• KOREAN JOURNAL OF CROP SCIENCE
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• v.54 no.2
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• pp.203-209
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• 2009

Yield monitoring is one of a precision agriculture technology that is used most widely. It is spatial variability analysis of yield information that should be attained with yield monitoring system development. This experiment was conducted to evaluate spatial variability of yield and grain moisture content in rice paddy field, and their relationships to rice productivity. It is necessary to minimize sampling interval for accurate yield map making or to control cutting width of rice combine. Considering small rice plots such as $0.2{\sim}0.4$ ha, optimum size of sampling plot was below 15 m more than 5 m in with and length. In variable rate treatment field, average yield was similar, but yield variation was reduced than conventional field. Gap of yield by another plot in same field was bigger than half of average yield than yield variation was significantly big. Therefore yield measuring flow sensor must be able to measure at least 300 kg/10a more than 1000 kg/10a. Variation of moisture content in same field was not big and spatial dependance did not appear greatly. But, variation between different field is appeared difference according to weather circumstance before harvesting. Change of spatial dependence of yield was not big, because of field variation of moisture content is not big.

• Journal of Biosystems Engineering
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• v.24 no.2
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• pp.135-144
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• 1999

An yield monitoring system plays a key role in precision farming. An yield monitoring system and a DGPS were implemented to a widely used domestic combine for yield mapping of a small sized paddy field, and yield mapping algorithms were investigated in this study. The yield variation in the 0.1ha rice paddy field was measured by installing a yield flow sensor and a grain moisture sensor at the end of the clean grain elevator discharging grains into a grain tank. Yield map of the test filed was drawn in a point map and a linear interpolated map based on the result of the field test. The size of a unit yield grid in yield mapping was determined based on the combine traveling speed, effective harvesting width and data storing period. It was possible to construct the yield map of a small sized paddy field.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2020.08a
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• pp.58-58
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• 2020

다양한 품종이 개발되어 재배되고 있는 다래(Actinidia arguta (Siebold & Zucc.) Planch. ex Miq.)의 재배유형이 T덕형과 평덕형으로 나누어지고, 덕 형태 및 품종에 따라 다래의 생육 양상에 차이가 있음에도 불구하고 결과모지 길이를 15cm 길이의 단일한 형태로 전정해 왔다. 본 연구에서는 T덕형을 활용한 다래 재배에서 품종별로 수확량을 증대하면서 과실 품질을 향상시킬 수 있는 적정한 결과모지 길이를 결정하고자 하였다. 이를 위해 국립산림과학원에서 개발한 '새한', '대성' '칠보' 및 '오텀센스' 등 4품종을 대상으로 하여 결과모지 길이가 15, 30 및 50cm가 되도록 전정하였다. 동일한 방법으로 재배한 후 수확기인 9월에 수확량 및 열매 특성을 조사하였다. 그 결과 품종과 가지수, 결과지의 수와 미결실지 수, 열매 무게, 총 수확량 에서 유의한 차이가 있었고, 전정 길이에 따라서는 총 착과 수, 열매 무게 및 총 수확량에서 차이가 있었다. 모든 품종에서 결과모지 길이가 길어질수록 총 수확량은 증가하였으며, 총 수확량은 품종에 따라 2~7배까지 증가하는 경향을 보였다. 결과모지 전정은 '새한', '대성' 및 '오텀센스'는 50cm 전정에서 칠보는 30cm 전정에서 생산량 및 품질특성이 우수하였다.

