• Journal of Biosystems Engineering
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• v.43 no.1
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• pp.30-36
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• 2018

Purpose: The objective of this study is to evaluate the effect of cultivation conditions on the growth and glucosinolate content of Chinese cabbage and kale. Methods: Chinese cabbage and kale were grown in three different cultivation conditions, including a plant factory, greenhouse, and open field. Samples were collected at two harvesting times (10 d and 20 d after transplanting the seedlings). Nine growth parameters (plant height, plant width, number of leaves, petiole diameter, SPAD readout, leaf length, leaf width, stem diameter, and plant weight) were measured immediately after harvesting, and the samples were freeze-dried and stored until the glucosinolate content was analyzed. Mean values of the growth parameters and glucosinolate contents were evaluated using Duncan's multiple range tests. Results: The results indicated that the plant parameters of the Chinese cabbage and kale were greater for plants grown in the plant factory and greenhouse. The plant height, width, and weight showed significant differences in the Duncan's multiple range tests at a 5% level. The plant factory also produced greater contents of most of the glucosinolates. Conclusions: Three different cultivation conditions significantly affected the growth and glucosinolate contents of Chinese cabbage and kale. Further study is necessary to investigate other functional components and different vegetable varieties.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.653-660
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• 2012

This study classified paddy fields according to rice varieties and monitored temporal changes in rice growth using SAR backscatter coefficients (${\sigma}^{\circ}$). A growing period time-series of backscatter coefficients was set up for nine fine-beam mode RADARSAT-1 SAR images from April to October 2005. The images were compared with field-measured rice growth parameters such as leaf area index (LAI), plant height, fresh and dry biomass, and water content in grain and plants for 45 parcels in Dangjin-gun, Chungnam Province, South Korea. The average backscatter coefficients for early-maturing rice varieties (13 parcels) ranged from -18.17 dB to -6.06 dB and were lower than those for medium-late maturing rice varieties during most of the growing season. Both crops showed the highest backscatter coefficient values at the heading stage (late July) for early-maturing rice, and the difference was greatest before harvest for early-maturing rice. The temporal difference in backscatter coefficients between rice varieties may play a key role in identifying early-maturing rice fields. On the other hand, comparisons with field-measured parameters of rice growth showed that backscatter coefficients decreased or remained on a plateau after the heading stage, even though the growth of the rice canopy had advanced.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.51 no.6
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• pp.497-504
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• 2006

Biodiversity is closely related to the conservation of ecosystems. Ecosystems provide more subtle, but equally essential, services. Microorganisms decompose human's waste and renew the soils that produce our food crops. Biodiversity in Korean paddies encompass 54 families and 107 species of freshwater invertebrates. In terms of the number of aquatic insects affected by different sources, the order starting with the highest population was swine slurry > chemical fertilizer > fresh straw with reduced fertilizers > control. The number of freshwater invertebrate and aquatic macro-invertebrate in surface water of the plots without insecticidal application were 2 and 2.1 times greater than in fields receiving insecticide applications, respectively. The soil microfungal flora of the 85 isolates paddy fields in Korea was 30 species in 13 genera and 11 isolates were unidentified yet. Agricultural policy should be changed to assist the conservation of biodiversity because until now the agricultural ecosystems have been negatively affected from the development of high-yield varieties to enhance food production, and the expansion of fertilizer and chemical use. For the conservation of agricultural ecosystems, agricultural practices with less investment and more resource saving, as well as enhancing the safety of agricultural and livestock products are essential. Finally, this paper was written for the contribution for the development of environmentally friendly farming systems with neighboring or whole ecosystems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.1
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• pp.16-25
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• 2020

Vegetation segmentation in a field color image is a process of distinguishing vegetation objects of interests like crops and weeds from a background of soil and/or other residues. The performance of the process is crucial in automatic precision agriculture which includes weed control and crop status monitoring. To facilitate the segmentation, color indices have predominantly been used to transform the color image into its gray-scale image. A thresholding technique like the Otsu method is then applied to distinguish vegetation parts from the background. An obvious demerit of the thresholding based segmentation will be that classification of each pixel into vegetation or background is carried out solely by using the color feature of the pixel itself without taking into account color features of its neighboring pixels. This paper presents a new pixel-based segmentation method which employs a multi-layer perceptron neural network to classify the gray-scale image into vegetation and nonvegetation pixels. The input data of the neural network for each pixel are 2-dimensional gray-level values surrounding the pixel. To generate a gray-scale image from a raw RGB color image, a well-known color index called Excess Green minus Excess Red Index was used. Experimental results using 80 field images of 4 vegetation species demonstrate the superiority of the neural network to existing threshold-based segmentation methods in terms of accuracy, precision, recall, and harmonic mean.

• Journal of Biosystems Engineering
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• v.27 no.2
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• pp.143-150
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• 2002

Several researches for site-specific weed control have tried to increase accuracy of weed detection with machine vision technique. However, there is a problem which needs substantial time to perform site-specific spraying. Therefore, new technology for real-time precision spraying system is needed. This research was executed to develope the new technology to estimate weed density and size in real time, and to conduct a real-time site-specific spraying. It would effectively reduce herbicide amounts applied for a crop field. The real-time precision spraying system consisted of a Differential Global Positioning System (DGPS) with an error of 2 cm, a machine vision system, a geomagnetic sensor for correction of view point of CCD camera and an automatic sprayer with separately controlled nozzle. The weed density was calculated with comparison between position information and a pre-designed electronic map. The position information was obtained in real time using the DGPS and the machine vision. The electronic map contained a position database of crops automatically constructed when seeding. The developed system was tested on an experimental field of Seoul National University. Success rate of the spraying was about 61%.

