Satellite Data

Precision agriculture refers to the management of farms with satellite and other data and technologies. The resulting maps provide highly detailed information about the topology and condition of farms, down to the individual plants.

Other common terms for precision agriculture include satellite farming and site-specific crop management.

A sea-faring vessel using a variety of sensors to manage its regular processes and navigation is a smart ship. Uses of this IoT technology range from single vessel to fleet management. However, even management of a single ship involves using massive amounts of data.

Sensor-enabled vehicles use an autonomous navigation system to move through the atmosphere or down roads and terrain without direct, continuous human direction. Vehicles that use autonomous navigation systems include robots, spacecraft, ships, airplanes, and cars.

Naturally, there is a lot of overlap between this use case and remote navigation systems.

Smart farming refers to the use of connected technology and artificial intelligence in the field of agriculture. It is also referred to as smart agriculture, agritech, agrotech, or even agro technology. However, the terms agritech and agrotech include the fields of horticulture and aquaculture.

Fair trade certification is the confirmation from an official organization that a product was produced and sold according to fair trade principles: mainly, fair compensation to farmers for their labor.

Additional goals of fair trade networks include environmental sustainability and women’s empowerment.

A power grid resilience system monitors the status of a power grid and the threats to its function.

From crop or livestock production to the consumer, agricultural waste is generated. However, a good agricultural waste management system enables people to prevent, reduce, or reuse this waste.

A store’s location directly impacts supply, profits, marketing, and almost every other aspect of store performance. Much can be learned from the performance of different types of retail stores in different neighborhoods, countries, or even climate areas. Therefore, retailers use geospatial data machine learning models to plan store locations and predict profits.

AI has been used to improve supply chain and demand forecasting for a couple of decades. Demand planning applications use data driven algorithms to take historical data and use it to forecast. Demand forecasting can include promotion planning or stock and sales forecasting. The machine looks at the forecast, compares it to actual shipments, and suggests alternatives and optimization options. Over time, many companies started doing more specific forecasting for specific regions, products or stores or for more granular points in time. Both retailers and their suppliers (CPG companies or 3rd party shipping and supply chain companies) use their data to help conduct automated decision making within the supply chain.