Between 2021 and 2100, the human population is expected to increase 38% from 7.9 Billion to 10.9 Billion.
At the same time, climate change and urban expansion is predicted to reduce the area of arable land by up to 41%.
Expanding agriculture into areas currently not used for agriculture will have a negative impact on natural habitats.
Increasing the area of agricultural land will also increase water, energy and pesticide use.
How can agricultural food production be increased to meet the needs of a growing population from a reduced land area while minimising water, energy and pesticide use?
This requires getting more yield from:
• less land,
• less water,
• less energy, and
• less pesticides.
The solution uses data to:
• inform farm planning decisions, and
• power automation during farming operations;
to enable agriculture to get more from less.
Informing planning decisions
Static site data (e.g. elevation) sourced from open sources, dynamic site data (e.g. soil moisture) from IoT, Weather data from IoT and manually entered management treatment and crop yield data is fused. This fused data is used in predictive and optimising AI with results visualised as statistics, graphs and maps.
Predictive AI enables crop yield to be estimated under different management and weather scenarios. Optimising AI generates management treatment recommendations to maximise crop yield.
Powering automation during farming operations
Optimising AI identifies the values of dynamic site variables, such as soil moisture, associated with maximum yield. IoT devices compare these against the prevailing conditions, and automatically turns autonomous farm equipment, such as irrigators, on to ensure these values are achieved.
Limits on water, energy and pesticide use can be set. IoT devices monitor the use of these resources and automatically turns farm equipment off when these limits are reached.
Relationship between planning and operations
The planning functionality helps inform the operations functionality and the results of operations informs planning. In this way, there is a feedback loop between planning and operations.
Scale of application
Can be applied to a single farm, multiple farms or whole regions including using data from multi-farm or regional Low Power Wide Area IoT Networks (LPWAN) such as Telstra LPWAN networks.
The system is cloud based running on a service such as Microsoft’s Azure.
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