• Proceedings of the Korean Society of Crop Science Conference
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• 2002.10a
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• pp.52-52
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• 2002

• Proceedings of the Plant Resources Society of Korea Conference
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• 2020.08a
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• pp.57-57
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• 2020

다래(Actinidia arguta (Siebold & Zucc.) Planch. ex Miq.)의 재배에서 현재 많이 이용되고 있는 평덕형은 기존의 T덕형에 비해 가지의 배치가 자유롭고 수확량이 많은 장점이 있다. 평덕형을 활용한 다래 재배에서 결과모지의 전정 길이에 대한 연구는 미흡한 실정이며, T덕형과 마찬가지로 15cm 길이의 단초전정이 권장되어 있다. 본 연구에서는 평덕형에서 품종별로 수확량을 증대하면서 과실 품질을 향상시킬 수 있는 적정한 결과모지 길이를 결정하고자 하였다. 이를 위해 과실 특성이 다른 '오텀센스'와 '대보' 품종을 이용하여 결과모지가 30, 50, 100, 150 및 200cm 길이가 되도록 전정하고, 수확기인 9월에 수확량 및 열매 특성을 조사하였다. 평덕형의 경우 품종과 결과모지 전정길이에 따라서 가지수, 결과지의 수, 총 착과 수, 열매무게 및 총 수확량에서 유의적인 차이를 보였다. 두 품종 모두 150cm로 결과모지를 전정하였을 때 결과모지당 착과 수가 각각 129±71개 및 27±8개로 가장 좋았으며, 총 수확량 역시 각각 1,697.0±990.4g 및 849.0±243.2g으로 가장 좋았다. '오텀센스'와 '대보' 품종은 평덕재배시 결과모지를 150cm로 전정하는 것이 생산량 증대 및 품질 특성이 우수한 것으로 나타났다.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1999.12a
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• pp.713-719
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• 1999

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.07a
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• pp.293-298
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• 2000

• Journal of Bio-Environment Control
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• v.18 no.4
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• pp.413-419
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• 2009

The appropriate dripper position in perlite bag was investigated for tomato production. Drippers were laid at 5(F5), 15cm (F15) away from the stem base or 5cm at first and then moved to 15cm later (M5-15). Roots were developed more near the stem base in F5, while less in F15. Roots were distributed evenly in M5-15. In vertical distribution of water in perlite bag, water content was higher as it went deeper with the variation by dripper positions. Yield was high in F15 and low in F5. In conclusion the position of dripper is the best at 15cm from the stem base in perlite bag culture in view of root distribution and yield.

• Weed & Turfgrass Science
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• v.5 no.2
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• pp.95-100
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• 2016

Seeded variety of zoysiagrass has not been bred yet in Korea. Development of seeded zoysiagrass cultivar will be very important for the growth of turfgrass industry internationally as well as domestically. This research was conducted to investigate seed yield and germinability of 102 collected native zoysiagrass ecotypes in South Korea. Two hundred and seventy seven ecotypes were collected from various locations including coastal and mountain areas, while 102 morphologically distinct and seed producing ecotypes were selected and planted in $1m{\times}1m$ maintenance plots. Seed yield ranged from 0.1 to $32.2g\;m^{-2}$. Highest yielding line was a medium leaf type zoysiagrass of Z6011 with $32.2g\;m^{-2}$. Most collected lines showed seed germination rates of below 50%. However, Z2095 showed highest germination rate of 78%. Considering germination rate and seed yield, collected lines of Z6011, Z 6015, Z1075, ZN1008, and Z1084, which were mostly medium leaf type and Z. japonica types, showed reasonably high potential to be used as breeding lines for high yield seed varieties of zoysiagrass.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1269-1276
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• 2023

Crop estimation is essential for the multinational meal and powerful demand due to its numerous aspects like soil, rain, climate, atmosphere, and their relations. The consequence of climate shift impacts the farming yield products. We operate the dataset with temperature, rainfall, humidity, etc. The current research focuses on feature selection with multifarious classifiers to assist farmers and agriculturalists. The crop yield estimation utilizing the feature selection approach is 96% accuracy. Feature selection affects a machine learning model's performance. Additionally, the performance of the current graph classifier accepts 81.5%. Eventually, the random forest regressor without feature selections owns 78% accuracy and the decision tree regressor without feature selections retains 67% accuracy. Our research merit is to reveal the experimental results of with and without feature selection significance for the proposed ten algorithms. These findings support learners and students in choosing the appropriate models for crop classification studies.