Articles
Plant electrophysiology for smart irrigation management of greenhouse
Article number
1373_13
Pages
89 – 96
Language
English
Abstract
Monitoring crop health is a daily routine for growers and farmers to manage and respond effectively and in a timely way to abiotic and biotic challenges, thus preventing crop loss and ensuring quality production.
Digital technology allows remote sensing in real-time for precision agriculture.
Many sensors are now deployed in the field to measure environmental factors such as weather conditions, soil conditions, insect populations, but sensors that directly target a plants physiological state are scarce.
Recent advances in plant electrophysiology allow real-time measurement of electrical signals from plants in greenhouses under typical production conditions.
Combined with machine learning techniques, electrophysiology can accurately predict physiological plant state modifications due to drought or nutrient deficiencies.
Here, we have investigated the ability of an electrophysiology sensor to support real-time crop supervision and manage precision irrigation based on plant demand/needs.
To address this aspect, an automated irrigation set-up has been developed and deployed in a real working environment, e.g., tomato soilless culture.
Based on real-time monitoring of electrical signals, the irrigation system is turned on/off via a set of relay controllers according to a drought-prediction model applied in real-time via a single board computer, namely a Raspberry Pi.
Different algorithms have been evaluated with a comparison between i) conventional greenhouse irrigation system vs. ii) electrophysiology-driven automated irrigation.
We found that irrigation volumes provided to the crop by electrophysiology-driven system were similar to the control.
A similar behaviour was also observed for the drainage.
In addition, fruit quality parameters (°Brix, acidity, firmness) and yield were not affected.
Measuring crop water status in real-time using plant electrophysiology would allow precision irrigation management and therefore improve resource management for sustainable agriculture.
Digital technology allows remote sensing in real-time for precision agriculture.
Many sensors are now deployed in the field to measure environmental factors such as weather conditions, soil conditions, insect populations, but sensors that directly target a plants physiological state are scarce.
Recent advances in plant electrophysiology allow real-time measurement of electrical signals from plants in greenhouses under typical production conditions.
Combined with machine learning techniques, electrophysiology can accurately predict physiological plant state modifications due to drought or nutrient deficiencies.
Here, we have investigated the ability of an electrophysiology sensor to support real-time crop supervision and manage precision irrigation based on plant demand/needs.
To address this aspect, an automated irrigation set-up has been developed and deployed in a real working environment, e.g., tomato soilless culture.
Based on real-time monitoring of electrical signals, the irrigation system is turned on/off via a set of relay controllers according to a drought-prediction model applied in real-time via a single board computer, namely a Raspberry Pi.
Different algorithms have been evaluated with a comparison between i) conventional greenhouse irrigation system vs. ii) electrophysiology-driven automated irrigation.
We found that irrigation volumes provided to the crop by electrophysiology-driven system were similar to the control.
A similar behaviour was also observed for the drainage.
In addition, fruit quality parameters (°Brix, acidity, firmness) and yield were not affected.
Measuring crop water status in real-time using plant electrophysiology would allow precision irrigation management and therefore improve resource management for sustainable agriculture.
Authors
S. Anselmo, G. Carron, T. Meacham, E. Najdenovska, F. Dutoit, L.E. Raileanu, N. Wallbridge, C. Plummer, C. Camps, D. Tran
Keywords
electrophysiology sensor, tomato, precision irrigation, drought algorithm, machine learning
Groups involved
- Division Plant-Environment Interactions in Field Systems
- Division Temperate Tree Fruits
- Division Temperate Tree Nuts
- Division Precision Horticulture and Engineering
- Division Vegetables, Roots and Tubers
- Division Ornamental Plants
- Division Tropical and Subtropical Fruit and Nuts
- Division Vine and Berry Fruits
- Division Greenhouse and Indoor Production Horticulture
- Division Landscape and Urban Horticulture
- Commission Agroecology and Organic Farming Systems
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