Case studies
Faster, fairer, smarter: Cropify’s AI grain grading innovation
Posted on 19 May 2025 by Dr Miguel Balbin
In the heart of South Australia's burgeoning agricultural technology (AgTech) sector, Anna Falkiner and her husband, Andrew Hannon, are pioneering a transformation in grain quality assessment through their startup company, Cropify. Established in 2019, Cropify aims to eliminate subjective testing in pulse and grain crops by leveraging artificial intelligence (AI) and machine learning technologies.1
Traditional grain classification methods heavily rely on human visual inspection, introducing variability and potential inaccuracies in quality assessment. This subjectivity can lead to disputes between sellers and buyers, and impacts the efficiency of the grain supply chain. Recognising these challenges from their extensive backgrounds in agriculture and marketing, Falkiner and Hannon sought to develop a more objective and reliable solution.
“We spoke to a lot of people, and someone suggested that [computer] vision had come a long way,” said Falkiner. “We looked at horticulture and what was being done in [that field] and then approached the Australian Institute for Machine Learning (AIML) and had a proof of concept done.2

Cropify co-founder and CEO Anna Falkiner and co-founder and COO Andrew Hannon. Photo credit: Cropify.
Leveraging an AIML grant from the Government of South Australia in 2020, Cropify worked with former AIML engineers Sam Bahrami and Aaron Lane to develop an AI-driven software prototype capable of analysing grain and pulse quality with high precision. Lane is now the Chief Technology Officer at Cropify.
The prototype’s initial focus was on small red lentils, a crop significant to South Australia's economy but notoriously difficult to classify given its small size.
“Sam and I put together the prototype over 6-8 weeks,” said Lane. “That prototype demonstrated that commercially viable results were possible. We handed over the working prototype and training pipeline for Cropify to develop further.”
“It was great to work with a client that listened closely to our advice and was willing to work on building the high-quality datasets that their use case needed. The SME Program let us really focus on getting the best result for our clients without being encumbered by research or IP (intellectual property) ownership concerns,” Lane continued.
“While the prototyping work was relatively fast, building the whole solution from scoping to delivery took time and perseverance. We were very pleased when Cropify was eventually able to leverage that prototype to gather support for their vision from the industry and raise their seed funding.”
By utilising high-resolution imaging and advanced algorithms, Cropify's technology can now assess an industry-standard sample of lentils in approximately 90-seconds—a substantial improvement over the traditional 24-minute manual process.3
Cropify's innovative approach has also garnered support from various industry stakeholders. The South Australian government's AgTech Growth Fund provided financial backing, facilitating the development of prototype hardware and software. This support enabled Cropify to conduct extensive performance assessments on their technology, achieving accuracy rates exceeding 98% in classifying lentil samples.4
In September 2024, Cropify secured $2 million AUD in funding from investors, including Australian venture capital firm Mandalay Venture Partners and Singapore's Hatcher+. This investment aims to accelerate the commercialisation of Cropify's technology within Australia, with plans to expand into international markets.5
Both Cropify’s Senior Machine Learning Engineer, Dr Antonios Perperidis, and Falkiner participated in a video of AIML collaborators as part of AIML’s Industrial AI Program launch event in June 2025. In the video, they both offer advice to small and medium enterprises (SMEs) on how to best adopt AI into their operations.
