• Journal of Biosystems Engineering
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• 제31권1호
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• pp.24-32
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• 2006

Compaction is becoming a greater concern in crop production and the environment because it can have deleterious effects on growing conditions that are difficult to remediate. Because compaction can vary considerably from point to point within a field, and also from depth to depth within the soil profile, it is important to consider quantification and management of the spatial and vertical variability in soil compaction when developing an overall site-specific crop management plan. In this paper, the importance of soil compaction, techniques for quantification of its variability, and the concept of site-specific tillage are examined. Methods and systems to detect within-field variation in soil strength as a surrogate measure of soil compaction and related soil properties are also compared and discussed. Quantification of variability in soil compaction and site-specific compaction management was motivated recently, and sensors and control systems are still under development. Future study will need to address a number of issues related to understanding and applying the sensor measurements.

• Agricultural and Biosystems Engineering
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• 제7권1호
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• pp.18-26
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• 2006

In-field site-specific nitrogen (N) management increases crop yield, reduces N application to minimize the risk of nitrate contamination of ground water, and thus reduces farming cost. Real-time N sensing and fertilization is required for efficient N management. An 'on-the-go' site-specific N management system was developed and evaluated for the supplemental N application to com (Zea mays L.). This real-time N sensing and fertilization system monitored and assessed N fertilization needs using a vision-based spectral sensor and controlled the appropriate variable N rate according to N deficiency level estimated from spectral signature of crop canopies. Sensor inputs included ambient illumination, camera parameters, and image histogram of three spectral regions (red, green, and near-infrared). The real-time sensor-based supplemental N treatment improved crop N status and increased yield over most plots. The largest yield increase was achieved in plots with low initial N treatment combined with supplemental variable-rate application. Yield data for plots where N was applied the latest in the season resulted in a reduced impact on supplemental N. For plots with no supplemental N application, yield increased gradually with initial N treatment, but any N application more than 101 kg/ha had minimal impact on yield.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2004년도 추계학술발표회
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• pp.84-85
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• 2004

• 한국토양비료학회지
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• 제34권1호
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• pp.42-54
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• 2001

질소비료는 작물의 생장에 있어서 가장 핵심적인 영양소라 할 수 있으며, 질소의 효율적 이용은 안정적인 수확량의 확보 뿐만 아니라 비용절감 및 환경오염원 감축 등 다양한 편익을 제공할 수 있을 것이다. 정밀농업은 토양의 특성에 따라 작물에 필요한 적정량의 시비로 영양분의 이용효율을 높임으로써 환경 경제적 편익을 동시에 추구하는 농법이라 할 수 있다. 본 연구는 GIS를 이용하여 구축한 455개 표본농경지의 토양 및 지형에 관한 Database에 근거하여 각 농경지내에서 토양을 세분하여 적정 시비를 할 경우(site-specific management)와 농경지별 적정 시비를 하였을 경우(uniform rate application) 비료의 사용량, 수확량, 수질오염 정도 등을 EPIC모델을 이용하여 비교분석 하였다. 전체 농경지에서의 가중평균적인 환경 경제적 효과는 큰 차이를 보이지 않지만 각각의 농경지가 다양한 토양으로 구성되어 있고 다양한 토양에 대한 최적의 질소량이 큰 차이를 나타낼 때는 정밀농업의 실천이 경제적 및 환경적 측면에서 충분한 잠재력이 있는 것으로 나타났다.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2000년도 THE THIRD INTERNATIONAL CONFERENCE ON AGRICULTURAL MACHINERY ENGINEERING. V.III
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• pp.764-769
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• 2000

A new transplant production system that produces high quality plug seedlings of specific crop has been studied. It is a plant factory designed to produce massive amount of virus free seedlings. The design concept for building this plant factory is to realize maximum energy efficiency and minimum initial investment and running cost. The basic production strategy is the sitespecific management. In this case, the management of the growth of individual plantlet is considered. This requires highly automated and information intensive production system in a closed aseptic environment the sterilized specific crops. One of the key components of this sophisticated system is the irrigation system. The conditions that this irrigation system has to satisfy are: 1. to perform the site specific crop management in irrigation and 2. to meet the no waste standard. The objective of this study is to develop an irrigation scheduling that can implement the no waste standard.

• 한국작물학회지
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• 제50권4호
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• pp.238-246
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• 2005

Rice yield and protein content have been shown to be highly variable across paddy fields. In order to characterize this spatial variability of rice within a field, two-year experiments were conducted in 2002 and 2003 in a large-scale rice field of $6,600m^2$ In year 2004, an experiment was conducted to know if variable rate treatment (VRT) of N fertilizer, that was prescribed for site-specific management at panicle initiation stage, could reduce spatial variation in yield and protein content of rice while increasing yield compared to conventional uniform N topdressing (UN, 33kg N/ha at PIS) method. VRT nitrogen prescription for each grid was calculated based on the nitrogen (N) uptake (from panicle initiation to harvest) required for target rice protein content of $6.8\%$, natural soil N supply, and recovery of top-dressed N fertilizer. The required N uptake for target rice protein content was calculated from the equations to predict rice yield and protein content from plant growth parameters at panicle initiation stage (PIS) and N uptake from PIS to harvest. This model· equations were developed from the data obtained from the previous two-year experiments. The plant growth parameters for the calculation of the required N were predicted non-destructively by canopy reflectance measurement. Soil N supply for each grid was obtained from the experiment of year 2003, and N recovery was assumed to be $60\%$ according to the previous reports. The prescribed VRT N ranged from 0 to 110kg N/ha with an average of 57kg/ha that was higher than 33 kg/ha of UN. The results showed that VRT application successfully worked not only to reduce spatial variability of rice yield and protein content but also to increase rough rice yield by 960kg/ha. The coefficient of variation (CV) for rice yield and protein content was reduced significantly to $8.1\%$ and $7.1\%$ in VRT from $14.6\%$ and $13.0\%$ in UN, respectively. And also the average protein content of milled rice in VRT showed very similar value of target protein content of $6.8\%$. In conclusion the procedure used in this paper was believed to be reliable and promising method for reducing within-field spatial variability of rice yield and protein content. However, inexpensive, reliable, and fast estimation methods of natural N supply and plant growth and nutrition status should be prepared before this method could be practically used for site-specific crop management in large-scale rice field.

