토마토뿔나방(Phthorimaea absoluta)은 현재 전 세계 토마토 재배에 심각한 문제를 초래하는 해충이며 국내에서는 2023년에 처음 발견된 이후. 전국적으로 퍼져 국내 토마토 재배 농가에 많은 경제적 손실을 입히고 있다. 특히 친환경 토마토 재배 농가에 대한 피해가 크게 발생하였지만 친환경 방제 방법이 부족한 상황이다. 북아프리카와 유럽에서는 토마토뿔나방의 포식성 천적인 담배장님노린재 (Nesidiocoris tenuis)를 상업적으로 이용하고 있으며, 스페인에서 알벌류(Tricogrammatidae)를 토마토뿔 나방 방제에 활용하는 등 친환경 방제수단으로써 천적을 활용하고 있다. 국외에서의 천적을 이용한 토마토뿔나방 방제 사례를 수집하여 기존 국내 천적의 활용성을 검증하고 이를 토대로 국내 토착 천적의 탐색 및 효과 검증을 통한 종합적인 천적 활용 친환경 방제 전략 수립에 이용하고자 한다.

Cherry tomato (Solanum lycopersicum L,. var. cerasiforme Mill.) is small fruits with a bright red color resembling a cherry and having an excellent taste, sweet and juicy ambience. So far, no cherry tomato variety was registered in Ethiopia. Consequently, six genotypes were imported from National Institute of Horticulture and Herbal Sciences (NIHHS), Rural Development Administration (RDA) Republic of Korea, and field experiment was conducted in RCBD with three replications at six Ethiopian testing sites, with irrigation, during off-seasons of 2021 and 2022 to identify high yielding, well adapted and good quality varieties. The overall analysis of variance across locations and years showed non-significant difference among the genotypes for marketable and total yields. But separate analysis for each site has revealed significant differences among genotypes at Melkassa, Koka, Adami- Tulu and Fogera, unlike that of Kulumsa and Woramit. There were significant differences (P < 0.05) among these genotypes for fruit numbers per plant, average fruit weight, fruits per cluster, plant height, skin thickness, juice volume and total soluble solid. Wonhong No.3 gave higher marketable (24.49 t/ha) and total (26.19 t/ha) yields, and generally Wonhong Nos.3 and 5 had higher yields and good qualities across these tested locations and years. Hence, Wonhong No.3 (designated as Jorgie-1) was registered for its higher yield, non-cracking, good TSS and color, while Wonhong No.5 (renamed as Jorgie-2) was preferred for its smaller fruit size, reasonable yield and quality (TSS, color, non-cracking). Hence, both varieties were officially registered in 2023 season for commercial production in different agro-ecologies of Ethiopia, and they are believed to add more economic and nutritional values for the tomato producers and the consumers. They can also support the intensification of tomato cultivation in peri-urban and urban agriculture, where demands and thus government focus are increasingly growing.

본 연구는 과피색에 따른 토마토 과실의 숙성 단계에 따른 기능성 물질 및 항산화 활성의 차이를 알아보기 위해 실시하 였다. 토마토 샘플은 성숙한 단계에서 과피색이 황색, 흑색, 적 색으로 구별되는 세 가지 토마토 품종을 이용하였다. 토마토 샘플을 녹색기, 변색기, 최색기, 완숙기의 4가지 숙성 단계에 서 수확한 후 당, 라이코펜, 아스코르브산, 폴리페놀 및 항산화 활성을 포함한 다양한 생리 활성 화합물을 분석하였다. 토마 토 과실의 주요 당분은 과당과 포도당이다. 황색 토마토의 과 당과 포도당 함량은 숙성 단계에 따라 점차 증가하였다. 그러 나 흑색 토마토와 적색 토마토는 변색기 단계에서 증가한 후 상대적으로 일정하게 유지되었다. 과피색에 관계없이 모든 토마토 과실에 함유된 라이코펜 함량은 숙성 단계에 따라 크 게 증가했습니다. 라이코펜 함량은 적색 토마토 과실의 성숙 단계에서 가장 높게 관찰되었다. 황색 토마토 과실의 아스코 르브산 함량은 낮았으며 숙성 단계 동안 상대적으로 일정하게 유지되었다. 흑색 토마토 과실의 아스코르브산 함량은 성숙 단계에서 2,249mg·kg-1으로 크게 증가한 반면, 적색 토마토 과실에서는 성숙 단계에서 3,529mg·kg-1으로 점차 증가했습 니다. 페놀성 화합물인 퀘르시트린은 토마토 과실에서 발견 되었지만, 성숙 단계에서 토마토 과실의 퀘르시트린 함량은 점차적으로 감소되었다. ABTS 라디칼 소거 활성은 최색기의 황색 토마토 과실에서 급격히 증가한 반면, 흑색 토마토와 적 색 토마토에서는 숙성 단계에 따라 점진적으로 증가하였다. 모든 토마토 과실의 DPPH 라디칼 소거 활성은 최색기에서 크 게 증가했다.