“The advice I’d give to industry looking at AI adoption is to actually look at what your problem is, and [ask if] AI is the solution,” said Falkiner. “Don’t look at AI for the sake of having AI. It has to be the right fit for your business.”2
“[My] advice would be to… understand [your problem] and try to keep an open mind on the solution. Avoid looking [for] faster horses. You’re looking for something new,” said Dr Perperidis.2
References
- ‘Anna Falkiner of Cropify: smart classification for objective testing of pulse & grain crops’, AgriDigital, https://www.agridigital.io/post/anna-falkiner-of-cropify-smart-classification-for-objective-testing-of-pulse-grain-crops
- ‘Launching the Industrial AI Program’, Australian Institute for Machine Learning, https://youtu.be/UKMYkJypaRo?si=6n-zJDnviV11JmHW
- ‘Meet the AgTech innovators with the fingers on the pulse’, Lot Fourteen, https://lotfourteen.com.au/news/making-the-grade/?utm_source=chatgpt.com
- ‘Smart classification of small type red lentils’, Department of Primary Industries and Regions South Australia, https://pir.sa.gov.au/__data/assets/pdf_file/0005/440537/agtech-growth-fund-cropify.pdf?utm_source=chatgpt.com
- ‘Aussie AI-driven grain grading company Cropify reaps $2 million’, Forbes, https://www.forbes.com.au/news/entrepreneurs/aussie-ai-driven-grain-grading-company-cropify-reaps-2-million/?utm_source=chatgpt.com
Construction maintenance using AI-driven insights
Posted on 19 May 2025 by Dr Miguel Balbin
Andrew Hannell, founder of Digital Constructors in Adelaide, South Australia, has been a long-time advocate for integrating digital technologies into the construction industry. With over 25 years of experience in architecture, engineering, and construction, he aims to leverage digital tools to enhance infrastructure assessment, reduce risk, and improve on-site decision-making.
One of the greatest challenges in his industry is the difficulty of visually assessing and documenting damage and potential hazards on infrastructure projects. Hannell’s interest in this area was initially piqued by the SteamRanger Heritage Railway on South Australia’s Fleurieu Peninsula, which needs to be regularly monitored to assess the condition of deteriorating tracks and to determine whether nearby vegetation, such as dried weeds, poses any fire risk.
Relying solely on human inspection introduces subjectivity, which could lead to inconsistent evaluations, reporting inaccuracies, and unnecessary increases in maintenance costs.
“Construction is a very expensive, very conservative business,” said Hannell. “During both construction and operational phases, there are many, many inspections that are required. On many projects, that’s done entirely manually. [It’s often] someone walking around with a clipboard.”
“Something as simple as counting potholes… and recording where they are could save millions, or hundreds of millions of dollars,” he continued. “If that can be automated, it will save a huge amount of time [and] add other benefits.”1
Hannell recognised the potential for AI-driven defect detection to bring greater objectivity and consistency to infrastructure assessments, pinpointing not only the location of defects but also evaluating their severity based on measurable criteria.
To explore this potential, he collaborated with AIML engineers Sam Hodge and Aaron Peter Poruthoor in 2022 to develop a practical solution tailored to the realities of infrastructure monitoring.

Andrew Hannell, Founder of Digital Constructors (front), together with AIML engineers Aaron Peter Poruthoor (left) and Sam Hodge (right). Photo credit: Digital Constructors.
The team committed to building a minimum viable product (MVP) within a 12-week sprint, resulting in ConstructAI, a camera-based machine learning platform that uses computer vision to automate critical infrastructure monitoring procedures when mounted on the front of a locomotive. Using data derived from SteamRanger footage provided by Hannell, the team trained the model to accurately detect and classify various issues.
“Based on testing during development of the MVP, the key benefits included rapid data collection that was many magnitudes faster than alternative methods,” said Hannell. “The tool also produced high-quality data that was non-subjective,”
Following this initial success, the MVP was refined over several phases of iterative testing to improve accuracy and reliability.
From the outset, AIML designed the system to keep Hannell and his team in the loop. The software was built to be accessible and easily maintained over the long term, and included thorough documentation to support future adaptation and development.

A prototype of ConstructAI equipped on the SteamRanger can identify people, infrastructure, and vegetation.
While ConstructAI has not yet been deployed commercially, Hannell sees enormous potential for its application, especially given the growing interest in artificial intelligence (AI) and machine learning even in the conservative construction sector. His collaboration with AIML has also provided a valuable framework for future AI-driven innovation in the sector.
“Working with AIML was a great experience. The engineers were practical and flexible, and we worked collaboratively on the project,” he said. “Although both the initial concept and final developed solution were quite simple in technical terms, AIML introduced valuable ideas and innovations to the process,” said Hannell.
