• Korean Journal of Soil Science and Fertilizer
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• v.50 no.6
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• pp.520-529
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• 2017

In Korea, the largest agricultural lands are paddy fields which have poor infiltration and drainage properties. Recently, Korean government pursuits cultivating upland crops in paddy fields to reduce overproduced rice in Korea. In order to succeed this policy, it is critical to set criteria suitability classification for upland crops cultivating in paddy field soils. The objective of this study was developing guideline of suitability classification for sesame cultivation in paddy field soils. Yields of sesame cultivated in paddy field soils and soil properties were investigated at 40 locations at nationwide scale. Soil properties such as topography, soil texture, soil moisture contents, slope, and drainage level were investigated. The guideline of suitability classification for sesame was determined by multi-regression method. As a result, sesame yields had the greatest correlation with topography, soil moisture content, and slope. Since sesame is sensitive to excessive soil moisture content, paddy fields with well drained, slope of 7-15% and mountain foot or hill were best suit for cultivating sesame. Sesame yields were greater with less soil moisture contents. Based on these results, area of best suitable paddy field land for sesame was 161,400 ha, suitable land was 62,600 ha, possible land was 331,600 ha, and low productive land was 1,075,500 ha. Compared to existing suitability classification, the new guideline of classification recommended smaller area of best or suitable areas to cultivate sesame. This result may suggest that sesame cultivation in paddy field can be very susceptible to soil moisture contents.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.403-413
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• 2022

Classification and analysis are improved factors for the realtime automation system. In the field of agriculture, the cultivation of different paddy crop depends on the atmosphere and the soil nature. We need to analyze the moisture level in the area to predict the type of paddy that can be cultivated. For this process, Ensemble Modulation Pattern system and Block Probability Neural Network based classification models are used to analyze the moisture and temperature of land area. The dataset consists of the collections of moisture and temperature at various data samples for a land. The Ensemble Modulation Pattern based feature analysis method, the extract of the moisture and temperature in various day patterns are analyzed and framed as the pattern for given dataset. Then from that, an improved neural network architecture based on the block probability analysis are used to classify the data pattern to predict the class of paddy crop according to the features of dataset. From that classification result, the measurement of data represents the type of paddy according to the weather condition and other features. This type of classification model assists where to plant the crop and also prevents the damage to crop due to the excess of water or excess of temperature. The result analysis presents the comparison result of proposed work with the other state-of-art methods of data classification.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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• pp.51-63
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• 2022

Up-to-date statistics of crop cultivation status is essential for farm land management planning and the advancement in remote sensing technology allows for rapid update of farming information. The objective of this study was to develop a classification model of rice paddy or winter crop fields based on NDWI, NDVI, and HSV indices using Sentinel-2 satellite images. The 18 locations in central Korea were selected as target areas and photographed once for each during summer and winter with a eBee drone to identify ground truth crop cultivation. The NDWI was used to classify summer paddy fields, while the NDVI and HSV were used and compared in identification of winter crop cultivation areas. The summer paddy field classification with the criteria of -0.195

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.80-80
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• 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.

• Korean Journal of Soil Science and Fertilizer
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• v.48 no.5
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• pp.351-360
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• 2015

Human influence on soil formation has dramatically increased with human civilization and industry development. Increase of anthropogenic soils induced researches on the anthropogenic soils; classification, chemical and physical characteristics of anthropogenic soils and plant growth from anthropogenic soils. However there have been no comprehensive analyses on soil pore or physical properties of anthropogenic soils from 3 dimensional images in Korea. The objectives of this study were to characterize physical properties of anthropogenic paddy field soils by depth and to find differences between natural and anthropogenic paddy field soils. Soil samples were taken from two anthropogenic and natural paddy field soils; anthropogenic (A_c) and natural (N_c) paddy soils with topsoil of coarse texture and anthropogenic (A_f) and natural (N_f) paddy soils with topsoil of fine texture. The anthropogenic paddy fields were reestablished during the Arable Land Remodeling Project from 2011 to 2012 and continued rice farming after the project. Natural paddy fields had no artificial changes or disturbance in soil layers up to 1m depth. Samples were taken at three different depths and analyzed for routine physical properties (texture, bulk density, etc.) and pore properties with computer tomography (CT) scans. The CT scan provided 3 dimensional images at resolution of 0.01 mm to calculate pore radius size, length, and tortuosity of soil pores. Fractal and configuration entropy analyses were applied to quantify pore structure and analyze spatial distribution of pores within soil images. The results of measured physical properties showed no clear trend or significant differences across depths or sites from all samples, except the properties from topsoils. The results of pore morphology and spatial distribution analyses provided detailed information of pores affected by human influences. Pore length and size showed significant decrease in anthropogenic soils. Especially, pores of A_c had great decrease in length compared to N_c. Fractal and entropy analyses showed clear changes of pore distributions across sites. The topsoil layer of A_c showed more degradation of pore structure than that of N_c, while pores of A_f topsoil did not show significant degradation compared with those of N_f. These results concluded that anthropogenic soils with coarse texture may have more effects on pore properties than ones with fine texture. The reestablished paddy fields may need more fundamental remediation to improve physical conditions.

