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Aston University researchers to use AI to help Ghanaian farmers monitor crops in real time

Aston University researchers are to use AI to help increase the number and range of different crops grown in Ghana by developing real time monitoring systems.

Due to its climate, the country has to import around £1,147 million of fruit and vegetables annually. The University has entered a knowledge transfer partnership (KTP) with Tropical Growers, a company which cultivates plants in a nutrient solution, without soil, also known as 'hydroponics'.

As well as reducing the amount of water needed, the technique allows production of a greater range of crops than Ghana's traditional 'open field' farming, such as salad and fruit.

A KTP is a three-way collaboration between a business, an academic partner and a highly qualified researcher, known as a KTP associate. The UK-wide program helps businesses to improve their competitiveness and productivity through the better use of knowledge, technology and skills. Aston University is a sector leading KTP provider, with 80% of its completed projects being graded as very good or outstanding by Innovate UK, the national body.

Tropical Growers currently needs to develop a digital platform for plant health monitoring so it can make sure its plants are cultivated in the best possible environment. It needs a system integrated with an evaporative cooling system, so that plants can withstand Ghana's extreme summer temperatures. It also needs it to function via an AI framework to allow automatic, continuous micro-adjustments to the plants' nutrition, temperature and humidity.

The Aston University team will create a system of devices, including cameras, humidity monitors and tiny weather-stations, which will monitor and control the environment inside a Tropical Grower's greenhouse.

The network will collect and analyze data, including real-time images of the crops, and will ultimately communicate with the evaporative cooling system, which itself will be powered by solar energy. A phone app will be developed to estimate crops' water and nutrient requirements, as well as for predicting the yield.


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