“On a personal level, I learnt a lot and enjoyed working with AIML.” He continues to advocate for AI’s ability to significantly improve safety and reduce costs in his industry.2
Sam Hodge, one of the engineers on the project, echoed the positive sentiment.
“Andrew [Hannell] was a dream customer,” said Hodge. “[He] understood the value of good data and that the simple things can often be the best value for the user story that needs to be solved.”
“He took an off-the-shelf computer vision model and applied it to a real-world problem of maintenance of infrastructure that would have been prohibitively expensive to do manually,” Hodge continued. “The automation means that maintenance of the heritage railway can continue far into the future.”
In June 2025, Hannell participated in a video of AIML collaborators as part of AIML’s Industrial AI Program launch event. In the video, he encourages small and medium enterprises (SMEs) to explore using AI in their operations.
“Start at a purely business level,” he said. “What costs [you] the most money? What takes the most time? Where are the biggest risks? Where are the biggest opportunities? [Then] work backwards from there. The answer or potential answers to those sorts of issues can certainly be found in AI.”1
Visit Digital Constructors' website
References
- ‘Launching the Industrial AI Program’, Australian Institute for Machine Learning, https://youtu.be/UKMYkJypaRo?si=6n-zJDnviV11JmHW
- ‘Why is AI important to construction?’, Digital Constructors Blog, https://hannellbim.com/2024/02/05/artificial-intelligence-in-construction/
Technology so good it can predict shadows: AIML's deep learning enhances 3D maps offered by SA company Aerometrex
Originally published in AIML's Machine Learning Capability Report, December 2021
As part of the South Australian government’s 2019 program of investment in SMEs, AIML worked with geospatial tech company Aerometrex to create enhanced 3D data products for clients in city planning, development, urban design and regional councils.
Adelaide-based Aerometrex’s high-resolution 3D models are ideal for developing new artificial intelligence and machine learning products.
AIML worked with Aerometrex to boost mapping products with deep learning capability. The new technology is so detailed it reveals shadows cast by buildings at different times of the day, and the heights of individual structures.
“Aerometrex remains at the cutting edge of global 3D modelling, and through this project we are taking our solutions to the next level,” says Fabrice Marre, Aerometrex’s Geospatial Innovation Manager. “AIML are helping us solve real-world problems.”
Enhanced 3D maps are expected to become the standard tool for developers and planners around the country who are making decisions around what developments should go ahead, or what designs need to be modified.
“Shading caused by a building is one of the big factors councils consider when approving new buildings,” says Marre. “With our maps, city planners are able to see with much more detail exactly how the surrounding areas are going to be impacted.”
Other industries stand to benefit too. Solar installers will be able to assess more accurately the optimal layout for solar panels on a roof, landscapers will be able to see where and how shade from large trees will fall at different times of year, and property developers will be able to better plan their projects to maximise the use of space.
Deep learning for better maps
While Aerometrex had developed capabilities for labelling items in 3D maps, their initial approach was restricted by the amount of data that could be labelled, and relatively weak software capability.
“We wanted to create maps with infinitely more detail, and to provide our customers with maps that enabled them to search for very specific information,” says Marre. Aerometrex pulled in AIML’s expertise to apply deep learning to create a solution. Machine Learning Engineer Sam Hodge was the AIML lead on the project.
“Because Aerometrex already had so much information, ‘teaching’ the algorithm was relatively straightforward,” says Hodge. “We were able to take all the information they already had, break it down into as much detail as possible then build a model that matched it all together.”
The beauty of the approach lies not just in the amount of detail captured, but in the scalability.
“Once we have ‘taught’ the algorithm enough, we no longer need to label every object,” explains Hodge. “The algorithm will keep learning for itself, drawing context and information not only from individual pixels but those surrounding it, and the tens of thousands of other pictures it has examined.”
The end result is a 3D map that contains an incredible amount of derived information. Users are able to search areas for specific detail – from single blocks to entire suburbs or cities – that can help them make better decisions whether it’s related to planning, developing, building or marketing. AIML continued to work with Aerometrex in 2021.
