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.

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

Figure 1

Bibliometric visualization produced by VOSviewer Software using the author’s specified keywords.

Figure 2

Planning and reporting process of systematic literature review (SLR).

Figure 3

Article inclusion and exclusion process flowchart.

Figure 4

Visual illustration of the deep learning techniques applied to controlled environment agriculture in 2019–2022 (Focusing on the reviewed papers).

Figure 5

Application distribution of deep learning in controlled environment agriculture.

Figure 6

Year-wise distribution of the publication from 2019 to April 2022.

Figure 7

Publication distribution for deep learning applications in controlled environment agriculture.

Figure 8

Country-wise distribution of the reviewed papers in controlled environment agriculture.

Figure 9

Evaluation parameters distribution of deep learning model in controlled environment agriculture.

Figure 10

Distribution of different deep learning training networks used in controlled environment agriculture.

Figure 11

Distribution of different deep learning optimizer used in controlled environment agriculture.

Figure 12

Growing medium distribution in controlled environment agriculture.

Figure 13

Plant distribution of papers for deep learning applications in controlled environment agriculture.