• Research Article

    Effects of CO2 concentration and greenhouse internal temperature on the production of the fully ripe tomato dephnis variety

    이산화탄소농도와 온실내부온도가 완숙토마토 ‘데프니스’ 생산량에 미치는 영향

    Bum-seouk Kim, Sungwan Park, Hoon Seonwoo

    김범석, 박성완, 선우훈

    Recent advancements in smart farming technology in South Korea have emphasized the importance of precise environmental control for enhancing crop productivity in … + READ MORE
    Recent advancements in smart farming technology in South Korea have emphasized the importance of precise environmental control for enhancing crop productivity in large-scale facilities. In particular, optimizing CO2 enrichment and temperature management is essential for maximizing the yield of hydroponically grown tomatoes in glass greenhouses. This study evaluated how greenhouse CO2 concentration and internal temperature were associated with the yield of fully ripe tomato cultivated in a Venlo-type glass greenhouse in Goheung, Jeollanam-do, Republic of Korea. Environmental data were recorded at 5-min intervals from 1 October 2022 to 29 April 2023 and summarized as 7-day averages. Tomato growth traits and harvested fruit weight were monitored throughout the cultivation period. To account for the approximate time lag between fruit set and harvest, environmental variables measured 8 weeks prior to harvest were compared with weekly yield patterns. CO2 enrichment began during cultivation, increasing the internal CO2 concentration from approximately 420 ppm to higher levels, and yield tended to increase after the rise in CO2 concentration. Multiple linear regression showed that the 8-week-lagged mean CO2 concentration and the internal maximum temperature significantly predicted harvested fruit weight. The model explained 69.6% of the variance in yield, with an adjusted R2 of 0.635, and it was statistically significant with an F value of 11.437 and a p value of 0.003. Both the CO2 concentration and the internal maximum temperature were positively associated with yield, with regression coefficients of 10.807 and 294.184 and corresponding p values of 0.015 and 0.019, respectively. Multicollinearity was not detected, as indicated by a variance inflation factor of 1.085. These results suggest that combining CO2 enrichment with appropriate temperature control, particularly by managing internal maximum temperature, may help achieve stable yield improvement in greenhouse-grown ‘Dephnis’ tomatoes. - COLLAPSE
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    31 March 2026

  • Research Article

    Development of deep learning-based pest detection technology for image-based automatic trap monitoring

    영상 기반 자동 트랩 모니터링을 위한 딥러닝 기반 해충 검출 기술 개발

    Hye-Ki Jeong, Seong-Hwa Oh, Gyeong-Tae Kim, Jeong-Gyu Park, Se-Hyeon Gwon, Seoyeon Hong, Suk-Ju Hong

    정혜기, 오성화, 김경태, 박정규, 권세현, 홍서연, 홍석주

    Pests and diseases cause significant agricultural losses each year, posing a serious threat to global food security. According to the Food and … + READ MORE
    Pests and diseases cause significant agricultural losses each year, posing a serious threat to global food security. According to the Food and Agriculture Organization, while approximately 50% more food production will be required by 2050, nearly 40% of current global crop yields are already lost to pest damage. As climate change alters the ecology and occurrence patterns of insect pests, conventional manual monitoring methods suffer from low accuracy and efficiency, limiting timely responses. In addition, earlier attempts to automate trap monitoring relied mainly on conventional image-processing techniques. Although such approaches showed the possibility of automated pest detection, their performance was often sensitive to pest posture, illumination changes, and complex field backgrounds, which limited robust application in real-world environments. To address these challenges, this research proposes a deep learning–based object detection model for species-level automatic detection and identification of two major pests— Spodoptera litura (tobacco cutworm) and Spodoptera exigua (beet armyworm)—within automatic trap environments. Model performance was evaluated using Precision, Recall, mAP@50, and mAP@75 metrics, based on training and validation with field-collected trap image data. Preliminary results demonstrate that the proposed system achieves reliable multi-species detection under varying environmental conditions, reduces dependence on on-site inspection through remote monitoring, and shows potential for supporting pest management decisions such as early warning and density estimation. - COLLAPSE
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    31 March 2026

  • Research Article

    Monitoring and control of nutrient variability management for hot-temperature crops in smart greenhouses: A review

    스마트 온실에서 고온성 작물의 양분 변이 관리를 위한 모니터링 및 제어에 관한 고찰

    Cholet Nyangoma Atulinda, Emmanuel Bicamumakuba, Md Nasim Reza, Sakib Robin, Hyeunseok Choi, Sun-Ok Chung

    AtulindaCholet Nyangoma, BicamumakubaEmmanuel, RezaMd Nasim, RobinSakib, 최현석, 정선옥

    Hot-temperature crop production is expanding rapidly to secure year-round supplies of high-value crops such as tomato, sweet pepper, cucumber, and melon in … + READ MORE
    Hot-temperature crop production is expanding rapidly to secure year-round supplies of high-value crops such as tomato, sweet pepper, cucumber, and melon in regions facing intensified heat, radiation, and water scarcity. However, nutrient management under hot weather conditions remains a persistent barrier because nutrient availability and uptake are highly sensitive to microclimate-water-plant interactions. This review addressed current technology on nutrient variability, defined as spatial and temporal fluctuations in nutrient concentration, availability, and plant uptake for hot-temperature crops in greenhouses, and evaluates monitoring and control solutions. Different studies showed the air temperature, high vapor pressure deficit, and root-zone temperatures frequently exceeding 30°C amplify evapotranspiration and disturb ion transport, often inducing calcium, magnesium, boron, or iron-related disorders despite adequate solution concentrations. Spatial variability is driven by heterogeneous airflow, cooling efficiency, irrigation uniformity, and substrate hydraulic/chemical properties, while temporal variability arises from diurnal microclimate shifts, growth stage demand, and nutrient accumulation-depletion cycles. Conventional laboratory-based solution sampling and tissue analyses were compared with advanced approaches including electrical conductivity (EC) and pH probes, ion-selective electrodes (ISE), optical sensing modules, and imaging/remote platforms supported by internet of things (IoT) connectivity and artificial intelligence (AI) analytics. Control strategies were reviewed across scales, from adaptive nutrient supply scheduling and dynamic nutrient formulation to closed-loop feedback systems, decision support tools, fuzzy and hybrid controllers, and emerging digital twin frameworks that couple sensor streams with real-time simulation. Key limitations include sensor drift and temperature interference, calibration and interoperability challenges, high upfront costs, and grower adoption barriers. Future research needs to prioritize robust, low-cost sensing technologies, standardized data fusion standards, predictive AI-IoT control systems, and sustainable circular nutrient management to enhance resilience and productivity under climate warming. - COLLAPSE
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    31 March 2026
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