Established in Australia in 1980, Aerometrex has a strong national and international reputation as a leading practitioner of aerial imaging, photogrammetry, 3D modelling and LiDAR surveys. The company provides professional, accurate digital image mapping and geospatial engineering solutions for clients in government and the private sector. In 2020 Aerometrex had its strongest financial year to date, launched Aerometrex USA and grew its workforce by 33 to reach a total of 116 employees.
How machine learning expertise helps small banks keep customers safe
Originally published in AIML's Machine Learning Capability Report, December 2021
As part of the South Australian government’s 2019 program of investment in SMEs, AIML worked with Lot Fourteen-based business automation company Neo-Analytics to create smarter software for regulatory compliance monitoring in banks and financial institutions.
Adherence to strict regulations and standards is vital for banks to keep financial risks low and ensure safety of clients’ money. However small financial institutions with few staff and limited technical capability can feel overwhelmed by this burden of compliance.
Machine learning offers a solution.
“One of the requirements of operating a financial services institution is to follow local and national government laws and regulations – this is called regulatory compliance,” said Rick Rofe, founder at Neo-Analytics.
“This is a huge job involving lots of data, and so we worked with AIML to develop automated processing capability for regulatory compliance.”
Neo-Analytics now applies algorithms that deliver reliable and accurate results, providing smarter more intelligent software for their client base.
“We are now working with three banks, one of which has our products in production,” Rofe said. “We hope these improvements will soon allow us to employ our contractors permanently as adoption of our machine learning products increases.”
High burden for small institutions
Financial services institutions include banks, building societies and credit unions – these are regulated entities that can carry on banking business, including taking deposits from customers.
Such institutions are compelled by law to have robust and accurate measures in place regarding risk, including detection, measurement, reporting and management. The implied dollar amount for regulatory compliance totalled A$5.4 billion in 2016, representing 24% of community bank net income.
Data management tools like Excel don’t cut it for financial management anymore – institutions need new approaches that offer analytic flexibility, scale and automation. For smaller institutions, costeffectiveness is also vital.
AIML applied machine learning models to improve Neo-Analytics’s regulatory compliance monitoring.
“AIML was instrumental in modelling our compliance needs, and they assisted our developers to implement machine learning algorithms from scratch,” said Rofe.
“AIML used the data we made available to them, and built a predictive model for regulatory compliance that proved to be accurate and met our requirements.”
Neo-Analytics is a finance RegTech innovator focussed on artificial intelligence and machine learning, credit monitoring and data management. Based at Adelaide’s Lot Fourteen innovation precinct, Neo-Analytics helps financial institutions survive and thrive in a market of accelerating and complex change – a market that has become exponentially more challenging due to recent global crises. Their primary focus is around AI-driven features that solve real problems for customers and creating better outcomes.
Yes, I'll endorse that: how AI helps Adelaide start-up Pickstar match celebrities with promotional opportunities
Originally published in AIML's Machine Learning Capability Report, December 2021
As part of the South Australian government’s 2019 program of investment in SMEs, AIML worked with start-up Pickstar to apply machine learning and data analytics in a technology platform that matches customers with celebrities for promotional opportunities.
Pickstar is an SA business that allows customers to use an online form to pick from a range of celebrities to be guest speakers and brand ambassadors.
CEO and founder of Pickstar James Begley said AIML has been helpful in providing a service that will be able to effectively pair up customers with the right stars within the client’s budget.
“The question that we answer as a business is, who can I get for my budget?” Begley said.
“The work that the AIML does allows us as a Pickstar platform to serve up and recommend the best available talent for someone’s brief and budget and that can only happen with heavy investment into machine learning and data analytics to underpin the recommendation engine.”
Begley said machine learning will help Pickstar to achieve faster and better results for their customers.
“AIML have provided us a road map but also a prototype, an actual tangible early-stage product that we are going to use and commercialize – this is taking university smarts and bringing it into the real world for commercial application,” he said.
