• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.672-680
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• 2019

This paper proposes "A Smart Farm Environment Optimization and Yield Prediction Platform based on IoT and Deep Learning" which gathers bio-sensor data from farms, diagnoses the diseases of growing crops, and predicts the year's harvest. The platform collects all the information currently available such as weather and soil microbes, optimizes the farm environment so that the crops can grow well, diagnoses the crop's diseases by using the leaves of the crops being grown on the farm, and predicts this year's harvest by using all the information on the farm. The result shows that the average accuracy of the AEOM is about 15% higher than that of the RF and about 8% higher than the GBD. Although data increases, the accuracy is reduced less than that of the RF or GBD. The linear regression shows that the slope of accuracy is -3.641E-4 for the ReLU, -4.0710E-4 for the Sigmoid, and -7.4534E-4 for the step function. Therefore, as the amount of test data increases, the ReLU is more accurate than the other two activation functions. This paper is a platform for managing the entire farm and, if introduced to actual farms, will greatly contribute to the development of smart farms in Korea.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.55 no.4
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• pp.284-291
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• 2010

A simulation modeling for predicting the harvesting date with high potato solids consists of development of mathematical models. The mathematical model on potato growth and its development should be obtained by using agricultural elements which analyze relations of solar radiation quantity, temperature, photon quantity, carbon dioxide exchange rate, water stress and loss, relative humidity, light intensity, and wind etc. But more reliable way to predict harvesting date against climatic change employs in vivo energy consumption for growth and induction shape in a slight environmental adaptation. Therefore, to calculate in vivo energy loss, we take a concept of estimate of the amount of basal metabolism in each tuber on the basis of $Wm={\int}^m_tf(x)dt$ and $Tp=\frac{Tm{\cdot}Wm^{Tp}}{Wm^{Tm}}$. In the validation experiments, results of measuring solid accumulation of potato harvested at simulated date agreed fairly well with the actual measured values in each regional field during the growth period of 2005-2009. The calculation method could be used to predict an appropriate harvesting date for a production of high potato solids according to weather conditions.

• The Korean Journal of Pesticide Science
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• v.15 no.4
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• pp.463-470
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• 2011

This study was carried out to survey the residual characteristic of chlorpyrifos and estimate their residues in squash before harvest. The pesticide was sprayed onto the crop at the recommended and its double rates 10 days before the prearranged harvest and sampling was done at 0, 2, 3, 5, 6, 7 and 10 days after spraying. The amounts of the chlorpyrifos residue in the crop was analyzed by chromatographic method. Limit of detection (LOD) of chlorpyrifos was 0.005 mg/kg and its recovery ranged from 95.21 to 102.69%. The initial concentration of chlorpyrifos sprayed with recommended dose exceeded its MRL of 0.1 mg/kg but its concentration was less than its MRL 10 days after application. However its concentration in case of the double dose was over its MRL both immediately and 10 days after application. Biological half-lives of chlorpyrifos sprayed onto squash was 2.5 and 2.9 days at the recommended and double doses, respectively. Ten days later, the residual concentration of chlorpyrifos in squash was decreased substantially. The concentration of chlorpyrifos was estimated in squash at the given day using its regression equations. The estimated concentration of chlorpyrifos in case of application with recommended dose was below its MRL at 10 days after application but its concentration in case of application with double dose was over its MRL at 10 days of the prearranged harvest. The rate of the estimated daily intake (EDI) of chlorpyrifos to its acceptable daily intake (ADI) was 282% right after application but it decreased to less than 18% at 10 days of the prearranged harvest.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.709-720
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• 2017

In this study, we tried to analyze the correlation between corn yield and, satellite-based vegetation index, NDVI (Normalized Difference Vegetation Index) and various climatic factors in the three provinces of Northeast China during the past 20 years (1996-2015). The corn yields in the corn cultivation area of all three provinces showed a statistically significant positive correlation with the NDVI of the harvest period. Also, these have significant negative correlation with the daily maximum temperature in August and September and the occurrence frequency of above $30^{\circ}C$ for the summer season. The correlation between the corn yields and the precipitation showed a significant positive coefficient in only Liaoning Province in July, but the correlation was not found in Jilin and Heilongjiang Provinces. In this study, the NDVI and the daily maximum temperature data are suitable to be used as predictors of corn yield in the three provinces of Northeast China provinces.

• Journal of Korean Society of Forest Science
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• v.90 no.2
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• pp.176-183
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• 2001

Loblolly pine (Pinus taeda L.) is the most economically important timber producing species in the southern United States. Much attention has been given to predicting diameter distributions for the solution of multiple-product yield estimates. The three-parameter Weibull diameter distribution yield prediction systems were developed for loblolly pine plantations. A parameter recovery procedure for the Weibull distribution function based on four percentile equations was applied to develop diameter distribution yield prediction models. Four percentiles (0th, 25th, 50th, 95th) of the cumulative diameter distribution were predicted as a function of quadratic mean diameter. Individual tree height prediction equations were developed for the calculation of yields by diameter class. By using individual tree content prediction equations, expected yield by diameter class can be computed. To reduce rounding-off errors, the Weibull cumulative upper bound limit difference procedure applied in this study shows slightly better results compared with upper and lower bound procedure applied in the past studies. To evaluate this system, the predicted diameter distributions were tested against the observed diameter distributions using the Kolmogorov-Smirnov two sample test at the ${\alpha}$=0.05 level to check if any significant differences existed. Statistically, no significant differences were detected based on the data from 516 evaluation data sets. This diameter distribution yield prediction system will be useful in loblolly pine stand structure modeling, in updating forest inventories, and in evaluating investment opportunities.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1427-1435
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• 2017

