Carl Menke

Carl Menke

By Carl Menke

The world is increasingly a digital space. Between 1995 and 2017, the number of people using the internet has grown from 16 million to 3.7 billion, of which 2 billion are from countries traditionally defined as ‘developing.’ The possibilities for creating and sharing data is incredible, and has real potential for improving project impacts and adoption, via i) larger audiences for agricultural extension, ii) Lower-cost outreach, iii) Accelerated data collection and interpretation

The creation of all this data however still requires interpretation, which usually requires pain-staking hours of work.  The problem to date with using machines to automate previously human tasks is that machines are fundamentally dumb, being programmed to do one task, seeking specific pre-determined inputs and limited by the capability and foresight of the designer(s). However, the growing rise of AI may change this. 

AI, is not only a three toed sloth, but refers to ‘artificial intelligence’ that utilizes ‘adaptive learning systems’ to new knowledge, interacting with and interpreting information. With each interaction the AI gets smarter, predicting responses, testing and then identifying changes to parameters to find the desired response. These traits in a human are often referred to as “deductive reasoning, planning, learning, perception” (etc…), so we really need to start being nice to our computers. Simply put, machines that learn from a range of interactions it has received and memorizing what caused different responses to adapt for the desired result. Sounds pretty smart!


Figure 1 Ai, coming to disrupt the way we run projects... source: birdquest

A quick search of novel solutions to on-going problems in agricultural development will find a search field awash with innovations applying aspects of AI:

  • automated extension: advice tailored to your crops, location, resources using smart and adaptive step logic 
  • rapid disease diagnosis: use of phones to take photos of suspicious crop or livestock changes to then be automatically assessed for known diseases and/or identify new ones 
  • Affordable mapping: attaching increasingly cheap and powerful smartphones onto helium balloons attached with a string to take photos of land to determine soil condition 

Development organizations such as the Bill and Melinda Gates Foundation and USAID are investing heavily in innovative, technology that apply aspects of AI (Socialcops - Big data, Crop Manager - extension, Digitalgreen - community led extension, iCow - extension and record keeping, - low-cost imagery, KUMU - stakeholder mapping). There is a good argument for agricultural research for development to invest greater skills and money in this field if we are to adapt to the future. And of course, applying AI systems effectively requires more than grafting western designed systems to in-country context – but working with partners to identify genuine problems to add value. So we don’t need to get worried about our jobs just yet, and if you are, here’s an article about Universal basic income to get you excited (or just learn to code). 

Steps for the future:

  1. Seeking, mapping and brokering partnerships with innovative IT upstarts with coding skills and in-country partners
  2. Hiring research experts in coding and AI specific technologies
  3. What else?

It is important to note that it is more than just plonking AI in a country, you need to train people, make sure it’s a value add and that the stakeholders see it as a value add. Then it needs to be resourced beyond the life of the project.

Aid projects are generally responsive to partner countries priorities, and as of yet I haven’t heard any clamoring for help with implementing AI systems in agricultural contexts. However,

While ACIAR doesn’t work in intelligent computer systems, we certainly do have a number of non-specialist jobs that are begging to be automated (provided the task can be completed as well as a human) (and projects that could benefit from improved access to information and extension – freed from the restriction of underfunded partners).

Examples of areas begging for smart automation: 

  • Writing of contracts and legal agreements  
  • Personalized extension – often massively underfunded in developing nations 
  • Trawling global big data to identify best practice extension methods
  • Improved accuracy of weather predictions and associated likelihood of crop success
  • Recognition technology utilized by Google Lens: plant and livestock deficiencies and diseases


Views expressed in this blog are the authors own.

Thursday, 15 June 2017 00:00

Graduate thoughts on reduced aid funding

Australian researchers in international agriculture need to re-focus on communicating the value of our research in a time of reduced aid funding 1, 2, 3. As of 2016, Australia contributes 0.32% of our gross national income (GNI) to aid despite a UN country target of 0.70% of GNI. Compared to the 28 wealthy OECD member nations that give aid, Australia ranks 16th. Australia’s international aid has reduced in the last year, at a time when 22 of the 28 OECD member nations increased their contributions.

While some critics of Australia’s aid spending may applaud this, this ignores the fact that a majority of this funding is invested in Australian institutions and expertise. A recent analysis shows that of the funding provided for forestry research for development projects, 56 % of this funding is injected into Australian institutions and expertise. Therefore reductions in aid spending is a double edged sword, reducing investment and employment opportunities for the brightest Australians in science, agriculture and economics. Without renewed opportunities for employment and job security, fewer will study to enter this crucial field of work.

The Australian government routinely re-distributes the departmental budget pie, and aid funding should be scrutinized to ensure improved impact. However the ramifications of reducing the total IQ contributing towards improved food security in a time of global warming may have disastrous impacts in the mid-long term. Therefore this is an important time for Australian researchers in agriculture for development to re-focus on communicating the importance of aid spending to the Australian tax-payer, and perhaps re-evaluate our aid strategy. 


One of many Australian researchers and project leaders attending an Indonesian project review

The important question is this; if Australia were to follow suit with our OECD peers and re-invest in Australian expertise and developing our regional neighbours, how should we do this? It seems that although there is still a strong demand for traditional science research (i.e. developing vaccines and disease resistant crops), greater investment in the capacity building of knowledge management of in-country institutions is of growing importance.  Another avenue perhaps is the greater investment in areas of research such as information communication technologies (ICT) and artificial intelligence (AI) to accelerate the research process in international agricultural research. AI has already helped to better utilize the vast amounts of available data, and improve the access to and application of this information (socialcops; CGIAR BigData). What else? 

Views expressed in this blog are the authors own.