• 응용통계연구
• /
• 제13권2호
• /
• pp.329-341
• /
• 2000

한국의 농업에서 쌀 생산량조사는 매우 중요한 조사로 알려져 있다. 특히 쌀 예상 생산량조사는 농업정책결정과 관련하여 유용한 기초자료를 제공한다는 점에서 가능한 한 정확한 예측을 필요로 한다. 본 논문에서는 통계적 모형을 이용한 쌀 예상 추정방법을 제안하고 기존의 주관적 추정방법인 달관추정과 비교함으로써 쌀 예상 생산량 추정과정 통계적방법의 응용 가능성과 타당성을 제시하고 있다.

• 한국작물학회:학술대회논문집
• /
• 한국작물학회 2017년도 9th Asian Crop Science Association conference
• /
• pp.46-46
• /
• 2017

We developed a rice simulation model for remote-sensing (SIMRIW-RS, Homma et al., 2007) to evaluate rice production and management on a regional scale. Here, we reports its application trial to estimate rice production in farmers' fields in Sendai, Japan. The remote-sensing data for the application was periodically obtained by multispectral camera (RGB + NIR and RedEdge) attached with unmanned air vehicle (UAV). The airborne images was 8 cm in resolution which was attained by the flight at an altitude of 115 m. The remote-sensing data was relatively corresponded with leaf area index (LAI) of rice and its spatial and temporal variation, although the correspondences had some errors due to locational inaccuracy. Calibration of the simulation model depended on the first two remote-sensing data (obtained around one month after transplanting and panicle initiation) well predicted rice growth evaluated by the third remote-sensing data. The parameters obtained through the calibration may reflect soil fertility, and will be utilized for nutritional management. Although estimation accuracy has still needed to be improved, the rice yield was also well estimated. These results recommended further data accumulation and more accurate locational identification to improve the estimation accuracy.

• 한국작물학회:학술대회논문집
• /
• 한국작물학회 2022년도 추계학술대회
• /
• pp.80-80
• /
• 2022

Estimation of heading date of paddy rice is laborious and time consuming. Therefore, automatic estimation of heading date of paddy rice is highly essential. In this experiment, deep learning classification models were used to classify two difference categories of rice (vegetative and reproductive stage) based on the panicle initiation of paddy field. Specifically, the dataset includes 444 slanted view images belonging to two categories and was then expanded to include 1,497 images via IMGAUG data augmentation technique. We adopt two transfer learning strategies: (First, used transferring model weights already trained on ImageNet to six classification network models: VGGNet, ResNet, DenseNet, InceptionV3, Xception and MobileNet, Second, fine-tuned some layers of the network according to our dataset). After training the CNN model, we used several evaluation metrics commonly used for classification tasks, including Accuracy, Precision, Recall, and F1-score. In addition, GradCAM was used to generate visual explanations for each image patch. Experimental results showed that the InceptionV3 is the best performing model in terms of the accuracy, average recall, precision, and F1-score. The fine-tuned InceptionV3 model achieved an overall classification accuracy of 0.95 with a high F1-score of 0.95. Our CNN model also represented the change of rice heading date under different date of transplanting. This study demonstrated that image based deep learning model can reliably be used as an automatic monitoring system to detect the heading date of rice crops using CCTV camera.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2008년도 학술발표회 논문집
• /
• pp.805-809
• /
• 2008

Measurement of leaf area index (LAI) is useful for understanding rice growth, water use, and canopy light interception. The top nitrogen content(TNC) per unit area is an important quantitative index of the condition of nitrogen nutrition in rice production. The rapid and simple method of estimation of TNC, with the use of the existing nondestructive analyzing instruments chlorophyll meter SPAD-502 and plant canopy analyzer (PCA) LAI-2000, was scrutinized. Destructive measurement is time consuming and labor intensive. Our objective was to evaluate sampling procedures using the Li-Cor LI-1800, LAI 2000 plant canopy analyzer (PCA) for nondestructive estimation of rice LAI, and SPAD-502 on the Northern Plains of Cheongju. The LAI estimated by PCA tended to underestimate the LAI determined by actual measurement by about 20%. The estimation of LAI by PCA was judged to have a sufficient accuracy as a practical technique. A high positive correlation was obtained between the values of the SPAD reading and LAI. NDVI and LAI also showed a very high correlation. The values of the SPAD reading and LAI, and NDVI gave a high positive correlation. These results indicated that the method described in this study was effective as a simple and rapid method for the estimation of rice growth.

