Adaptation of SUBSTOR for controlled-environment potato production with elevated carbon dioxide.

The SUBSTOR crop growth model was adapted for controlled-environment hydroponic production of potato (Solanum tuberosum L. cv. Norland) under elevated atmospheric carbon dioxide concentration. Adaptations included adjustment of input files to account for cultural differences between the field and controlled environments, calibration of genetic coefficients, and adjustment of crop parameters including radiation use efficiency. Source code modifications were also performed to account for the absorption of light reflected from the surface below the crop canopy, an increased leaf senescence rate, a carbon (mass) balance to the model, and to modify the response of crop growth rate to elevated atmospheric carbon dioxide concentration. Adaptations were primarily based on growth and phenological data obtained from growth chamber experiments at Rutgers University (New Brunswick, N.J.) and from the modeling literature. Modified-SUBSTOR predictions were compared with data from Kennedy Space Center's Biomass Production Chamber for verification. Results show that, with further development, modified-SUBSTOR will be a useful tool for analysis and optimization of potato growth in controlled environments.

Machine vision monitoring of plant growth and motion.

The tomato plant was used as a model to study growth and movement due to temperature changes in the environment. A morphological feature, plant top projection canopy area (TPCA), was used to characterize the plant growth and movement. Three temperature regimes (normal temperature, low temperature, and a step change from normal to low temperature) were used for the study. It is found that the plants have significant cyclic canopy movement. In addition, both plant growth, which is represented by canopy expansion, and canopy movement are affected by air temperature. The response of the plant to a step change of air temperature was also documented.

An automated plant monitoring system using machine vision.

A plant growth chamber equipped with a machine vision (MV) system was developed for the continuous, non-contact sampling and near-real-time evaluation of the top projected leaf area (TPLA) of lettuce (Lactuca sativa, cv. Ostinata) seedlings. A rotary table enabled automatic, individual presentation of the lettuce plants to the imaging system. Hourly measurements were continuously made for 16 plants from the first true leaf stage through 30 days from seeding. A near-infrared radiation source illuminated the plants during the dark period, permitting measurements without interrupting the 12 hour photoperiod. Daily minimum hourly change of TPLA for the plants occurred from 3 to 4 hours after the start of the light period. Most rapid increase in TPLA occurred from 4 to 5 hours after the onset of the dark period. The machine vision system was capable of determining a plant physiological response to the nutrient stress within 24 hours of the change of the nutrient regime.

Monitoring of plant development in controlled environment with machine vision.

Information acquisition is the foremost requirement for the control and continued operation of any complex system. This is especially true when a plant production system is used as a major component in a sustainable life support system. The plant production system not only provides food and fiber but is a means of providing critically needed life supporting elements such as O2 and purified H2O. The success of the plant production system relies on close monitoring and control of the production system. Machine vision technology was evaluated for the monitoring of plant health and development and showed promising results. Spectral and morphological characteristics of a model plant were studied under various artificially induced stress conditions. From the spectroscopic studies, it was found that the stresses can be determined from visual and non-visual symptoms. The development of the plant can also be quantified using a video image analysis base approach. The correlations between the qualities of the model plant and machine vision measured spectral features were established. The success of the research has shown a great potential in building an automated, closed-loop plant production system in controlled environments.

Research on flexible automation and robotics for plant production at Rutgers University.

This is an overview of research activities in the areas of flexible automation and robotics (FAR) within controlled environment plant production systems (CEPPS) in the Department of Bioresource Engineering, Rutgers University. In the past thirty years, our CEPPS research has dealt with the topics including structures and energy, environmental monitoring and control, plant growing systems, operations research and decision support systems, flexible automation and robotics, and impact to natural (i.e. surrounding) environment. Computer and modeling/simulation techniques have been utilized extensively. Mechanized systems have been developed to substitute human's physical labor and maintain uniformity in production. Automation research has been directed towards adding, to the mechanized systems, the capabilities of perception, reasoning, communication, and task planning. Computers, because of their programmability, provide flexibility to automated systems, when incorporated with generic hardware devices. Robots are ideal hardware tools to be employed in flexible automation systems. Some technologies developed in our CEPPS research may be readily adaptable to Closed Bioregenerative Life Support Systems (CBLSS).

Systems approach to instrumenting and controlling plant growth systems.

Acquisition and analysis of sensory information are foremost for the control and continued operation of any complex system. The sensors and their attributes must be selected by understanding the biological and physical parameters which, first, can describe, and second, when linked to control systems, can modulate, the plant growth system. These parameters are not all understood, or known, and practical sensors may not even exist for their measurement. A systematic analysis of the general plant system would: focus without prejudice on all the descriptive parameters, as well as, their interrelationships within the biophysical system; highlight the significance of each parameter; expose the areas of weakness and strength of current knowledge; expand the knowledge base; provide the platform for the development of operational models for real-time monitoring and control requirements; and support the longer term tactical and strategic planning needs. Components of such a procedure of systematic analysis which is in development for intensive plant production systems within controlled environments will be discussed.