• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.637-647
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• 2019

The detection of crop stress is an important issue for the accurate assessment of yield decline. The photochemical reflectance index (PRI) was developed as a remotely sensed indicator of light use efficiency (LUE). The PRI has been tested in crop stress detection and a number of studies demonstrated the feasibility of using it. However, only few studies have focused on the use of PRI from remote sensing imagery. The monitoring of PRI using drone and satellite is made difficult by the low spectral resolution image captures. In order to estimate PRI from multispectral sensor, we propose a band fusion method using adjacent bands. The method is applied to the drone-based hyperspectral and multispectral imagery and estimated PRI explain 79% of the original PRI. And time series analyses showed that two PRI data (drone-based and SRS sensor) had very similar temporal variations. From these results, PRI from multispectral imagery using band fusion can be used as a new method for evaluation of crop stress.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1225-1234
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• 2023

The intensity of crop water stress caused by moisture deficit is affected by growth and heat conditions. For more accurate detection of crop water stress state using remote sensing techniques, it is necessary to select vegetation indices sensitive to crop response and to understand their changes considering not only soil moisture deficit but also heat conditions. In this study, we measured the MERIS terrestrial chlorophyll index (MTCI) and chlorophyll/carotenoid index (CCI) under drought and heat wave conditions. The MTCI, sensitive to chlorophyll concentration, sensitively decreased on non-irrigation conditions and the degree was larger with heat waves. On the other hand, the CCI, correlated with photosynthesis efficiency, showed less sensitivity to water deficit but had decreased significantly with heat waves. After re-irrigation, the MTCI was increased than before damage and CCI became more sensitive to heat stress. These results are expected to contribute to evaluating the intensity of crop water stress through remote sensing techniques.

• The Plant Pathology Journal
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• v.35 no.6
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• pp.654-661
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• 2019

The soybean cyst nematode, Heterodera glycines, is a major plant-parasitic nematode that has caused important economic losses to Korea's soybean production. Four species of cyst nematodes, H. schachtii, H. glycines, H. trifolii, and H. sojae, all belong to schachtii group are coexist in field soil in Korea. The rapid identification of the nematode is crucial for preventing crop damage and in decision making for controlling this nematode. This study aimed to develop a species-specific primer set for quantitative PCR (qPCR) assay of H. glycines. The specific primer set (HGF1 and HGR1) for H. glycines was designed based on the cytochrome c oxidase subunit I (COI) sequence of mitochondrial DNA. After optimization, it is possible to identify the H. glycines using a qPCR assay with DNA extracted from a single cyst and single second-stage juvenile (J2). The specificity was confirmed by the absence of SYBR fluorescent signals of three other Heterodera species. A serial dilution of DNA extracted from a single cyst was obtained for the sensitivity test. The result showed that the standard curve of the test had a highly significant linearity between DNA concentration and Ct value (R² = 0.996, slope = -3.49) and that the detection limit concentration of DNA of the primer set was 10 pg of DNA per reaction. Our findings suggested that H. glycines could be distinguished from H. sojae and other Heterodera species when a qPCR assay is used with a specific primer set.

• Korean Journal of Soil Science and Fertilizer
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• v.36 no.2
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• pp.92-103
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• 2003

Research and technological advances in the field of remote sensing have greatly enhanced the ability to detect and quantify physical and biological stresses that affect the productivity of agricultural crops. Reflectance in specific visible and near-infrared regions of the electromagnetic spectrum have proved useful in detection of nutrient deficiencies. Especially crop canopy sensors as a ground remote sensing measure the amount of light reflected from nearby surfaces such as leaf tissue or soil and is in contrast to aircraft or satellite platforms that generate photographs or various types of digital images. Multi-spectral vegetation indices derived from crop canopy reflectance in relatively wide wave band can be used to monitor the growth response of plants in relation to environmental factors. The normalized difference vegetation index (NDVI), where NDVI = (NIR-Red)/(NIR+Red), was originally proposed as a means of estimating green biomass. The basis of this relationship is the strong absorption (low reflectance) of red light by chlorophyll and low absorption (high reflectance and transmittance) in the near infrared (NIR) by green leaves. Thereafter many researchers have proposed the other indices for assessing crop vegetation due to confounding soil background effects in the measurement. The green normalized difference vegetation index (GNDVI), where the green band is substituted for the red band in the NDVI equation, was proved to be more useful for assessing canopy variation in green crop biomass related to nitrogen fertility in soils. Consequently ground remote sensing as a non destructive real-time assessment of nitrogen status in plant was thought to be useful tool for site specific crop nitrogen management providing both spatial and temporal information.