Five insecticides (Acrinathrin, Dinotefuran, Emamectin benzoate, Chlorfenapyr and fluxametamide) approved for tomato cultivation were evaluated in Frankliniella occidentalis populations collected from Chungcheong province (Cheongyang, Chungju and Gongju). Leaf dip bioassay was used to evaluate resistance levels (LC50). Bioassays on Acrinathrin demonstrated higher LC50 concentration in evaluated populations. In particular, the Chungju population was 745.61 times the recommended concentration of the insecticide. Other remarkable resistance levels were recorded for the Dinotefuran with 435.06 times and 196.29 times the recommended concentrations for the populations from Chungju and Gongju, respectively. Bioassays for Emamectin benzoate, Chlorfenapyr and Fluxametamide showed low resistance to insecticides in the evaluated populations.

Density survey should be carried out for applying integrated pest management strategies, but it is labor-intensive, time- and cost-consuming. Therefore, binomial sampling plans are developed for estimating and classifying the population density of whiteflies late larvae based on the relationship between the mean density per sample unit (7 leaflets) and the proportion of leaflets infested with less than T whiteflies ( ). In this study, models were examined using tally thresholds ranging from 1 to 5 late larvae per 7 leaflets. Regardless of tally thresholds, increasing the sample size had little effect on the precision of the binomial sampling plan. Based on the precision of the model, T=3 was the best tally threshold for estimating the densities of late larvae. Models developed using T=3 validated by Resampling Validation for Sampling Plan program. Above all, the binomial model with T=3 performed well in estimating the mean density of whiteflies in greenhouse tomato.

Gray leaf spot caused by Stemphylium spp., is a major disease of tomatoes, and it threatens its cultivation worldwide, especially in warm and humid areas. This study was conducted on 223 tomato germplasm conserved at the National Agrobiodiversity Center to select the resources resistant to the gray leaf spot pathogen strain previously isolated in Korea, using a bioassay and genotypic analysis of the resistance gene (Sm). Two weeks after inoculation with Stemphylium lycopersici, the disease index (rated on a scale of 0-4) of gray leaf spot was assessed in detached tomato leaves. The results showed that 22 resources were resistant, with a disease index of 0-1. Additionally, 65 genetic resources were found to be moderately resistant, with a disease index between 1.0 and 2.0. Subsequently, Hybridization Probe Melting (HPM) analysis of the 22 resistant genetic resources confirmed the genotype of the gray leaf spot resistance gene (Sm). Among them, 20 genetic resources showed a homozygous resistant genotype. The resources selected in this research may contribute to the breeding of new tomato varieties resistant to gray leaf spot and may serve as a basis for further genotypic analysis studies.

