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.

Abstract
서 론
재료 및 방법
    1. 실험 장소
    2. 데이터 수집
    3. 데이터 정리 및 분석
결과 및 고찰
    1. 분석 모형화
    2. 딸기 수확량 예측모델
적 요
Literature Cited

• 김나은(경상국립대학교 대학원 바이오시스템공학과 대학원생) | Na-eun Kim (Graduate Student, Department of Bio-Systems Engineering, Graduate School of Gyeonsang National University, Jinju 52828, Korea)
• 한희선(아이티아이즈 부장) | Hee-sun Han (Department Head, Iteyes Inc., Seoul 07238, Korea)
• 아룰모지엘렌체쟌(경상국립대학교 대학원 바이오시스템공학과 대학원생) | Elanchezhian Arulmozhi (Graduate Student, Department of Bio-Systems Engineering, Graduate School of Gyeonsang National University, Jinju 52828, Korea)
• 문병은(경상국립대학교 스마트팜연구소 학술연구교수) | Byeong-eun Moon (Research Professor, Institute of Smart Farm, Gyeongsang National University, Jinju 52828, Korea)
• 최영우(경상국립대학교 대학원 바이오시스템공학과 대학원생) | Yung-Woo Choi (Graduate Student, Department of Bio-Systems Engineering, Graduate School of Gyeonsang National University, Jinju 52828, Korea)
• 김현태(경상국립대학교 생물산업기계공학과(스마트팜연구소) 교수) | Hyeon-tae Kim (Professor, Department of Bio-Industrial Machinery Engineering, Gyeongsang National University (Institute of Smart Farm), Jinju 52828, Korea) Corresponding author