Get more from less

Project Info

Zirkarta thumbnail

Team Members


Sonja , Dylan

Project Description


Background

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.

Problem

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.

Solution

The solution uses data to:

• inform farm planning decisions, and
• power automation during farming operations;

to enable agriculture to get more from less.


#iot #open data #data fusion #artificial intelligence #agriculture #planning #operations #manual data #geospatial

Data Story


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.

Find out more

https://drive.google.com/file/d/1xqppezq5_XFLA1jMCqkEia7_-13U1nkj/view?usp=sharing


Evidence of Work

Video

Project Image

Team DataSets

ACT Soil Landscapes

Description of Use Static site data that is fused with dynamic site data, weather, management and yield data to make predictions about crop yield and make management recommendations.

Data Set

ACT 2015 10m contours

Description of Use Static site data that is fused with dynamic site data, weather, management and yield data to make predictions about crop yield and make management recommendations.

Data Set

Challenge Entries

IoT insights for better regional agribusiness at scale

How might we harness open data and IoT insights in near-real-time to enable local agribusiness and farms in our regions be more productive and sustainable at scale? How can we share private IoT data and regional open data for an overall more productive and sustainable agribusiness?

Go to Challenge | 13 teams have entered this challenge.

The digital future of agriculture

How might we use data and digital technology to make agriculture more sustainable, more ethical, and more efficient?

Eligibility: Must use at least one open dataset.

Go to Challenge | 16 teams have entered this challenge.