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

Abstract

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.

Plant phenotyping relevance

CEAにおける深層学習の応用を体系的にレビューし、作物モニタリング、ストレス検出、収量推定、成長モニタリングなど植物形質・状態の取得手法を分析しているため、方法論レビューとして中心的に適格。

Code and data availability

This is a systematic review of deep learning in controlled environment agriculture, not an original phenotyping study. It reports no authors' phenotype datasets, images, code, or models; the Data Availability Statement states 'Not applicable.' All URLs in the text are references to cited prior work, market reports, or,

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