• Proceedings of the Korean Society of Crop Science Conference
• /
• 2022.10a
• /
• pp.58-58
• /
• 2022

With the development of advanced technology, automation of agricultural work is spreading. In association with the 4th industrial revolution-based technology, research on field smart farm technology is being actively conducted. A state-of-the-art unmanned automated agricultural production demonstration complex was established in Naju-si, Jeollanam-do. For the operation of the demonstration area platform, it is necessary to build a sophisticated, advanced, and intelligent field smart farming model. For the operation of the unmanned automated agricultural production demonstration area platform, we are building data on the growth of soybean for smart cultivated crops and conducting research to determine the optimal time for agricultural work. In order to operate an unmanned automation platform, data is collected to discover digital factors for water management immediately after planting, water management during the growing season, and determination of harvest time. A subsurface drip irrigation system was established for smart water management. Irrigation was carried out when the soil moisture was less than 20%. For effective water management, soil moisture was measured at the surface, 15cm, and 30cm depth. Vegetation indices were collected using drones to find key factors in soybean production prediction. In addition, major growth characteristics such as stem length, number of branches, number of nodes on the main stem, leaf area index, and dry weight were investigated. By discovering digital factors for effective decision-making through data construction, it is expected to greatly enhance the efficiency of the operation of the unmanned automated agricultural production demonstration area.

• The Korean Journal of Applied Statistics
• /
• v.36 no.6
• /
• pp.619-629
• /
• 2023

Fertilizers are crucial for increasing crop yield, but using too much of them without taking into account the nutrients that the crops need can increase costs for farm management and have a negative impact on the environment. Through smart agriculture, fertilizers can be applied as needed at the right time to reflect the growth characteristics of crops, reducing the burden of fertilizer losses and providing economical nutrient management. In this study, we use the total dry weight of field-cultivated red pepper and green onion grown in various growing environments to fit a nonlinear model-based crop growth model using different growth curves (logistic, Gompertz, Richards, and double logistic curve), and we propose a fertilizer distributed algorithm based on crop growth rate.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.4
• /
• pp.113-120
• /
• 2023

Smart farms are steadily increasing in research to minimize labor, energy, and quantity put into crops as IoT technology and artificial intelligence technology are combined. However, research on efficiently managing crop growth information in smart farms has been insufficient to date. In this paper, we propose a management technique that can efficiently monitor crop growth information by applying autonomous sensors to smart farms. The proposed technique focuses on collecting crop growth information through autonomous sensors and then recycling the growth information to crop cultivation. In particular, the proposed technique allocates crop growth information to one slot and then weights each crop to perform load balancing, minimizing interference between crop growth information. In addition, when processing crop growth information in four stages (sensing detection stage, sensing transmission stage, application processing stage, data management stage, etc.), the proposed technique computerizes important crop management points in real time, so an immediate warning system works outside of the management criteria. As a result of the performance evaluation, the accuracy of the autonomous sensor was improved by 22.9% on average compared to the existing technique, and the efficiency was improved by 16.4% on average compared to the existing technique.

• Magazine of the Korean Society of Agricultural Engineers
• /
• v.35 no.2
• /
• pp.73-80
• /
• 1993

A simplified physiologically-based dynamic model, SIMRIW was selected for predicting the growth and yield of rice. The applicability of the model to the rice cultivars and weather conditions in the Republic of Korea was evaluated. Parameters of the model were calibrated using actual rice yields in Suweon region and an optimization scheme, Constrained Rosenbrock Algorithm. The simulated results from the calibrated model were in good agreement with the field data. The model with parameters calibrated for Suweon was applied to other five regions for the evaluation of transferability, but the simulated results fell short of satisfaction. However, the model is found to be applied to real-time prediction of the growth and yield of rice crop, which is believed to be useful for timely rice crop management, agricultural policy making, and optimal irrigation water management.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.20 no.2
• /
• pp.214-227
• /
• 2018

An object oriented crop model was developed to perform crop growth simulation taking into account complex interaction between biotic and abiotic factors in an agricultural ecosystem. A set of classes including Atmosphere class, Plant class, Soil class, and Grower class were designed to represent weather, crop, soil, and crop management, respectively. Objects, which are instance of class, were linked to construct an integrated system for crop growth simulation. In a case study, yield of corn and soybean, which was obtained at an experiment farm in Rural Development Administration from 1984 to 1986, were compared with yield simulated using the integrated system. The integrated system had relatively low error rate of corn yield, e.g., <4%, under sole and intercropping conditions. In contrast, the system had a relatively large underestimation error for above ground biomass except for grain compared with those observed for corn and soybean. For example, estimates of biomass of corn leaf and stem was 31% lower than those of observed values. Although the integrated system consisted of simple models, the system was capable of simulating crop yield under an intercropping condition. This result suggested that an existing process-based model would be used to have more realistic simulation of crop growth once it is reengineered to be compatible to the integration system, which merits further studies for crop model improvement and implementation in object oriented paradigm.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.20 no.2
• /
• pp.228-232
• /
• 2018

