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Ag AI. Fad or gamechanger?

Many consumers see farming as a labour-intensive manual industry with low costworkers, and low productivity. But its worth reflecting that just a century ago around one fifth of the workforce were employed in agriculture, compared to a little over one per cent today. Farming has seen constant innovation, where larger farms with new farming systems and methodologies have seen the amount of food per worker reach record levels.

So perhaps it’s more reasonable to see the use of technology, robotics and AI as simply further innovation in a relentless and long-term drive for efficiency. So, what are the key changes emerging? Here are some of the more impactful ones:

Image recognition

Employing the use of agricultural drones help increase crop production and monitor crop growth. Drones that use AI help farmers scan their fields and monitor the production cycle to enable data driven decisions to be made about intervention on issues like irrigation challenges, soil variation and pest/fungal infestations.

Driverless tractors

A driverless tractor is an autonomous farm vehicle that delivers a high tractive effort at slow speeds, for the purpose of tillage and other agricultural tasks. It is considered driverless because it operates without the presence of a human inside the tractor itself.Just like other driverless vehicles, the driverless tractors are programmed to independently observe their position, decide speed and avoid obstacles such as animals, human beings or objects in the field performing their task.

Data analytics

By using software to analyse multiple data sources such as climate conditions, soil type and condition, and invasive risks, AI supports farmer decision making onvariables like seed choice, time of planting, and crop protection which can significantly improve farm investment ROI.

Robotics picking technologies

Until recently the picking of salads and soft fruit has been a 100% human activity. The University of Cambridge has recently trialled its Vegebot robot on iceberg lettuce, using machine learning to identify and harvest the crop. The prototype demonstrates how the use of robotics in agriculture might be expanded and could help with labour shortages and reduction of food waste, according to the university.

Automated irrigation systems

Automated irrigation systems are designed to utilise real-time machine learning to constantly maintain desired soil conditions to increase average yields. Not only does this require significantly less labour and have the potential to drive down production costs, but with 70% of the world’s freshwater used for agriculture, the ability to better manage how it’s used will also have a huge impact on the world’s water supply.

Crop health monitoring

Similarly, conventional crop health monitoring methods are incredibly time-consuming and are generally categorical in nature. In comparison, automated detection and analysis technologies – such as hyperspectral imaging and 3D laser scanning – are substantially increasing the precision and volume of data collected. With the ability for microscopic data collection, farmers are able to produce diagnostics specific to individual plots or even single plants.

Facial recognition

Facial recognition is nothing new, however this is now extending beyond humans into the world of domestic cattle. Whilst ‘smart’ cattle monitoring is more commonplace, existing systems largely require the use of physical tracking devices. Facial recognition technology eliminates the stress of fitting these devices, allowing easy monitoring of an entire herd with minimal interaction, enabling individual monitoring of group behaviour, early detection of lameness and accurate recording of feeding habits.

Ag AI looks like it’s here to stay….

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