• 한국작물학회:학술대회논문집
• /
• 한국작물학회 2022년도 추계학술대회
• /
• pp.81-81
• /
• 2022

Recently, many studies on big data based smart farming have been conducted. Research to quantify morphological characteristics using image data from various crops in smart farming is underway. Rice is one of the most important food crops in the world. Much research has been done to predict and model rice crop yield production. The number of productive tillers per plant is one of the important agronomic traits associated with the grain yield of rice crop. However, modeling the basic growth characteristics of rice requires accurate data measurements. The existing method of measurement by humans is not only labor intensive but also prone to human error. Therefore, conversion to digital data is necessary to obtain accurate and phenotyping quickly. In this study, we present an image-based method to predict leaf number and evaluate tiller number of individual rice crop using YOLOv5 deep learning network. We performed using various network of the YOLOv5 model and compared them to determine higher prediction accuracy. We ako performed data augmentation, a method we use to complement small datasets. Based on the number of leaves and tiller actually measured in rice crop, the number of leaves predicted by the model from the image data and the existing regression equation were used to evaluate the number of tillers using the image data.

• 한국산학기술학회논문지
• /
• 제14권11호
• /
• pp.5417-5422
• /
• 2013

본 논문은 MODIS 위성 영상을 이용하여 논벼 생산량을 추정하는 모형의 적합도 개선 및 추정모형내 적절한 설명변수를 탐색하고자 수행되었다. 또한 이 연구는 한국에서 논벼 생산량 조사를 위해 위성 영상을 사용하는 방안을 검토하기 위해 수행되었다. 미국, 호주, 일본 등 많은 선진국들은 재배면적 및 생산량 조사와 같은 농업통계를 산출하기 위해 위성 영상을 이용하고 있다. 그러나 위성 영상을 이용한 작물 생산량 조사의 정확성은 아직 충분치 않은 수준이다. 본 연구는 위성 영상을 이용한 논벼 생산량 조사의 정확도를 증대시키기 위한 몇 가지 방법을 검토하고 있다. 많은 작물 중 논벼를 연구대상으로 선정한 이유는 논벼가 다른 작물 보다 재배면적과 작황의 영상 분석이 용이하였기 때문이고, 다양한 위성 영상 중 MODIS 영상을 이용한 것은 한국 논벼 생산량 조사 연구를 위해 보다 적절한 영상을 다수 포함하고 있었기 때문이다. 이 연구에서 등온선에 의해 구분된 논벼로부터 도출된 NDVI지수, 논벼 등숙기의 일조시간, 강우량, 온도 등 기상변수를 이용하여 단수함수가 추정되었다. 단수함수 추정결과, 모형의 적합도(R-squared)는 0.768-0.891를 보였다. 이 연구는 연평균 등온선에 의해 구분된 NDVI지수와 (등숙기) 기상변수가 단수함수 추정에 매우 유용하게 이용될 수 있음을 보이고 있다.

• 농업과학연구
• /
• 제32권1호
• /
• pp.81-94
• /
• 2005

This study is aimed at giving the basic information for individual farm households to make decisions for optimizing their farm sizes and for the government to implement farm size optimization policies through the identification of combinations among rice production factors in plain areas like Cheolwon district and the suggestion of the optimal farm sizes of individual farmers based on the scale of economy calculated. The data of agricultural production costs of 50 rice farmers in the plain area which is located in Dongsong-eup Cholwon district, Kangwon province were used in the analysis. The 'translog' cost function among various methods which is a flexible function type was adopted to calculate the scale of economy in rice production. Seemingly unrelated regression(SUR) method was used in forecasting functions and processing other statistics by SHAZAM which is one of the computer aid program for quantitative econometric analysis. In conclusion, the long-run average cost(LAC) curve showed 'U-shape' which was different from 'L-type' one which was shown in the previous studies by others. The lowest point of the LAC was 9.764ha and the concerned production cost amounted to 633 Won/kg. Based on these results, it have to be suggested that around 10 ha of paddy is the target size for policy assistances to save costs under the present level of farming practices and technology. The above results show that the rice production costs could be saved up to 10ha in Cheolwon plain area which is a typical paddy field. However, land use, land condition, land ownership and manager's ability which may affect scale of economy should be considered. Furthermore, reasonable management will have to be realized by means of labor saving technology and cost saving management skill like enlargement of farm size of rice.

