Artificial Intelligence and Irrigation

Irrigation: Overview and Challenges

Irrigation is the alternative to rain-fed agriculture. It is the artificial process of providing water to lands and crops through sprinklers, sprays, pumps, pipes, canals, and other artificially constructed means. Irrigation water is derived from a wide range of sources such as groundwater (springs, wells), surface water (lakes, rivers), or other sources like drainage water, wastewater and desalinated water.

The delivery of irrigation can be through any of the following different techniques:

i. Manual irrigation

ii. Localized irrigation

iii. Surface Irrigation

iv. Drip irrigation

v. Sprinkler irrigation

vi. Lateral move irrigation

vii. Center pivot irrigation

viii. Sub-irrigation

Irrigation is an agricultural activity of significant economic importance. Only 20% of farming land is irrigated yet this area is responsible for 40% of the world’s food production. According to some estimates, the worldwide yield of rice, sugar cane, citrus, and cotton would reduce by 31%. The global production of cereal would also be set back by 47%. Irrigation contributes this much because of the relative stability it provides in agriculture. Rainfall is a variable source of water for growing crops. As a result, irrigation – when done efficiently – can fill deficits and also provide more to ensure crop survival.

Despite the importance of irrigation to fighting global food shortage, there are still existing deficiencies in irrigating more farmland. In Europe, for example, based on data from 2016 – of the 8.9% irrigable land available, only 5.9% was irrigated.  

The shortfalls in irrigation are a combination of physical water scarcity and economic water scarcity. The former is due to the actual shortage of water while the latter is a result of insufficient resources by way of infrastructure or human capital to utilize the available water.

In summary, these are some of the biggest problems faced by irrigation:

i. Depletion of groundwater sources e.g. aquifers

ii. Under-irrigation which can cause soil salinity

iii. Over-irrigation which can lead to chemical wastage and cause water pollution

The Role of AI in Transforming Irrigation

Agriculture consumes 85% of the world’s freshwater. It is only ideal that the water is channeled efficiently for such a noteworthy consumption.

AI can guarantee this efficiency in several ways. One of such ways is through the real-time automated monitoring of soil moisture to determine the need for irrigation. In this way, the delivery of water to crops can be tailored to actual need rather than just broad-spectrum delivery. This intervention can further be optimized as the machine learning algorithm understands more about how crops and the soil respond to irrigation within the context of environmental changes. AI can also learn from weather and climate data to optimize the delivery schedule of water to crops.

Beyond soil moisture content, AI algorithms can monitor groundwater and surfacewater content to make irrigation decisions. 

Weeds compete with plants for nutrients and moisture. In most cases, they win the battle against crops. Computer Vision technology in mechanized farming equipment or even Unmanned Aerial Vehicles (UAV) to identify and eliminate weeds before they pose challenges and lead to irrigation inadequacy.

Predictive maintenance is another important area of AI application where algorithms are designed to run periodic checks on sensory equipment data and make predictions regarding possible breakdown. If malfunction events can be prevented then inefficiencies in the irrigation process can be reduced.

Resources:

https://inc42.com/resources/how-ai-is-transforming-the-water-sector/

https://www.cdc.gov/healthywater/other/agricultural/types.html#:~:text=Irrigation%20is%20the%20artificial%20application,to%20the%20entire%20field%20uniformly.

https://wad.jrc.ec.europa.eu/irrigations

https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Agri-environmental_indicator_-_irrigation#Analysis_at_EU_and_country_level

https://www.worldbank.org/en/topic/water-in-agriculture#1

https://www.agritechtomorrow.com/article/2018/10/artificial-intelligence-and-its-uses-in-ag-irrigation/11094

Tanha T., Dhara S., Nivedita P., Hiteshri Y., Manan S. (2020). Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture 4(0) 58-73. https://doi.org/10.1016/j.aiia.2020.04.002.




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