• Korean Journal of Organic Agriculture
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• v.19 no.spc
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• pp.295-297
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• 2011

Weed control is the most important issue in organic farming systems that limit crop growth and their yield. Field experiments were conducted in organic soybean (Glycine max Merrill) to evaluate the weed suppression effects of salt and seawater treatment. Weed population and fresh weight were monitored after 6 weeks of salt and seawater treatments. The most important weeds were Digitaria sanguinalis, Portulaca oleracea, Tradescantia reflexa and Chenopodium album var. centrorubrum, but also 6 other species were observed in soybean arable field. Soybean crops under seawater or their solids application were well grown. The results treated with salts and seawater indicate decreases by 13.4~30.8% in weed density and by 18.0~43.2% in their fresh weight and soil hardness increases of up to 2.1-fold. Salt and seawater provided good additional weed control, but they were caused a serious problem in deterioration of soil physical properties.

• Korean Journal of Organic Agriculture
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• v.19 no.spc
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• pp.139-140
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• 2011

In organic farming agriculture, integration of cover crop into cropping system is recommended to improve the soil quality, prevent soil erosion, and control weeds. The aim of this study was to control weeds in soybean fields by integration of cover crops such as hairy vetch and rye. Due to cover crop mulching, weeds occurrence and growth were radically decreased. One month later after transplanting, weed growth inhibition rate of hairy vetch and rye treatment were 98% and 89% respectively, while crimson clover treatment were 50%. These effects last long over two month. The soybean yield of hairy vetch treatment was best. Therefore using hairy vetch as cover crop was highly recommended in organic soybean field.

• Journal of The Korean Society of Grassland and Forage Science
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• v.42 no.3
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• pp.208-214
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• 2022

Drought stress is a condition that occurs frequently in the field, it reduces of the agricultural yield of field crops. The aim of the study was to screen drought-adapted genotype of Italian rye grass. The experiments were conducted between the two Italian ryegrass (Lolium multiflorum L.) cultivars viz. Hwasan (H) and Kowinearly (KE). The plants were exposed to drought for 14 days. The results suggest that the morphological traits and biomass yield of KE significantly affected by drought stress-induced oxidative stress as the hydrogen peroxide (H₂O₂) level was induced, while these parameters were unchanged or less affected in H. Furthermore, the cultivar H showed better adaptation by maintaining several physiological parameter including photosystem-II (Fv/Fm), water use efficiency (WUE) and relative water content (RWC%) level in response to drought stress. These results indicate that the cultivar H shows improved drought tolerance by generic variation, improving photosynthetic efficiency and reducing oxidative stress damages under drought stress. These findings can be useful to the breeder and farmer for improving drought tolerance in Italian rye grass through breeding programs.

• Korean Journal of Environmental Biology
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• v.39 no.4
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• pp.536-541
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• 2021

Pantala flavescens is a dominant Odonata species in the rice fields in Korea. To determine the effects of different temperatures on its larval growth and emergence, field and laboratory experiments were conducted. Larval growth was also monitored in mono-cropping and double-cropping rice fields. The growth of larvae was monitored every week by measuring the head width. In the field experiment, no difference was found in larval growth and emergence between the control temperature and +1.9℃ of the control temperature. The larval growth was greater at 23℃ than at 20℃ laboratory temperatures, and no emergence was recorded at either temperature after eight weeks of monitoring. There was a quadratic relationship between larval growth and temperature in an incubator at five temperature regimes of 15, 20, 25, 30, and 35℃. Midseason water drainage caused the extinction of the existing individuals and newly hatched larvae dominated after re-watering in the rice fields. Larval size was greater in double-cropping fields than in mono-cropping fields in late July but the tendency was reversed in early August. The results of this study suggest that temperature warming will directly promote the larval growth of P. flavescens and indirectly influence seasonal growth via changes in water management in rice fields.

• Smart Media Journal
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• v.11 no.7
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• pp.94-103
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• 2022

Weeds bring disadvantages to crops since they can damage them, and a clean treatment with less pollution and contamination should be developed. Artificial intelligence gives new hope to agriculture to achieve smart farming. This study delivers an automated weeds growth point detection using deep learning. This study proposes a combination of semantic graphics for generating data annotation and U-Net with pre-trained deep learning as a backbone for locating the growth point of the weeds on the given field scene. The dataset was collected from an actual field. We measured the intersection over union, f1-score, precision, and recall to evaluate our method. Moreover, Mobilenet V2 was chosen as the backbone and compared with Resnet 34. The results showed that the proposed method was accurate enough to detect the growth point and handle the brightness variation. The best performance was achieved by Mobilenet V2 as a backbone with IoU 96.81%, precision 97.77%, recall 98.97%, and f1-score 97.30%.