• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.384-395
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• 2023

Since transpiration plays a key role in optimal irrigation management, knowledge of the irrigation demand of crops like tomatoes, which are highly susceptible to water stress, is necessary. One way to determine irrigation demand is to measure transpiration, which is affected by environmental factor or growth stage. This study aimed to estimate the transpiration amount of tomatoes and find a suitable model using mathematical and deep learning models using minute-by-minute data. Pearson correlation revealed that observed environmental variables significantly correlate with crop transpiration. Inside air temperature and outside radiation positively correlated with transpiration, while humidity showed a negative correlation. Multiple Linear Regression (MLR), Polynomial Regression model, Artificial Neural Network (ANN), Long short-term Memory (LSTM), and Gated Recurrent Unit (GRU) models were built and compared their accuracies. All models showed potential in estimating transpiration with R² values ranging from 0.770 to 0.948 and RMSE of 0.495 mm/min to 1.038 mm/min in the test dataset. Deep learning models outperformed the mathematical models; the GRU demonstrated the best performance in the test data with 0.948 R² and 0.495 mm/min RMSE. The LSTM and ANN closely followed with R² values of 0.946 and 0.944, respectively, and RMSE of 0.504 m/min and 0.511, respectively. The GRU model exhibited superior performance in short-term forecasts while LSTM for long-term but requires verification using a large dataset. Compared to the FAO56 Penman-Monteith (PM) equation, PM has a lower RMSE of 0.598 mm/min than MLR and Polynomial models degrees 2 and 3 but performed least among all models in capturing variability in transpiration. Therefore, this study recommended GRU and LSTM models for short-term estimation of tomato transpiration in greenhouses.

• Korean Journal of Agricultural Science
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• v.20 no.2
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• pp.167-180
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• 1993

Most of small size diesel engines are widely used with the same size and weight flywheel in the levels of 6.0kW and 7.5kW. This study was conducted to obtain basic data which affect the engine performance of the power tiller. The flywheel weight was considered as a major factor in this research. Basically, fuel consumption ratio, motoring loss, torque, vibration and mechanical efficiency of the engine were measured and analyzed on four levels of flywheel weight, 32.2, 29.4, 26.2 and $24.2kg_f$, respectively. Results were obtained as follows: 1. The weights of flywheel were $23.7kg_f$ from design program of JSME and $24.5kg_f$ from ASME and SAE design criteria. Therefore, the flywheel weight of $32.2kg_f$ might be reduced about $8kg_f$ in 7.5kW engine. 2. The rated outputs of 6.0kW and 7.5kW engine were actually 7.43kW and 7.85kW, respectively. When flywheel weight was reduced from $32.2kg_f$ to $24.2kg_f$, outputs were increased from 7.43kW to 7.70kW in 6.0kW engine and from 7.85kW to 8.25kW in 7.5kW engine. 3. When the flywheel weight was reduced from $32.2kg_f$ to $24.2kg_f$, fuel consumption ratio was decreased from 300.8 to 296.8g/kW-hr in 6.0kW engine and also from 313.6 to 312.8g/kW-hr in 7.5 kW engine, respectively. 4. When the flywheel weight was reduced from $32.2kg_f$ to $24.2kg_f$, mechanical efficiency of engine was increased from 76.1% to 76.8% in 6.0kW engine and also from 76.7% to 77.0% in 7.5kW engine, respectively. 5. When the flywheel weight was reduced from $32.2kg_f$ to $24.2kg_f$, vibration was decreased at X-axis and Z-axis in 6.0kW engine, however, slightly increased at Y-axis in 6.0kW engine and at all axes in 7.5kW engine. 6. When the flywheel weight was reduced from $32.2kg_f$ to $24.4kg_f$ motoring loss was decreased from 2.33kW to 1.75kW in 6.0kW engine and also from 2.46kW to 1.84kW in 7.5kW engine.

• Korean Journal of Agricultural Science
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• v.15 no.2
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• pp.143-152
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• 1988

This study was conducted to obtain basic data which affected engine performance of the power tiller being widely used in the rural area. Among the various factors affected engine performance, only flywheel weight was considered as the major factor in this study. Fuel consumption ratio, motoring loss, torque, vibration and mechanical efficiency of the engine tested were measured and analyzed on the four levels of flywheel weight (32.2, 29.7, 26.4, 24.2 kg). The results obtained were as follows: 1. The maximum output of 6 and 7.5 kW engine was 7.43 kW and 7.85 kW respectively. When flywheel weight was reduced from 32.2 kg to 24.2 kg, output power of the engine was increased 0.27 kW in 6 kW engine and increased 0.39 kW in 7.5 kW engine. 2. The fuel consumption ratio was decreased from 300.8 to 296.8 g/kW-hr in 6 kW engine and decreased from 313.6 to 312.8 g/kW-hr in 7.5 kW engine when the flywheel weight was reduced from 32.2 kg to 24.2 kg. 3. The mechanical efficiencies of the engine was increased from 76.1 to 76.8% in 6 kW engine and increased from 76.7 to 77.0% in 7.5 kW engine when the flywheel weight was reduced from 32.2 kg to 24.2 kg. 4. When the flywheel weight was reduced from 32.2 kg to 24.2 kg, a tendency of a little decrease of vibration at X- and Z-axis in 6 kW engine and of a little increase of vibration at Y-axis in 6 kW engine and all directions in 7.5 kW engine was observed. 5. Motoring losses was decreased from 2.33 to l.76 kW in 6 kW engine and decreased from 2.46 to 1.84 kW in 7.5 kW engine when the flywheel weight was reduced from 32.2 kg to 24.2 kg. From the above results and the flywheel weight calculated theoretically, it was recommendable that the flywheel weight should be reduced about 7 kg in 6 kW engine and about 10 kg in 7.5 kW engine, respectively.