The smart farm is a remarkable system because it utilizes information and communication technologies in agriculture to bring high productivity and excellent qualities of crops. It automatically measures the growth environment of the crops and accumulates huge amounts of environmental information in real time growing in smart farms using multi-variable control of environmental factors. The statistical model using the collected big data will be helpful for decision making in order to control optimal growth environment of crops in smart farms. Using data collected from a smart farm of tomato, we carried out multiple regression analysis to determine the relationship between yield and environmental factors and to predict yield of tomato. In this study, appropriate parameter modification was made for environmental factors considering tomato growth. Using these new factors, we fit the model and derived the optimal environmental factors that affect the yields of tomato. Based on this, we could predict the yields of tomato. It is expected that growth environment can be controlled to improve tomato productivities by using statistical model.

• Journal of Bio-Environment Control
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• v.27 no.4
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• pp.349-355
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• 2018

This study was aimed at predicting the yield of paprika (Capsicum annuum L.) through analyzing the growth characteristics, yield pattern and greenhouse environment. In the greenhouse of the Gyeongnam area (667 m above sea level), the red paprika 'Cupra' and the yellow paprika 'Fiesta' were grown from July 5, 2016 to July 15, 2017. The planting density was $3.66plants/m^2$ and attracted 2 stems. During the cultivation period, the average external radiation of the glasshouse was $14.36MJ/m^2/day$ and the internal average temperature was controlled as $20.1^{\circ}C$. After 42 weeks of planting, the growth rate of 'Cupra' was 7.3 cm/week and that of 'Fiesta' was 6.9 cm/week. The first fruit setting of 'Cupra' appeared at 1.0th node and 'Fiesta' at 2.7th node. The first harvest of 'Fiesta' was 11 weeks after planting and 'Fiesta' was 14 weeks. Comparing the yield per 10 a until the end of the cultivation in July, 'Fiesta' was 19,307 kg, which was 2.4% higher than that of 'Cupra'. And the fruit weight ratio of over 200 g of 'Cupra' was 27.7% which was 7.7% higher than that of 'Fiesta'. The average required days to harvest after fruit setting of 'Cupra' was 72.6 days and 'Fiesta' was 63.8 days. According to the relationship between the average required days to harvest and the cumulative radiation (during from fruit setting to harvest), the more radiation increases the less required days to harvest increases after February. In terms of yield, 'Cupra' increased in yield as the cumulative radiation increased, while 'Fiesta' showed an irregular pattern. Cumulative radiation from fruit setting to harvest was negatively correlated with required days to harvest after February in both cultivars. But in relation to yield, there were difference between 'Cupra' and 'Fiesta'.

• Journal of Korean Society of Forest Science
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• v.76 no.4
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• pp.390-396
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• 1987

The model and its example were provided to predict wood production for a district forest planning. The method of Gentan probability is widely accepted for the prediction of wood production. The suggested model is different in the decision of cutting age distribution from that of Prof. Suzuki; the former can use either Weibull distribution or Gamma distribution, but the latter is possible only by Gamma distribution. This developed system can be used not only for establishing a district forest planning, but also for providing forest management information, such as periodic harvest volume, growing stock, labor requirement, and so forth.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.706-708
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• 2017

노지에서 재배되는 실외작물의 경우 외부 환경에 노출되어 재배되기에 생육 또는 수학시기가 외부 요인에 많은 영향을 받는다. 이러한 외부 요인 중 과일의 당도 및 수확량에 많은 영향을 미치는 요인은 바로 날씨이다. 고온의 날씨 또는 저온의 날씨가 지속되거나 강한 풍속, 적절한 강수가 이루어지지 않을 경우 과일의 당도가 낮아지거나, 흠집이 발생할 수 있어 과일 도매가격에 영향을 미치게 된다. 본 논문에서는 월별 평균 온도, 강우량, 습도, 일사량, 최대풍속 등의 날씨 관련 데이터와 제사 또는 명절에 자주 사용되는 과실류인 배, 단감, 사과, 수박의 도매가격간의 상관관계를 분석을 통해 얻은 결과로 추후 농산물 가격 예측 또는 과일 가격 예측 연구에 기여를 하고자 한다.

• Journal of Korean Society of Forest Science
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• v.109 no.4
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• pp.504-511
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• 2020

This study estimated planned felling volumes to set targets for management planning of nationwide country-owned forests. Estimates were made using timber harvest prediction methods that use probability density functions, including area weighting (AW), area ratio weighting (ARW), and sample area change ratio weighting (SCRW). Country-owned forest areas in 2010 and 2015 were used to estimate planned felling volumes, as shown in basic forest statistics, and calculations were made assuming that the felling areas were the changes in the forest area over the 5-year period. For the age classes of V-VI, the average felling ages for AW, ARW, and SCRW were 5.41, 5.56, and 5.37, respectively, and the felling areas were 594,462, 586,704, and 580,852 ha, respectively, with ARW reaching closest to the actual changes. The actual changes in the areas and chi-squared test results were most stable with the SCRW method. This study showed that SCRW was more adequate than AW and ARW as a method to predict timber harvests for forest management planning.