| 1 | Advances in greenhouse automation and controlled environment agriculture:A transition to plant factories and urban agriculture显示文摘Greenhouse cultivation has evolved from simple covered rows of open-fields crops to highly sophisticated controlled environment agriculture(CEA)facilities that projected the image of plant factories for urban agriculture.The advances and improvements in CEA have promoted the scientific solutions for the efficient production of plants in populated cities and multi-story buildings.Successful deployment of CEA for urban agriculture requires many components and subsystems,as well as the understanding of the external influencing factors that should be systematically considered and integrated.This review is an attempt to highlight some of the most recent advances in greenhouse technology and CEA in order to raise the awareness for technology transfer and adaptation,which is necessary for a successful transition to urban agriculture.This study reviewed several aspects of a high-tech CEA system including improvements in the frame and covering materials,environment perception and data sharing,and advanced microclimate control and energy optimization models.This research highlighted urban agriculture and its derivatives,including vertical farming,rooftop greenhouses and plant factories which are the extensions of CEA and have emerged as a response to the growing population,environmental degradation,and urbanization that are threatening food security.Finally,several opportunities and challenges have been identified in implementing the integrated CEA and vertical farming for urban agriculture. | Redmond Ramin Shamshiri Fatemeh Kalantari K.C.Ting Kelly R.Thorp Ibrahim A.Hameed Cornelia Weltzien Desa Ahmad Zahra Mojgan Shad | 2018 | International Journal of Agricultural and Biological Engineering2018，11，1： | 14 |
| 3 | Detect and attribute the extreme maize yield losses based on spatio-temporal deep learning显示文摘Providing accurate crop yield estimations at large spatial scales and understanding yield losses under extreme climate stress is an urgent challenge for sustaining global food security.While the data-driven deep learning approach has shown great capacity in predicting yield patterns,its capacity to detect and attribute the impacts of climatic extremes on yields remains unknown.In this study,we developed a deep neural network based multi-task learning framework to estimate variations of maize yield at the county level over the US Corn Belt from 2006 to 2018,with a special focus on the extreme yield loss in 2012.We found that our deep learning model hindcasted the yield variations with good accuracy for 2006-2018(R^(2)=0.81)and well reproduced the extreme yield anomalies in 2012(R^(2)=0.79).Further attribution analysis indicated that extreme heat stress was the major cause for yield loss,contributing to 72.5%of the yield loss,followed by anomalies of vapor pressure deficit(17.6%)and precipitation(10.8%).Our deep learning model was also able to estimate the accumulated impact of climatic factors on maize yield and identify that the silking phase was the most critical stage shaping the yield response to extreme climate stress in 2012.Our results provide a new framework of spatio-temporal deep learning to assess and attribute the crop yield response to climate variations in the data rich era. | Renhai Zhong Yue Zhu Xuhui Wang Haifeng Li Bin Wang Fengqi You Luis F.Rodríguez Jingfeng Huang K.C.Ting Yibin Ying Tao Lin | 2023 | Fundamental Research2023，3，6： | 0 |
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