiration rate increased with DAT as exponential growth. Proportional relationship was obtained between leaf area and transpiration rate. Total amounts of transpiration of lettuces grown in plant factory could be obtained by the models with high $r^2$ values. The results indicated the simplified Penman-Monteith equation could be used to predict water requirements as well as heating and cooling loads required in plant factory system.

• Journal of Bio-Environment Control
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• v.20 no.4
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• pp.247-252
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• 2011

Electrical conductivity, drainage, and irrigation amount of nutrient solution are important factors for determination of the mixing ratio of fresh and reused nutrient solutions in closed-loop soilless culture. Generally a fixed mixing ratio is applied in commercial scale greenhouses using solar radiation-based irrigation system. Although it ensures continuous supply of fresh nutrient solution in the mixing process, occasional discharge of the drainage is inevitably required. This study was conducted to compare the nutrient replenishing effect under different mixing processes and to investigate appropriate mixing process. For this experiment, a fixed mixing ratio (FR), modifiable mixing ratio (MR), and open-loop (OP) as control were applied. Mixing ratio was determined by a set value of EC for dilution of collected drainage in FR and the set values of 1.0 and $2.0dS{\cdot}m^{-1}$ were used as treatments (FR 1.0 and FR 2.0), respectively. In MR, mixing ratio was determined based on EC and volume of drainage within irrigation volume per event. The volume of drainage stored in the drainage tank tended to increase in FR 1.0. Although such trend was not observed in FR 2.0 and MR, the volume of drainage stored in MR was lower than that in FR 2.0. The ion balance of $Mg^{2+}:K^+:Ca^{2+}$ or $SO^{2-}_4:NO^-_3:PO^{3-}_4$ in the drainage and reused nutrient solution changed within a narrow range regardless of treatment.

• Journal of Bio-Environment Control
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• v.28 no.2
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• pp.95-103
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• 2019

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 ($1,032m^2$) 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 ($R^2=0.988$) and with four hidden layers and 64 nodes for relative humidity ($R^2=0.990$). 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.

• Journal of Bio-Environment Control
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• v.18 no.3
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• pp.238-243
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• 2009

To investigate the characteristics of moisture content (MC), moisture distribution and starting point of drainage in a rockwool slab culture, time domain reflectometry (TDR) sensors were used in a drip irrigation system. MC values ($0{\sim}100%$) measured by TDR sensors in a slab were compared to those by loadcells. Seventy two seedlings of paprika (Capsicum annuum L.) were cultured for $5{\sim}6$ months in a green-house and the starting point of irrigation was determined by the average value of three TDR sensors which were inserted diagonally across the slabs under the plants. MCs as a standard for starting point of irrigation by TDR were determined with 40%, 50%, and 60%. Distribution of MCs in a slab measured with five TDR sensors equally spaced from two irrigation points were not much different when the MC in the slab increased from zero to saturation point. The saturated MCs in the slab were presented at $58{\sim}65%$ and the drain was started when the MC became around $50{\sim}55%$. At the saturated MC in the slab, TDR sensors presented 100% but the values from the loadcell showed 90% at the same time. However, measurement errors between two methods for MC remarkably decreased with a decrease in the MC in a slab. Especially when the MC was maintaining below 60%, the errors between TDR and loadcell methods for measuring MC in the rock-wool slab were less than 5%. There were no significant differences in number of fruits and fresh and dry weights of fruits when they were cultured under the different MC conditions with three irrigation regimes (40%, 50%, and 60%). These results indicated that the MC control by TDR sensors in a rock-wool based paprika culture can be suggested as a method to determine the starting point of irrigation for a soilless culture system.