• 한국농업기계학회:학술대회논문집
• /
• 한국농업기계학회 1996년도 International Conference on Agricultural Machinery Engineering Proceedings
• /
• pp.987-995
• /
• 1996

The time components of the operations were investigated on the rice paddy field operations by radio controlled helicopter. The net time of operations, seeding and application, were less than a quarter of the required total time in the field of 1 ha. The most interesting is how to decrease the time of hop-off and landing. In this paper the seeding density itself is taken under a new look and its describing method is discussed. Voronoi diagram was introduced to consider individual plant of rice paddy. Extremely wide ranges of the distribution of seeding density are not supposed by the common indexes based on the concept of mean values and discussed on the aggregate of plants.

• 대한원격탐사학회지
• /
• 제33권5_2호
• /
• pp.733-740
• /
• 2017

본 총설에서는 작황조사 시험을 활용한 통계적 작황예측 방법에 대해 소개하고 이를 원격탐사를 이용한 방법과 비교하였다. 17개 지역에서 이루어지는 작황조사시험 기반으로 작황조사시험의 수량구성요소 중 등숙률을 일사량과 선형회귀식으로 예측하고 면적당 영화수는 작황조사의 실측값을 활용하여 수량을 재구성하는 방법으로 예측 결과를 얻어진다. 예측 결과는 비교적 정확하였는데 지난 2010년부터 2016년까지 가장 적은 오차는 1 kg/10a였으며 가장 큰 편차는 19 kg/10a 이었다. 크게 편차가 발생한 이유는 태풍에 의해 피해 때문이었다. 즉 작황조사를 이용한 통계적 방법은 재해에 의한 공간변이를 충분히 반영하지 못하는 약점이 있다. 반면 원격탐사는 이러한 재해에 의한 공간적 변이를 보다 잘 설명할 수 있는 장점이 있다. 따라서, 벼의 생육상황에 큰 문제가 없는 경우에는 두가지 접근법 모두 유효하고 재해가 발생하였을 때는 원격탐사가 더 정확할 수 있을 것으로 보인다.

• 대한원격탐사학회지
• /
• 제37권3호
• /
• pp.503-514
• /
• 2021

쌀 수급 조절 정책의 합리적 수립을 지원하기 위해서는 벼 재배면적의 조기 추정이 필요하다. 본 연구는 국내 벼 주산지인 김제시를 대상으로 Sentinel-1 위성영상을 활용하여 이앙이 마무리되는 7월 초순 벼 재배면적을 조기에 추정하기 위해 최적의 훈련자료 수집을 위한 무인기(UAV) 영상 활용 방안을 제시하고자 수행하였다. 5월부터 7월 초까지 수집한 Sentinel-1 위성영상은 ESA에서 제공하는 SNAP(SeNtinel application platform, Version 8.0)프로그램으로 전처리하고 팜맵을 활용하여 농경지만을 추출하였다. 벼 재배지 중심 지역과 벼·콩 혼재지 무인기 영상 촬영 영역을 혼합하여 훈련자료로 선정하여 김제시 전체 벼 재배지를 추정한 결과, 정확도와 카파 계수는 각각 89.9%, 0.774로 가장 좋은 결과를 보였는데, 이는 김제시 전역을 대상으로 무작위 표본조사를 수행하여 분류한 결과와 비교 시 전체 정확도 1% 내외, 카파 계수 0.02~0.04 범위에서 차이를 보여 벼 재배지 조기 추정을 위한 무인기 영상 활용 가능성을 확인할 수 있었다.