HYDRIT

THE PROBLEM

• Agriculture is the largest user of the world's freshwater resources, consuming 70%.
• More than 1.2 billion people (20% of the world) live in areas of physical water scarcity, where there is not enough water to meet all their demands.
• A further 1.6 billion people live in areas experiencing economic water scarcity, where the lack of investment in water or insufficient human capacity make it impossible for authorities to satisfy the demand for water.
• Global issue with important and growing debate.
• Alternative strategies are sought for in order to avoid setbacks in the allocation of water resources.

HYDRIT is a water resource management system, designed for optimal water reservation and allocation in agriculture.

The project can be divided in two major parts:

1. Data management through an app
2. Mechanical irrigation system

1. Data management

We are planning to construct an app, easily accessible through smartphones and tablets.

1.a) Imported data

1.a.a) Index of every plant that can be cultivated. It will contain a practical information for the farmer, as ideal temperature conditions and soil moisture. Later in the process, we can enrich the index with the water consumption range per unit (ex. 800-1000 L/acre). The farmer (either professional or novice) will be able to assess then the ideal crop for his area based on the environmental conditions and the maximum quantity of water he is willing to consume in any part of the world.

1.a.b) Meteorological data. It will receive and take the probability of rain and other meteorological hazards (ex. hail) into consideration.

1.b) Extracted data

*The data will be provided in the form of easily interpretable compositions and graphs.

• Liters of water consumed per day in each field (if more than one).
• Ratio of consumption in relation to the total irrigation of the fields.
• Notification for an extreme meteorological phenomenon (e.g. hail).
• In a 2nd phase, a range of consumption will be emerged from every user of the app for every cultivation, expressed in a defined unit (e.g. liter/acre or liter/mm2 for smaller fields or gardens), helping compare the consumption between 2 crops and even use filters to access a list of plants with specific irrigation demands

2.a) Main principles of the irrigation system

We are using the soil moisture levels (%) as factor for regulating the irrigation system. A sensor covering a cultivation area measures the humidity of the terrain. When the humidity drops below a minimum %, an “actuator” (closed loop system) is activated which gives a command to a generator (programmed with slow start for low energy consumption) to initiate the irrigation. The watering process terminates when the humidity rises to a certain desired level (ex. the median of the desired moisture %).

Since the farmer selects his crop in our app, the sensor receives the irrigation pattern (% of moisture) from our app index.

There can be many sensors covering multiple areas, fields and crops and get coordinated from the same app. Each sensor sends its measures to a concentrator (gateway) which sends them to a cloud server to be stored.

In addition, in a high probability of upcoming rain (% that we can pre-set in the app) a command will be sent through the sensor to pause the irrigation routine.

BENEFITS

a) Main benefits

• Personalized watering pattern depending on the desired crop: we use only the water necessary for maximizing the production.
• Avoidance of unnecessary water supply and field overflooding in an imminent rain.
• Easily applicable all over the world.

b) Secondary benefits

• Use of low energy consumption devices such as microcontrollers which in long term, will bring profit and overcome the system installation costs.
• Data extraction of the updated databases for statistical studies in the field of agriculture.
• Improvement and increase of the primary production

• Ability of simultaneous coordination of various irrigation patterns.

WE <3 OPEN DATA BUT...

BUT we certainly believe that the people who produce them, should receive a minimum reward for giving away this valuable treasure for the prosperity of all! So, the extracted data of our system (as described in the section 1.b) above), will not only be utilized as a means of training the system's machine learning algorithms, but will also be used as an income resource for the user of our system! Our system will take special care of paying back a suitable financial reward, to any farmer who will be generous enough to share his precious data.

RESOURCES - FURTHER NOTES

Use of NASA's open data:

HYDRIT will make use of NASA's Earth data (https://earthdata.nasa.gov/), at least regarding the input of the meteorogical data mentioned in the 1.a.b. section above.

https://www.researchgate.net/figure/A-typical-WSN-...