Articles
CROP MONITORING BY MEANS OF AN ON-LINE EARLY WARNING SYSTEM
Article number
718_20
Pages
183 – 188
Language
English
Abstract
Modern horticulture is driven by quality and yield, which are highly dependent on plant health status.
Today the grower can only react and intervene when symptoms become visible but in most cases with already irrecoverable damage.
The challenge is to monitor the plants physiological status in a continuous way to anticipate unfavourable environmental conditions more rapidly.
The objective of the reported research was to develop real-time mathematical algorithms to monitor plant stress as a basis for an on-line early-warning system.
Truss tomato plants (Lycopersicon esculentum Mill. Clothilde) were cultivated inside two greenhouse compartments.
During the growing season several severe and moderate stress conditions such as an increased and decreased indoor temperature were applied alternately in between the two compartments.
In total 17 stress experiments were executed, duplicated over two compartments.
The stress intensity was function of the outdoor climate and the duration of the applied stressor such that the achieved minimal indoor temperature ranged from 12.8°C to 16°C for the cold shock and the maximal indoor temperature ranged from 27.6°C to 36.7°C for the heat shock.
Climate and plant variables were sampled every 20 seconds.
Leaf temperature was measured as a plant response and was on-line modelled as function of climate variables with a black-box model approach.
The plant health status was evaluated by the calculated steady state gain of the black box model.
Depending on stress intensity and health status of the plant in combination with the environmental conditions changes in plant status could be detected, some minutes after stress situations were applied.
Visible damage occurred hours later after the stress conditions were applied.
Today the grower can only react and intervene when symptoms become visible but in most cases with already irrecoverable damage.
The challenge is to monitor the plants physiological status in a continuous way to anticipate unfavourable environmental conditions more rapidly.
The objective of the reported research was to develop real-time mathematical algorithms to monitor plant stress as a basis for an on-line early-warning system.
Truss tomato plants (Lycopersicon esculentum Mill. Clothilde) were cultivated inside two greenhouse compartments.
During the growing season several severe and moderate stress conditions such as an increased and decreased indoor temperature were applied alternately in between the two compartments.
In total 17 stress experiments were executed, duplicated over two compartments.
The stress intensity was function of the outdoor climate and the duration of the applied stressor such that the achieved minimal indoor temperature ranged from 12.8°C to 16°C for the cold shock and the maximal indoor temperature ranged from 27.6°C to 36.7°C for the heat shock.
Climate and plant variables were sampled every 20 seconds.
Leaf temperature was measured as a plant response and was on-line modelled as function of climate variables with a black-box model approach.
The plant health status was evaluated by the calculated steady state gain of the black box model.
Depending on stress intensity and health status of the plant in combination with the environmental conditions changes in plant status could be detected, some minutes after stress situations were applied.
Visible damage occurred hours later after the stress conditions were applied.
Authors
J. Dekock, J.-M. Aerts, D. Berckmans, K. Vermeulen, K. Steppe, R. Lemeur, K. Janssen, P. Bleyaert, J. Westra, T. Rieswijk
Keywords
Lycopersicon esculentum, crop monitoring, black box, early warning, speaking plant, phytomonitoring
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