• Korean Journal of Remote Sensing
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• v.32 no.3
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• pp.235-244
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• 2016

In large-area crop classification with MODIS data, a mixed pixel problem caused by the low resolution of MODIS data has been one of main issues. To mitigate this problem, this paper proposes a hierarchical classification algorithm that selectively classifies the specific crop class of interest by using their spectral characteristics. This selective classification algorithm can reduce mixed pixel effects between crops and improve classification performance. The methodological developments are illustrated via a case study in Jilin city, China with MODIS Normalized Difference Vegetation Index (NDVI) and Near InfRared (NIR) reflectance datasets. First, paddy fields were extracted from unsupervised classification of NIR reflectance. Non-paddy areas were then classified into corn and bean using time-series NDVI datasets. In the case study result, the proposed classification algorithm showed the best classification performance by selectively classifying crops having similar spectral characteristics, compared with traditional direct supervised classification of time-series NDVI and NIR datasets. Thus, it is expected that the proposed selective hierarchical classification algorithm would be effectively used for producing reliable crop maps.

• Korean Journal of Agricultural Science
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• v.44 no.3
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• pp.325-331
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• 2017

This study was conducted to investigate the dominance and distribution of weed species in organic and conventional paddy fields. The organic paddy fields were maintained for organic farming for more than five years in Anseong, Gyeonggi province of Korea. According to the Braun-Blanquet method, 42 and 36 weed species were found in the organic paddy fields in 2015 and 2016, respectively, while 38 and 36 weed species were found in the conventional paddy fields in 2015 and 2016, respectively. As a result of two years' survey, 53 species from 24 families in the organic paddy fields were identified and classified as 32 annuals, 3 biennials and 18 perennials. In conventional paddy fields, 51 species from 24 families (30 annuals, 6 biennials, and 15 perennials) were identified. According to the classification by family, the most abundant weed species were Compositae (9 species), followed by Poaceae (8 species) and Polygonaceae (6 species) in organic paddy fields. In conventional paddy fields, Compositae (9 species) were the most abundant weed species, followed by Cruciferae (6 species), Poaceae, Polygonaceae, and Cyperaceae. This result indicates that the difference in diversity of weeds in paddy fields was influenced more by the agricultural environment than the type of cultivation. Our results could be used as a base data to control the occurrence of weed species in the paddy fields.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.337-345
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• 2023

Estimating the rice heading date is one of the most crucial agricultural tasks related to productivity. However, due to abnormal climates around the world, it is becoming increasingly challenging to estimate the rice heading date. Therefore, a more objective classification method for estimating the rice heading date is needed than the existing methods. This study, we aimed to classify the rice heading stage from various images using a CNN classification model. We collected top-view images taken from a drone and a phenotyping tower, as well as slanted-view images captured with a RGB camera. The collected images underwent preprocessing to prepare them as input data for the CNN model. The CNN architectures employed were ResNet50, InceptionV3, and VGG19, which are commonly used in image classification models. The accuracy of the models all showed an accuracy of 0.98 or higher regardless of each architecture and type of image. We also used Grad-CAM to visually check which features of the image the model looked at and classified. Then verified our model accurately measure the rice heading date in paddy fields. The rice heading date was estimated to be approximately one day apart on average in the four paddy fields. This method suggests that the water head can be estimated automatically and quantitatively when estimating the rice heading date from various paddy field monitoring images.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.3
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• pp.21-28
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• 2017

The main goal of this paper is to assess application of UAV (Unmanned Aerial Vehicle) remote sensing and GIS based images in detection and measuring of rice field drought area in South Korea. Drought is recurring feature of the climatic events, which often hit South Korea, bringing significant water shortages, local economic losses and adverse social consequences. This paper describes the assesment of the near-realtime drought damage monitoring and reporting system for the agricultural drought region. The system is being developed using drought-related vegetation characteristics, which are derived from UAV remote sensing data. The study area is $3.07km^2$ of Wonbuk-myeon, Taean-gun, Chungnam in South Korea. UAV images were acquired three times from July 4 to October 29, 2015. Three images of the same test site have been analysed by object-based image classification technique. Drought damaged paddy rices reached $754,362m^2$, which is 47.1 %. The NongHyeop Agricultural Damage Insurance accepted agricultural land of 4.6 % ($34,932m^2$). For paddy rices by UAV investigation, the drought monitoring and crop productivity was effective in improving drought assessment method.

• Journal of Biosystems Engineering
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• v.39 no.2
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• pp.96-100
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• 2014

Purpose: The objective of this study was to develop a classification index and the grade levels for a five-grade energy efficiency classification of agricultural dryers in Korea. Methods: The classification index and the grade levels were determined by using the performance test data published by the FACT over the last eight years to reflect a state of the art technology for agricultural dryers in Korea. The five grades were designed to have the classified dryers distributed normally over the grades with 15% for the $1^{st}$ grade, 20% for the $2^{nd}$ grade, 30% for the $3^{rd}$ grade, 20% for the $4^{th}$ grade and 15% for the $5^{th}$ grade. Results: The classification index was defined as the total amount of fuel and electrical energy consumed per 1% of the wet basis moisture content evaporated from a unit mass of grain or agricultural crops during the drying process: 1 MT of paddy rice for grain dryers and 1 kg of red pepper for agricultural crop dryers as the standard mass. Conclusions: The grade levels for the five-grade energy efficiency classification of grain dryers, kerosene dryers, and electric dryers were proposed in terms of the classification index value.