Biomedical subjects

P P Ling

Publications and source records attributed to P P Ling.

9 recordsLinked to original sources

PubMed1999

Coupling machine vision and crop models for closed-loop plant production in advanced life support systems.

We present a conceptual framework for coupling nondestructive sensing to crop models for closed-loop plant production for NASA's program in advanced life support. Coupling is achieved by comparing nondestructive observations with model predictions of plant growth and development. The information thus provided may be useful in diagnosing problems with the plant growth system, or as a feedback to the model for evaluation of plant scheduling and potential yield. We illustrate this concept using canopy height and machine vision measured top projected canopy area (TPCA), and the CROPGRO crop growth model. Model simulations of soybean TPCA and canopy height were evaluated against data for hydroponic soybean grown under two separate light/dark cycle temperature regimes (23/19 degrees C and 26/22 degrees C). Our results suggest that TPCA and canopy height are potentially useful variables for closed-loop plant production in controlled environments during the first few weeks of growth, before canopy closure.

Biomass↗

PubMed1998

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.

Carbon Dioxide↗

PubMed1996

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).

Artificial Intelligence↗

PubMed1996

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.

Ecological Systems, Closed↗

PubMed1996

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.

Ecological Systems, Closed↗

PubMed1994

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.

Computer Simulation↗

PubMedDate not supplied

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.

Automation↗

PubMedDate not supplied

Machine vision guided sensor positioning system for leaf temperature assessment.

A sensor positioning system was developed for monitoring plants' well-being using a non-contact sensor. Image processing algorithms were developed to identify a target region on a plant leaf. A novel algorithm to recover view depth was developed by using a camera equipped with a computer-controlled zoom lens. The methodology has improved depth recovery resolution over a conventional monocular imaging technique. An algorithm was also developed to find a maximum enclosed circle on a leaf surface so the conical field-of-view of an infrared temperature sensor could be filled by the target without peripheral noise. The center of the enclosed circle and the estimated depth were used to define the sensor 3-D location for accurate plant temperature measurement.

Algorithms↗

PubMedDate not supplied

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.

Ecological Systems, Closed↗