Tomato is one of the major widely cultivated crops around the world. The leaf area is directly related to the total amount of photosynthesis, which affects the yield and quality of the fruit. Traditional methods of measuring the leaf area are time-consuming and can cause damage to the leaves. To address these problems, various studies are being conducted for measuring the leaf area. In this study, we introduced a model to estimate the leaf area using images of tomatoes. Using images captured by a camera, we measured the leaf length and width and used linear regression analysis to derive the leaf area estimation formula. Furthermore, we used a Neural Network (NN) for additional analysis to compare the accuracy of the models. Initially, to verify the reliability of the image data, we conducted a correlation analysis between the actual measurement data and the image data, which showed a high positive correlation. The leaf area estimation model presented 23 estimation formulas. We used regression analysis to estimate the coefficients of each model and also used employed an artificial neural network analysis to derive high R-squared (R2) values and low Root Mean Square Error (RMSE) values. Among the estimation formulas, the ninth model showed the highest reliability with an R-squared value of 0.863. We conducted a verification experiment to confirm the accuracy of the selected model, and the R-squared value was 0.925. This study confirmed the reliability of data measured from images and the reliability of the leaf area estimation model using image data. These methods are expected to be an important tool in agriculture, using imaging equipment for measuring and monitoring the crop growth.

증산은 적정 관수 관리에 중요한 역할을 하므로 수분 스트레스에 취약한 토마토와 같은 작물의 관개 수요에 대한 지식이 필요하다. 관수량을 결정하는 한 가지 방법은 증산량을 측정하는 것인데, 이는 환경이나 생육 수준의 영향을 받는다. 본 연구는 분단위 데이터를 통해 수학적 모델과 딥러닝 모델을 활용하여 토마토의 증발량을 추정하 고 적합한 모델을 찾는 것을 목표로 한다. 라이시미터 데이터는 1분 간격으로 배지무게 변화를 측정함으로써 증산 량을 직접 측정했다. 피어슨 상관관계는 관찰된 환경 변수가 작물 증산과 유의미한 상관관계가 있음을 보여주었다. 온실온도와 태양복사는 증산량과 양의 상관관계를 보인 반면, 상대습도는 음의 상관관계를 보였다. 다중 선형 회귀 (MLR), 다항 회귀 모델, 인공 신경망(ANN), Long short-term memory(LSTM), Gated Recurrent Unit(GRU) 모델을 구 축하고 정확도를 비교했다. 모든 모델은 테스트 데이터 세트에서 0.770－0.948 범위의 R2 값과 0.495mm/min－ 1.038mm/min의 RMSE로 증산을 잠재적으로 추정하였다. 딥러닝 모델은 수학적 모델보다 성능이 뛰어났다. GRU 는 0.948의 R2 및 0.495mm/min의 RMSE로 테스트 데이터에서 최고의 성능을 보여주었다. LSTM과 ANN은 R2 값이 각각 0.946과 0.944, RMSE가 각각 0.504m/min과 0.511로 그 뒤를 이었다. GRU 모델은 단기 예측에서 우수한 성능 을 보였고 LSTM은 장기 예측에서 우수한 성능을 보였지만 대규모 데이터 셋을 사용한 추가 검증이 필요하다. FAO56 Penman-Monteith(PM) 방정식과 비교하여 PM은 MLR 및 다항식 모델 2차 및 3차보다 RMSE가 0.598mm/min으로 낮지만 분단위 증산의 변동성을 포착하는 데 있어 모든 모델 중에서 가장 성능이 낮다. 따라서 본 연구 결과는 온실 내 토마토 증산을 단기적으로 추정하기 위해 GRU 및 LSTM 모델을 권장한다.

