Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects

被引:30
|
作者
Ojo, Mike O. [1 ]
Zahid, Azlan [1 ]
机构
[1] Texas A&M Univ Syst, Dept Biol & Agr Engn, Texas A&M AgriLife Res, Dallas, TX 75252 USA
基金
美国食品与农业研究所; 美国农业部;
关键词
smart farming; greenhouse; deep neural networks; indoor agriculture; plant factory; protected agriculture; vertical farm; smart agriculture; deep learning; DISEASE DIAGNOSIS; FRUIT DETECTION; CLASSIFICATION; ACCURACY; NETWORK; SYSTEM;
D O I
10.3390/s22207965
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Controlled environment agriculture (CEA) is an unconventional production system that is resource efficient, uses less space, and produces higher yields. Deep learning (DL) has recently been introduced in CEA for different applications including crop monitoring, detecting biotic and abiotic stresses, irrigation, microclimate prediction, energy efficient controls, and crop growth prediction. However, no review study assess DL's state of the art to solve diverse problems in CEA. To fill this gap, we systematically reviewed DL methods applied to CEA. The review framework was established by following a series of inclusion and exclusion criteria. After extensive screening, we reviewed a total of 72 studies to extract the useful information. The key contributions of this article are the following: an overview of DL applications in different CEA facilities, including greenhouse, plant factory, and vertical farm, is presented. We found that majority of the studies are focused on DL applications in greenhouses (82%), with the primary application as yield estimation (31%) and growth monitoring (21%). We also analyzed commonly used DL models, evaluation parameters, and optimizers in CEA production. From the analysis, we found that convolutional neural network (CNN) is the most widely used DL model (79%), Adaptive Moment Estimation (Adam) is the widely used optimizer (53%), and accuracy is the widely used evaluation parameter (21%). Interestingly, all studies focused on DL for the microclimate of CEA used RMSE as a model evaluation parameter. In the end, we also discussed the current challenges and future research directions in this domain.
引用
收藏
页数:43
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