• Journal of agriculture & life science
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• v.52 no.6
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• pp.37-47
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• 2018

The objective of this study was to analyze the influences of forest management activity on the diameter distribution of Larix kaempferi Carriere stands in South Korea. We used 232 managed stands data, 47 unmanaged stands data of National Forest Inventory for this study. We employed the Weibull distribution function for estimating diameter based on percentiles and parameter recovery method. The results revealed that the average diameter breast height movements and growth of tree in the managed stands higher than the unmanaged stands according to the scenario: age, site index, and tree density change. The finding shows the percentage of the total amount of large class diameter was also high in the managed stands. The results of this study could be apply for the estimation of multi-products of timbers per diameter classes and stand structure development for Larix kaempferi Carriere stands in South Korea.

• Journal of Bio-Environment Control
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• v.31 no.1
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• pp.1-7
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• 2022

Strawberry is a stand-out cultivating fruit in Korea. The optimum production of strawberry is highly dependent on growing environment. Smart farm technology, and automatic monitoring and control system maintain a favorable environment for strawberry growth in greenhouses, as well as play an important role to improve production. Moreover, physiological parameters of strawberry plant and it is surrounding environment may allow to give an idea on production of strawberry. Therefore, this study intends to build a machine learning model to predict strawberry's yield, cultivated in greenhouse. The environmental parameter like as temperature, humidity and CO₂ and physiological parameters such as length of leaves, number of flowers and fruits and chlorophyll content of 'Seolhyang' (widely growing strawberry cultivar in Korea) were collected from three strawberry greenhouses located in Sacheon of Gyeongsangnam-do during the period of 2019-2020. A predictive model, Lasso regression was designed and validated through 5-fold cross-validation. The current study found that performance of the Lasso regression model is good to predict the number of flowers and fruits, when the MAPE value are 0.511 and 0.488, respectively during the model validation. Overall, the present study demonstrates that using AI based regression model may be convenient for farms and agricultural companies to predict yield of crops with fewer input attributes.