Current status and future challenges in implementing and upscaling vertical farming systems.

Vertical farming can produce food in a climate-resilient manner, potentially emitting zero pesticides and fertilizers, and with lower land and water use than conventional agriculture. Vertical farming systems (VFS) can meet daily consumer demands for nutritious fresh products, forming a part of resilient food systems-particularly in and around densely populated areas. VFS currently produce a limited range of crops including fruits, vegetables and herbs, but successful implementation of vertical farming as part of mainstream agriculture will require improvements in profitability, energy efficiency, public policy and consumer acceptance. Here we discuss VFS as multi-layer indoor crop cultivation systems, exploring state-of-the-art vertical farming and future challenges in the fields of plant growth, product quality, automation, robotics, system control and environmental sustainability and how research and development, socio-economic and policy-related institutions must work together to ensure successful upscaling of VFS to future food systems.

Machine vision extracted plant movement for early detection of plant water stress.

A methodology was established for early, non-contact, and quantitative detection of plant water stress with machine vision extracted plant features. Top-projected canopy area (TPCA) of the plants was extracted from plant images using image-processing techniques. Water stress induced plant movement was decoupled from plant diurnal movement and plant growth using coefficient of relative variation of TPCA (CRV[TPCA)] and was found to be an effective marker for water stress detection. Threshold value of CRV(TPCA) as an indicator of water stress was determined by a parametric approach. The effectiveness of the sensing technique was evaluated against the timing of stress detection by an operator. Results of this study suggested that plant water stress detection using projected canopy area based features of the plants was feasible.

Design and development of an automated and non-contact sensing system for continuous monitoring of plant health and growth.

An automated system was designed and built to continuously monitor plant health and growth in a controlled environment using a distributed system approach for operational control and data collection. The computer-controlled system consisted of a motorized turntable to present the plants to the stationary sensors and reduce microclimate variability among the plants. Major sensing capabilities of the system included machine vision, infrared thermometry, time domain reflectometry, and micro-lysimeters. The system also maintained precise growth-medium moisture levels through a computer-controlled drip irrigation system. The system was capable of collecting required data continuously to monitor and to evaluate the plant health and growth.