Articles
From current to future approaches in the irrigation scheduling of open field vegetable crops
Article number
1416_73
Pages
575 – 584
Language
English
Abstract
The accurate estimate of the irrigation demand of open field vegetable crops is essential for sustainable vegetable production.
Adequate and precise irrigation scheduling (IS) serves equally the ecological requirements and the economic needs of the practice.
The challenge of choosing and using the appropriate IS-system is based on crop, field, farm(er!) and regional constraints.
These can be addressed by a variety of traditional and new IS approaches.
The approaches are differentiated by: i) the physical basis for determining water demand, including soil sensors, crop water balance, and plant signals; ii) recent advancements in automation, smart and precision IS, based on cloud computing, big data processing and IoT. Therein, we focus on wireless sensor networks, proxy and remote sensors for plant signalling and evaporative demand tools for dynamic estimates of ET0 and crop coefficients; and iii) developments in digital technologies and modeling techniques involving artificial intelligence (AI) for estimation of soil moisture.
Most promising decision support systems for IS are built on the coupling of traditional approaches with new IT, sensor, communication and data processing technologies.
With the use of AI, the model complexity and thus the number of sensors required can be reduced, enhancing the implementability of IS-systems.
Adequate and precise irrigation scheduling (IS) serves equally the ecological requirements and the economic needs of the practice.
The challenge of choosing and using the appropriate IS-system is based on crop, field, farm(er!) and regional constraints.
These can be addressed by a variety of traditional and new IS approaches.
The approaches are differentiated by: i) the physical basis for determining water demand, including soil sensors, crop water balance, and plant signals; ii) recent advancements in automation, smart and precision IS, based on cloud computing, big data processing and IoT. Therein, we focus on wireless sensor networks, proxy and remote sensors for plant signalling and evaporative demand tools for dynamic estimates of ET0 and crop coefficients; and iii) developments in digital technologies and modeling techniques involving artificial intelligence (AI) for estimation of soil moisture.
Most promising decision support systems for IS are built on the coupling of traditional approaches with new IT, sensor, communication and data processing technologies.
With the use of AI, the model complexity and thus the number of sensors required can be reduced, enhancing the implementability of IS-systems.
Publication
Authors
S. Rubo, J. Zinkernagel
Keywords
irrigation scheduling, open field, automation, digitization, artificial intelligence, water balance, decision support systems, precision irrigation
Online Articles (74)