Food & Agriculture

Climate change, population growth and food security have pushed for innovative technological solutions to farming.

The increased access to faster internet connections, investment in connectivity infrastructure and wider availability of smart phones provides a rock bed for the uptake of digital technologies and especially AI among farmers even in developing countries.

If applied correctly, these technologies can help the food industry boost efficiency, reduce food waste, improve food safety and support sustainability.

Species recognition

Traditional methods to classify species from images are often very superficial, looking at simple features like color and shape of leaves.

More recent computer vision techniques can give more accurate and faster results, by looking at finer-grained features like vein morphology.

Land monitoring

Land monitoring helps measure and report on forests and land use, for improved climate change mitigation plans and better-informed land-use policies.

Machine learning can provide comprehensive image processing capabilities and enable detection of small-scale changes in forests, such as those associated with illegal or unsustainable timber harvesting.

Supply chain optimization

AI can help make the supply chain more transparent and help monitor every stage of the process. It makes price and inventory management predictions and tracks the path of goods from their source to the place where consumers receive it.

Predictive maintenance and remote monitoring

Predictive maintenance can help figure out the time-to-repair and cost-to-repair indicators through categorizing issues and making predictive alerts. Timely repairs can save money.

To perform remote monitoring on complicated mechanisms, you can make a digital twin of a machine that will show you the performance data on parameters and manufacturing processes and boost the throughput.

Customer insights

Customers' tastes are diverse and change over time. Data analytics can help identify preferences, predict demand, and correctly manage the acquisition of supplies.

Defect detection & predictive maintenance

Being able to predict defects and failures using AI can reduce unplanned downtime on the shop floor, and significantly improve product quality, throughput, and yield.

Sensors in machines create a continuous stream of data on their use and state of maintenance. Predictive maintenance uses data analysis and algorithms to predict the need of maintenance, helping prevent unnecessary downtime.

Disease detection

Disease detection is a critical task for farmers. Crop diseases are a major threat to food security, but many parts of the world lack the necessary infrastructure to do rapid identification.

Advances in computer vision and the recent ability to deploy these algorithms on smartphones has paved the way to an easier smartphone-assisted disease diagnosis.

Automate back office tasks

There are numerous logistics related forms like bill of lading from which structured data needs to be extracted. Most businesses do this manually.

By automating the process, your company can auto-generate regular reports that are required to inform managers and ensure everyone in the company is aligned. Easily auto-generate reports, analyze their content, and email them to relevant stakeholders.

Voice assist

Voice control gives its users accurate access to information at any stage of the production process, through hands-free, intuitive and efficient interactions.

Workers can connect to other devices in the factory floor and issue key instructions with their voice. This will speed up internal processes, and improve overall productivity.

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