4.6 Review

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

期刊

SENSORS
卷 22, 期 20, 页码 -

出版社

MDPI
DOI: 10.3390/s22207965

关键词

smart farming; greenhouse; deep neural networks; indoor agriculture; plant factory; protected agriculture; vertical farm; smart agriculture; deep learning

资金

  1. United States Department of Agriculture (USDA)'s National Institute of Food and Agriculture (NIFA) Federal Appropriations [TEX09954, 7002248]
  2. AgriLife Research
  3. VFIC
  4. Hatch program of the National Institute of Food and Agriculture, U.S. Department of Agriculture

向作者/读者索取更多资源

This article presents a systematic review of the application of deep learning (DL) in controlled environment agriculture (CEA). The review provides an overview of DL applications in different CEA facilities and analyzes commonly used DL models, evaluation parameters, and optimizers. The study found that most research focuses on DL applications in greenhouses, particularly in yield estimation and growth monitoring. The review also discusses current challenges and future research directions in this field.
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.

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