• Journal of Biosystems Engineering
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• 제35권2호
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• pp.132-137
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• 2010

Site-specific N application for corn is one of the precision crop management. To implement the site-specific N application, various nitrogen stress sensing methods, including aerial image, tissue analysis, soil sampling analysis, and SPAD meter readings, have been used. Use of side-dressing, an efficient nitrogen application method than a uniform application in either late fall or early spring, relies mainly on the capability of nitrogen deficiency detection. This paper presents map-based variable rate nitrogen application based using a multi-spectral corn nitrogen deficiency(CND) sensor. This sensor assess the nitrogen stress by means of the estimated SPAD reading calculated from the corn leave reflectance. The estimated SPAD value from the CND sensor system and location information form DGPS of each field block was combined into the field map using a ArcView program. Then this map was converted into a raster file for a map-based variable rate application software. The relative SPAD (RSPAD = SPAD over reference SPAD) was investigated 2 weeks after the treatments. The results showed that the map-based variable rate application system was feasible.

• 대한원격탐사학회지
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• 제40권1호
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• pp.57-69
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• 2024

Crop classification plays a vitalrole in monitoring agricultural landscapes and enhancing food production. In this study, we explore the effectiveness of Long Short-Term Memory (LSTM) models for crop classification, focusing on distinguishing between apple and rice crops. The aim wasto overcome the challenges associatedwith finding phenology-based classification thresholds by utilizing LSTM to capture the entire Normalized Difference Vegetation Index (NDVI)trend. Our methodology involvestraining the LSTM model using a reference site and applying it to three separate three test sites. Firstly, we generated 25 NDVI imagesfrom the Sentinel-2A data. Aftersegmenting study areas, we calculated the mean NDVI values for each segment. For the reference area, employed a training approach utilizing the NDVI trend line. This trend line served as the basis for training our crop classification model. Following the training phase, we applied the trained model to three separate test sites. The results demonstrated a high overall accuracy of 0.92 and a kappa coefficient of 0.85 for the reference site. The overall accuracies for the test sites were also favorable, ranging from 0.88 to 0.92, indicating successful classification outcomes. We also found that certain phenological metrics can be less effective in crop classification therefore limitations of relying solely on phenological map thresholds and emphasizes the challenges in detecting phenology in real-time, particularly in the early stages of crops. Our study demonstrates the potential of LSTM models in crop classification tasks, showcasing their ability to capture temporal dependencies and analyze timeseriesremote sensing data.While limitations exist in capturing specific phenological events, the integration of alternative approaches holds promise for enhancing classification accuracy. By leveraging advanced techniques and considering the specific challenges of agricultural landscapes, we can continue to refine crop classification models and support agricultural management practices.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2005년도 국제학술회의
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• pp.22-46
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• 2005

Spatial and timely information on crop and filed conditions is one of the most important basics for rational and efficient planning and management in agriculture. Remote sensing, GIS, and modeling are powerful tools for such applications. This paper presents an overview of the state of the art in remote sensing of crop and field conditions with some case studies. It is also shown that a synergistic linkage between process-based models and remote sensing signatures enables us to estimate the multiple crop/ecosystem variables at a dynamic mode. Remotely sensed information can greatly reduce the uncertainty of simulation models by compensating for insufficient availability of data or parameters. This synergistic approach allows the effective use of infrequent and multi-source remote sensing data for estimating important ecosystem variables such as biomass growth and ecosystem $CO_2$ flux. This paper also shows a geo-spatial information system that enables us to integrate, search, extract, process, transform, and calculate any part of the data based on ID#, attributes, and/or by river-basin boundary, administrative boundary, or boundaries of arbitrary shape/size all over Japan. A case study using the system demonstrates that the nitrogen load from fertilizer was closely related to nitrate concentration of groundwater. The combined use of remote sensing, GIS and modeling would have great potential for various agro-ecosystem applications.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.274-276
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• 2003

The traditional crop productivity simulations based on crop models are normally site-specific. To simulate regional crop productivity, the spatial crop model is developed in this study by integrating Geographical Information System (GIS) with Erosion Productivity Impact Calculator (EPIC) model. The integration applied a loose coupling approach. Data are exchanged using ASCII or binary data format between GIS and EPIC model without a common user interface. The spatial EPIC model is conducted to simulate the average corn and wheat productivity of 1980s in North China. The results show that the simulation accuracy of the spatial EPIC model is acceptable. The simulation accuracy can be improved by using the detailed crop management information, such as irrigation, fertilizer and tillage schedule.