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.

Light Intensity Affects the Assimilation Rate and Carbohydrates Partitioning in Spinach Grown in a Controlled Environment

“…Plant production in controlled environment agriculture (CEA) is proposed as the modern evolution of agriculture. CEA is an advanced farming technology under controlled conditions, in terms of light, temperature, carbon dioxide, relative humidity, and nutrient supply, and also in presence of an unfavourable outdoor climate and at high cultivation density [ 1 ]. It aims to guarantee high and constant year-round production, food quality and security, resilience to climate change, and sustainability, and allows high resource use efficiency and better pathogen control.…”

Design and validation of an open-sourced automation system for vertical farming

“…Another significant challenge in the sector is the high capital expenditure when compared to traditional agriculture [3] , [15] , [16] , [17] . This substantial initial investment is primarily associated with the development of infrastructure, including lighting, racking, grow systems, and buildings [18] .…”

Lettuce Production in Intelligent Greenhouses—3D Imaging and Computer Vision for Plant Spacing Decisions

“…Modern camera systems and innovative artificial intelligence (AI) technologies such as computer vision allow objective, non-invasive, and continuous data for precision horticulture applications [ 33 ]. Advances in machine learning for image processing have resulted in a wide range of research and applications for crop monitoring [ 34 ].…”