• Korean Journal of Medicinal Crop Science
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• v.28 no.6
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• pp.412-420
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• 2020

Background: Measurement of chlorophyll fluorescence (CF) is useful for detection the ability of plants to tolerate environmental stresses such as drought, and excessive sunlight. Cnidium officinale Makino is highly sensitive to water stress and excessive sunlight. In this study, we evaluated the effect of soil water and shade treatment on the photosynthesis and leaf temperature change of C. officinale. Methods and Results: C. officinale was cultivated under uniform irrigation for 1 week drought stress (no watering) for 6 days. A significant decrease in CF was observed on the 5th day of withholding water (approximately 6% of soil water content) regardless of shading. Notably, the Rfd_lss parameter (CF decrease rates) with and without shade treatment was reduced by 73.1% and 56.5% respectively, at 6 days compared with those at the initial stage (0 day). The patterns of the degree of CF parameters corresponded to those of the soil water content and difference between leaf temperature (Ts) and air temperature (Ta). Meanwhile, CF parameters recovered to the 3 - 4 days levels after re-watering, while the soil water potential was completely restored. The suitable soil water content for C. officinale optimal growth was between -5 kPa and -10 kPa in this experiment. Conclusions: Lack of soil water in the cultivation of C. officinale, even with shading, decreased latent heat cooling through transpiration. As a result, heat dissipation declined, and the plant was subjected to drought stress. Soil water content plays a major role in photosynthesis and leaf temperature in C. officinale.

• The Journal of Korea Robotics Society
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• v.11 no.1
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• pp.1-8
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• 2016

In this paper, we propose a new algorithm of the guidance line extraction for autonomous agricultural robot based on vision camera in paddy field. It is the important process for guidance line extraction which finds the central point or area of rice row. We are trying to use the central region data of crop that the direction of rice leaves have convergence to central area of rice row in order to improve accuracy of the guidance line. The guidance line is extracted from the intersection points of extended virtual lines using the modified robust regression. The extended virtual lines are represented as the extended line from each segmented straight line created on the edges of the rice plants in the image using the Hough transform. We also have verified an accuracy of the proposed algorithm by experiments in the real wet paddy.

• 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.

• Korean Journal of Medicinal Crop Science
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• v.18 no.5
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• pp.311-314
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• 2010

The content of luteolin in the different parts of Lithospermum erythrorhizon was determined by high performance liquid chromatography (HPLC). Luteolin was quantified by a reverse-phase column with gradient solvent program (water : ACN = 90 : 10 to 77 : 23 for 60 min). UV detection was conducted at 330 nm. The content of luteolin was measured in the seeds (0.28 mg/g), stems (0.30 mg/g), and roots (0.16 mg/g) of L. erythrorhizon. The presence of luteolin in Lithospermum species is important in agricultural crop production for increasing the amounts of clinically available medicine and health supplements.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.153-153
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• 2022

There is a growing need to develop ion sensors for agriculture. As a result, several technologies have been developed, such as colorimetry, spectrophotometry, and ion-selective electrode (ISE). Among them, ISE has some advantages compared to others. First, it does not require pre-treatment processes and expensive equipment. Second, it is possible for the portable detection system by introducing small-sized electrodes. Finally, real-time and multiple detections of several ions are pursued. It is well-known that N, P, and K nutrients are critical for crop growth. With the development of agriculture techniques, the importance of soil nutrient analysis has attracted much attention for cost-effective and eco-friendly agriculture. Among several issues, minimizing the use of fertilizers is significant through quantitative analysis of soil nutrients. As a result, it is highly important to analyze certain nutrients, such as N (ammonium ion, nitrate ion, nitrite ion), P (dihydrogen phosphate ion, monohydrogen phosphate ion), and K (potassium ion). Therefore, developing sensors for accurate analysis of soil nutrients is highly desired. n this study, several ISEs have been fabricated to detect N, P, and K. Their performance has been intensively studied, such as sensitivity, selectivity coefficient, and concentration range, and compared with commercialized ISEs. In addition, preliminary tests on the in-situ N, P, and K monitoring have been conducted inside the soil.

• Smart Media Journal
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• v.12 no.10
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• pp.38-46
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• 2023

Farming activities are transforming from traditional skill-based agriculture into knowledge-based and technology-driven digital agriculture. The use of intelligent information and communication technology introduces the idea of smart farming that enables farmers to collect weather data, monitor crop growth remotely and detect crop diseases easily. The introduction of Plantix, a pest and disease management tool in the form of a mobile application has allowed farmers to identify pests and diseases of the crop using their mobile devices. Hence, this study collected the reviews of Plantix to explore the response of the users on the Google Play Store towards the application through Latent Dirichlet Allocation (LDA) topic modeling. Results indicate four latent topics in the reviews: two positive evaluations (compliments, appreciation) and two suggestions (plant options, recommendations). We found the users suggested the application to additional plant options and additional features that might help the farmers with their difficulties. In addition, the application is expected to benefit the farmer more by having an early alert of diseases to farmers and providing various substitutes and a list of components for the remedial measures.