Process-based crop models have been used to assess the impact of climate change on crop production. These models are implemented in procedural or object oriented computer programming languages including FORTRAN, C++, Delphi, Java, which have a stiff learning curve. The requirement for a high level of computer programming is one of barriers for efforts to develop and improve crop models based on biophysical process. In this study, we attempted to develop a Chinese cabbage model using Microsoft Excel with Visual Basic for Application (VBA), which would be easy enough for most agricultural scientists to develop a simple model for crop growth simulation. Results from Soil-Plant-Atmosphere-Research (SPAR) experiments under six temperature conditions were used to determine parameters of the Chinese cabbage model. During a plant growing season in SPAR chambers, numbers of leaves, leaf areas, growth rate of plants were measured six times. Leaf photosynthesis was also measured using LI-6400 Potable Photosynthesis System. Farquhar, von Caemmerer, and Berry (FvCB) model was used to simulate a leaf-level photosynthesis process. A sun/shade model was used to scale up to canopy-level photosynthesis. An Excel add-in, which is a small VBA program to assist crop modeling, was used to implement a Chinese cabbage model under the environment of Excel organizing all of equations into a single set of crop model. The model was able to simulate hourly changes in photosynthesis, growth rate, and other physiological variables using meteorological input data. Estimates and measurements of dry weight obtained from six SPAR chambers were linearly related ($R^2=0.985$). This result indicated that the Excel/VBA can be widely used for many crop scientists to develop crop models.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.22 no.1
• /
• pp.20-25
• /
• 2020

Spatial simulation of crop growth often requires application of management conditions to each cell. In particular, it is of great importance to determine the temperature conditions during the nursery period for rice seedlings, which would affect heading date projections. The objective of this study was to determine the value of TMPSB, which is the parameter of ORYZA2000 model to represent temperature increase under a plastic tunnel during the rice seedling periods. Candidate values of TMPSB including 0℃, 2℃, 5℃, 7℃ and 9℃ were used to simulate rice growth and yield. Planting dates were set from mid-April to mid-June. The simulations were performed at four sites including Cheorwon, Suwon, Seosan, and Gwangju where climate conditions at rice fields common in Korea can be represented. It was found that the TMPSB values of 0℃ and 2℃ resulted in a large variation of heading date due to low temperature occurred in mid-April. When the TMPSB value was >7℃, the variation of heading date was relatively small. Still, the TMPSB value of 5℃ resulted in the least variation of heading date for all the planting dates. Our results suggested that the TMPSB value of 5℃ would help reasonable assessment of climate change impact on rice production when high resolution gridded weather data are used as inputs to ORYZA2000 model over South Korea.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.20 no.4
• /
• pp.277-283
• /
• 2018

The crop growth model has been widely used for climate change impact assessment. Crop growth model require genetic coefficients for simulating growth and yield. In order to determine the genetic coefficients, regional growth monitoring data or yield trial data of crops has been used to calibrate crop growth model. The aim of this study is to verify that yield trial data of corn is appropriate to calibrate genetic coefficients of CERES-MAIZE. Field experiment sites were Suwon, Jinju, Daegu and Changwon. The distance from the weather station to the experimental field were from 1.3km to 27km. Genetic coefficients calibrated by yield trial data showed good performance in silking day. The genetic coefficients associated with silking are determined only by temperature. In CERES-MAIZE model, precipitation or irrigation does not have a significant effect on phenology related genetic coefficients. Although the effective distance of the temperature could vary depending on the terrain, reliable genetic coefficients were obtained in this study even when a weather observation site was within a maximum of 27 km. Therefore, it is possible to estimate the genetic coefficients by yield trial data in study area. However, the yield-related genetic coefficients did not show good results. These results were caused by simulating the water stress without accurate information on irrigation or rainfall. The yield trial reports have not had accurate information on irrigation timing and volume. In order to obtain significant precipitation data, the distance between experimental field and weather station should be closer to that of the temperature measurement. However, the experimental fields in this study was not close enough to the weather station. Therefore, When determining the genetic coefficients of regional corn yield trial data, it may be appropriate to calibrate only genetic coefficients related to phenology.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.22 no.3
• /
• pp.194-204
• /
• 2020

Cultivar parameters, which are key inputs to a crop growth model, have been estimated using observation data in good quality. Observation data with high quality often require considerable labor and cost, which makes it challenging to gather a large quantity of data for calibration of cultivar parameters. Alternatively, data in sufficient quantity can be collected from the reports on the evaluation of cultivars by region although these data are of questionable quality. The objective of our study was to assess the quality of crop and management data available from the reports on the regional adaptation trials for rice cultivars. We also aimed to propose the measures for improvement of the data quality, which would aid reliable estimation of cultivar parameters. DatasetRanker, which is the tool designed for quantitative assessment of the data for parameter calibration, was used to evaluate the quality of the data available from the regional adaptation trials. It was found that these data for rice cultivars were classified into the Silver class, which could be used for validation or calibration of key cultivar parameters. However, those regional adaptation trial data would fall short of the quality for model improvement. Additional information on management, e.g., harvest and irrigation management, can increase the quantitative quality by 10% with the minimum effort and cost. The quality of the data can also be improved through measurements of initial conditions for crop growth simulations such as soil moisture and nutrients. In addition, crop model improvement can be facilitated using crop growth data in time series, which merits further studies on development of approaches for non-destructive methods to monitor the crop growth.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
• /
• 2003.09a
• /
• pp.39-41
• /
• 2003

The crop growth simulation model could be adopted to evaluate the impact not only of the long term climate change such as atmosphere $CO_2$ concentration rising and global warming but also of the predicted short term weather variability on the national crop production. There are several growth simulation models for predicting rice crop performance such as ORYZA1, CERES-Rice, Rice Clock Model, and SIMRIW.(omitted)