• Korean Journal of Soil Science and Fertilizer
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• v.42 no.3
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• pp.167-171
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• 2009

An attempt was made to provide the most reasonable fertilizer recommendation for radish crop based on soil analysis data and yield response to the N, P, K fertilizers, which was obtained from field experiments on 2004 in highland, 850 meters above the sea level. Optimum times of NPK application to past application amount based on soil test were 0.90-0.77-0.50 for radish. The adjusted NPK recommendation models of highland soil were made by adding the application times to past application methods which were based on chemical properties of soil. The revised models for fertilizer application were recommended to decrease the amount of N, P, K by 10-23-50% for radish in highland. In application to total cultivation area, 2,546ha for radish, saving amounts of NPK fertilizers with these adjusted recommendation in comparison with past application levels will be 244.4 tons for radish. Using the optimal application amounts for radish, we will can reduce agricultural pollution without affecting crop yields.

• Korean Journal of Agricultural Science
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• v.41 no.1
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• pp.23-27
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• 2014

Potato late blight occurrence is caused by various environmental factors, and the progress can be regularly predicted so that several predictive models have been developed. The models predict the timing of the disease occurrence, but they do not include the methods of the disease control. Effective fungicide control, economic threshold, prediction models were investigated in the study to reflect on customized control modules for the model forecasting the occurrence of potato late blight.

• Korean Journal of Agricultural Science
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• v.41 no.1
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• pp.29-34
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• 2014

Phytophthora blight occurrence is caused by various environmental factors, and the progress can be regularly predicted so that several predictive models have been developed. The models predict the timing of the disease occurrence, but they do not include the methods of the disease control. Effective fungicide control, control threshold, prediction models were investigated in the study to reflect on customized control modules for the model forecasting the occurrence of Phytophthora blight on hot pepper.

• The Plant Pathology Journal
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• v.36 no.1
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• pp.54-66
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• 2020

This study was conducted to evaluate usefulness of numerical weather prediction data generated by the Unified Model (UM) for plant disease forecast. Using the UM06- and UM18-predicted weather data, which were released at 0600 and 1800 Universal Time Coordinated (UTC), respectively, by the Korea Meteorological Administration (KMA), disease forecast on bacterial grain rot (BGR) of rice was examined as compared with the model output based on the automated weather stations (AWS)-observed weather data. We analyzed performance of BGRcast based on the UM-predicted and the AWS-observed daily minimum temperature and average relative humidity in 2014 and 2015 from 29 locations representing major rice growing areas in Korea using regression analysis and two-way contingency table analysis. Temporal changes in weather conduciveness at two locations in 2014 were also analyzed with regard to daily weather conduciveness (C_i) and the 20-day and 7-day moving averages of C_i for the inoculum build-up phase (C_inc) prior to the panicle emergence of rice plants and the infection phase (C_inf) during the heading stage of rice plants, respectively. Based on C_inc and C_inf, we were able to obtain the same disease warnings at all locations regardless of the sources of weather data. In conclusion, the numerical weather prediction data from KMA could be reliable to apply as input data for plant disease forecast models. Weather prediction data would facilitate applications of weather-driven disease models for better disease management. Crop growers would have better options for disease control including both protective and curative measures when weather prediction data are used for disease warning.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.195-195
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• 2022

Abiotic stress is a major problem in global agriculture as it negatively affects crop growth, yield, and quality. Wheat (Triticum aestivum) is the world's second-highest-producing food resource, so the importance of mitigating damage caused by abiotic stress has been emerging. In this study, we performed GWAS to search for SNPs associated with salt tolerance and drought tolerance. NaCl (200 mM) treatment was performed at the seedling stage using 613 wheat varieties in Korean wheat core collection. Root length, root surface area, root average diameter, and root volume were measured. Drought stress was applied at the seedling stage, and the above phenotypes were measured. GW AS was performed for each phenotype data using the MLM, MLMM, and FarmCPU models. The best salt-tolerant wheat varieties were 'MK2402', 'Gyeongnam Geochang-1985-3698', and 'Milyang 13', showing superior root growth. The significant SNP AX-94704125 (BA00756838) were identified in all models. The genes closely located to the significant SNP were searched within ± 250 kb of the corresponding SNP. A total of 11 genes were identified within the region. NB-ARC involved in the defense response, FKSI involved in cell wall biosynthesis, and putative BP Ml involved in abiotic stress responses were discovered in the 11 genes. The best drought-tolerant wheat varieties were 'PI 534284', 'Moro of Sind', and 'CM92354-33M-0Y-0M-6Y-0B-0BGD', showing superior root growth. This study discovered SNPs associated with salt tolerance in Korean wheat core collection through GWAS. GWAS of drought tolerance is now proceeding, and the GWAS results will be represented on a poster. The SNPs identified by GWAS can be useful for studying molecular mechanisms of salt tolerance and drought tolerance in wheat.

• Korean journal of applied entomology
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• v.62 no.4
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• pp.315-323
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• 2023

The brassica leaf beetle, Phaedon brassicae Baly (Coleoptera: Chrysomelidae), is one of the important pests infesting cruciferous vegetables. In order to understand the biological characteristics of the insect, we investigated the effects of temperature on development of each life stage, adult longevity and fecundity of P. brassicae at four constant temperatures of 15, 20, 25 and 27.5℃ for immature life stage and five constant different temperatures of 10, 15, 20, 25 and 27.5℃ for adult stage. Eggs and larvae successfully developed next life stage at temperature tested. The development period of egg, larva, and pupa decreased as temperature increased. Lower developmental threshold (LDT) and thermal constant (K) were calculated using linear regression as 8.7℃ and 344.73DD, respectively. Lower and higher threshold temperature (TL and TH) from egg to adult emergence were estimated by Briere function as 5.3℃ and 40.4℃, respectively. Adults produced eggs at the temperature range between 10℃ and 27.5℃, and showed an estimated maximum number, ca. 627.5 eggs at 21.7℃. Adult oviposition models including aging rate, age-specific survival rate, age-specific cumulative oviposition, and temperature-dependent fecundity were constructed. Temperature-dependent development models and adult oviposition models would be useful components to understand the population dynamics of P. brassicae and to establish the strategy of integrated pest management in cruciferous crops.

• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.515-525
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• 2020

This study investigates the potential of bidirectional long short-term memory (Bi-LSTM) for efficient modeling of temporal information in crop classification using multitemporal remote sensing images. Unlike unidirectional LSTM models that consider only either forward or backward states, Bi-LSTM could account for temporal dependency of time-series images in both forward and backward directions. This property of Bi-LSTM can be effectively applied to crop classification when it is difficult to obtain full time-series images covering the entire growth cycle of crops. The classification performance of the Bi-LSTM is compared with that of two unidirectional LSTM architectures (forward and backward) with respect to different input image combinations via a case study of crop classification in Anbadegi, Korea. When full time-series images were used as inputs for classification, the Bi-LSTM outperformed the other unidirectional LSTM architectures; however, the difference in classification accuracy from unidirectional LSTM was not substantial. On the contrary, when using multitemporal images that did not include useful information for the discrimination of crops, the Bi-LSTM could compensate for the information deficiency by including temporal information from both forward and backward states, thereby achieving the best classification accuracy, compared with the unidirectional LSTM. These case study results indicate the efficiency of the Bi-LSTM for crop classification, particularly when limited input images are available.

• Korean Journal of Agricultural Science
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• v.38 no.4
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• pp.747-752
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• 2011

Wireless technology has enabled farmers monitor and control protected production environment more efficiently. Utilization of USN (Ubiquitous Sensor Network) devices also brought benefits due to reduced wiring and central data handling requirements. However, wireless communication loses signal under unfavorable conditions (e.g., blocked signal path, low signal intensity). In this paper, performance of commercial wireless communication devices were evaluated for application to protected crop production. Two different models of wireless communication devices were tested. Sensors used in the study were weather units installed outside and top of a greenhouse (wind velocity and direction, precipitation, temperature and humidity), inside ambient condition units (temperature, humidity, $CO_2$, and light intensity), and irrigation status units (irrigation flow and pressure, and soil water content). Performance of wireless communication was evaluated with and without crop. For a 2.4 GHz device, communication distance was decreased by about 10% when crops were present between the transmitting and receiving antennas installed on the ground, and the best performance was obtained when the antennas were installed 2 m above the crop canopy. When tested in a greenhouse, center of a greenhouse was chosen as the location of receiving antenna. The results would provide information useful for implementation of wireless environment monitoring system for protected crop production using USN devices.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.486-486
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• 2018

During the post green revolution era, wheat and rice were the main crops of concern to cater the food security issues of Pakistan. The use of semi dwarf high yielding varieties along with extensive use of fertilizers and surface and ground water lead to substantial increase in crop production. However, the higher crop productivity came at the cost of over exploitation of the precious land and water resources, which ultimately has resulted in the dwindling production rates, loss of soil fertility, and qualitative and quantitative deterioration of both surface and ground water bodies. Recently, during the past two decades, severe climate changes are further pushing the Pakistan's wheat-rice system towards its limits. This necessitates a careful analysis of the current crop water requirements and water footprints (both green and blue) to project the future trends under the most likely climate change phenomenon. This was done by using the FAO developed CROPWAT model v 8.0, coupled with the statistically-downscaled climate projections from the 8 Global Circulation Models (GCMs), for the two future time slices, 2030s (2021-2050) and 2060s (2051-2080), under the two Representative Concentration Pathways (RCPs): 4.5 and 8.5. The wheat-rice production system of Punjab, Pakistan was considered as a case study in exploration of how the changing climate might influence the crop water requirements and water footprints of the two major crops. Under the worst, most likely future scenario of temperature rise and rainfall reduction, the crop water requirements and water footprints, especially blue, increased, owing to the elevated irrigation demands originating from the accelerated evapotranspiration rates. A probable increase in rainfall as envisaged by some GCMs may partly alleviate the adverse impacts of the temperature rise but the higher uncertainties associated with the predicated rainfall patterns is worth considering before reaching a final conclusion. The total water footprints were continuously increasing implying that future climate would profoundly influence the crop evapotranspiration demands. The results highlighted the significance of the irrigation water availability in order to sustain and improve the wheat-rice production system of Punjab, Pakistan.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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• pp.15-26
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• 2022

3D open-field farm model developed from UAV (Unmanned Aerial Vehicle) data could make crop monitoring easier, also could be an important dataset for various fields like remote sensing or precision agriculture. It is essential to separate crops from the non-crop area because labeling in a manual way is extremely laborious and not appropriate for continuous monitoring. We, therefore, made a 3D open-field farm model based on UAV images and developed a crop segmentation model using a supervised machine learning algorithm. We compared performances from various models using different data features like color or geographic coordinates, and two supervised learning algorithms which are SVM (Support Vector Machine) and KNN (K-Nearest Neighbors). The best approach was trained with 2-dimensional data, ExGR (Excess of Green minus Excess of Red) and z coordinate value, using KNN algorithm, whose accuracy, precision, recall, F1 score was 97.85, 96.51, 88.54, 92.35% respectively. Also, we compared our model performance with similar previous work. Our approach showed slightly better accuracy, and it detected the actual crop better than the previous approach, while it also classified actual non-crop points (e.g. weeds) as crops.