Tomatoes in greenhouse are a widely cultivated horticultural crop worldwide, accounting for high production and production value. When greenhouse ventilation is minimized during low temperature periods, CO2 enrichment is often used to increase tomato photosynthetic rate and yield. Plant-induced electrical signal (PIES) can be used as a technology to monitor changes in the biological response of crops due to environmental changes by using the principle of measuring the resistance value, or impedance, within the crop. This study was conducted to investigate the relationship between tomato growth data, vital response, and PIES resulting from CO2 enrichment in greenhouse tomatoes. The growth of tomato treated with CO2 enrichment in the morning was significantly better in all items except stem diameter compared to the control, and PIES values were also higher. The growth of tomato continuously applied with CO2 was better in the treatment groups than control, and there was no significant difference in chlorophyll fluorescence and photosynthesis. However, PIES and SPAD values were higher in the CO2 treatment group than control. CO2 enrichment have a direct relationship with PIES, growth increased, and transpiration increased due to the increased leaf area, resulting in increased water absorption, which appears to be reflected in PIES, which measures vascular impedance. Through this, this study suggests that PIES can be used to monitor crops due to environmental changes, and that PIES is a useful method for non-destructively and continuously monitoring changes of crops.

This study was aimed to determine the changes in CO2 concentration according to the temperatures of daytime and nighttime in the CO2 supplemental greenhouse, and to compare calculated supplementary CO2 concentration during winter and spring cultivation seasons. CO2 concentrations in experimental greenhouses were analyzed by selecting representative days with different average temperatures due to differences in integrated solar radiation at the growth stage of leaf area index (LAI) 2.0 during the winter season of 2022 and 2023 years. The CO2 concentration was 459, 299, 275, and 239 μmol·mol-1, respectively at 1, 2, 3, and 4 p.m. after the CO2 supplementary time (10:00-13:00) under the higher temperature (HT, > 18°C daytime temp. avg. 31.7, 26.8, 23.8, and 22.4°C, respectively), while it was 500, 368, 366, 364 μmol·mol-1, respectively under the lower temperature (LT, < 18°C daytime temp. avg. 22.0, 18.9, 15.0, and 13.7°C, respectively), indicating the CO2 reduction was significantly higher in the HT than that of LT. During the nighttime, the concentration of CO2 gradually increased from 6 p.m. (346 μmol·mol-1) to 3 a.m. (454 μmol·mol-1) in the HT with a rate of 11 μmol·mol-1 per hour (240 tomatoes, leaf area 330m2), while the increase was very lesser under the LT. During the spring season, the CO2 concentration measured just before the start of CO2 fertilization (7:30 a.m.) in the CO2 enrichment greenhouse was 3-4 times higher in the HT (>15°C nighttime temperature avg.) than that of LT (< 15°C nighttime temperature avg.), and the calculated amount of CO2 fertilization on the day was also lower in HT. All the integrated results indicate that CO2 concentrations during the nighttime varies depending on the temperature, and the increased CO2 is a major source of CO2 for photosynthesis after sunrise, and it is necessary to develop a model formula for CO2 supplement considering the nighttime CO2 concentration.

This study was conducted to investigate the growth characteristics of cucumber (Cucumis sativus L. ‘Joeunbaekdadagi’) and tomato (Solanum lycopersicum L. ‘Dotaerang Dia’) seedlings by light intensities and CO2 concentrations in a closed-type plant production system (CPPS). Cucumber and tomato seeds were sown in 50-cell trays and germinated in CPPS at air temperature 25 ± 1°C and relative humidity 50 ± 10% for 4 days. After germination, the CO2 concentrations and light intensity treatment were treated at 500 (ambient), 1,000, and 1,500 μmol·mol-1 and 100, 200, and 300 μmol·m-2·s-1 photosynthetic photon flux density (PPFD), respectively. The leaf area of cucumber showed the highest value in CO2 1,500 μmol·mol-1. However, the leaf area of the tomato had no significant difference in CO2 concentrations and light intensities treatments. In cucumber and tomato both seedlings, the growth and quality such as compactness and leaf area rate were increased with the increase of light intensity, and there were highest in 300 μmol·m-2·s-1. The root surface and number of root tips of cucumber and tomato seedlings were significantly increased with the increase in light intensity. In conclusion, the regulation of the CO2 concentrations and light intensity can control the growth and quality of cucumber and tomato seedlings in CPPS, especially, increasing the light intensity can improve more significantly the growth and quality of seedlings.