“For us the investment into machine learning and data analytics is only going to increase, so if we can maintain that relationship with the institute we will be very pleased to follow on.”
Data is king
Dr Grant Osborne, the Lead Machine Learning Engineer at AIML, said Pickstar could see how important data is and how it can be applied to improve the experience for everyone involved.
“The guys at Pickstar are really on it when it comes to seeing that data is important, and knowing if we have this kind of opportunity available for a talent these are the most appropriate jobs,” Osborne explained.
Osborne said they try to make the process simple and understandable for companies like Pickstar.
“We build demonstrators that we can put straight in front of the clients, they’ve got a demo app where they can see all the data and see what the predictions and recommendations type engines will be able to do for them,” Osborne said.
“We’ve been working with Pickstar to essentially help them build a more data-driven business. We’ve been looking at data sets, helping to identify the most important element of the data, building dashboards as well as doing machine learning around prediction and the kinds of talent they will be recommending for certain jobs. Data is king.”
Based in South Australia, Pickstar was started in 2013 by former AFL players James Begley and Matthew Pavlich. The platform hosts a database of sports players and celebrities that is searchable by potential customers seeking to book a star to speak at an event or endorse a product or brand. Pickstar recently opened offices in the USA and the UK to capitalise on new global opportunities with major sporting institutions.
Blockbuster AI behind the perfect Hollywood face
Originally published in AIML's Annual Report for 2021 by Kurtis Eichler and Eddie Major
In 2020, millions of people across the globe experienced the very latest in South Australian AI technology in an industry worth more than $100 billion. And if it all went according to plan, they probably didn’t even notice it at all.
AIML researchers teamed up with Adelaide-based visual effects company Rising Sun Pictures (RSP) to use AI to create effects for some of Hollywood’s biggest blockbusters, including Marvel Studios’ Shang-Chi and the Legend of the Ten Rings.
This involved creating an AI method of replacing the faces of stunt performers in combat scenes with those of the lead actors. Computer vision researchers Dr Ben Ward and Dr John Bastian worked on the film with RSP, before joining the VFX studio as full time employees in October.
“Rather than the traditional 2D and 3D face replacements typically used in highintensity action scenes, the team used an AI deepfake method,” Dr Ward says.
Deepfakes are synthetic media where a person in an existing image is convincingly replaced (or faked) with the likeness of someone else, using deep learning — a type of AI that learns from data and uses multiple software layers inspired by our brain’s own network of neurons.
For Shang-Chi, this involved around 30,000 facial images across five characters, training five machine models in more than four million training iterations. The models were used for around 50 face replacements in six key action scenes.
“AI can help artists accomplish incredible artistic effects without the tedium of executing every frame in a long sequence,” Dr Bastian says.
Dr Ward and Dr Bastian are currently using their facial replacement method on three new film projects.
Tony Clark, RSP’s managing director, says AI can speed up delivery of visual effects and relieve artists of tedious, time consuming work.
“AI holds great promise for visual effects applications, especially in terms of accelerating labour-intensive tasks and augmenting human creativity,” Clark says.
Our early work with AI has produced spectacular results and we are eager to push development further.” While AI might help bring the creative magic of Hollywood to life, the reality is show business is exactly that, business.
For RSP, Clark says the collaboration with AIML helped the company generate $1 million in increased revenue with an additional $3 million forecast in 2022.
“Collaborating with AIML has enabled us to continue to deliver amazing images, and reinforces to global studio executives that RSP is among the best in the world at embracing and implementing advancements in technology such as AI,” Clark says.
Pivotal needs in Industrial AI
According to a 2021 article in the MIT Technology Review, there are three pivotal needs driving capital-intensive industries to digitise and implement purpose-built AI systems: 1
Generational shifts in the workforce are creating a loss of operational expertise. Veteran workers with years of institutional knowledge are retiring, replaced by younger workers taught on technologies and concepts that don’t match the reality of many organisations’ workflows and systems. This dilemma is fuelling the need for automated knowledge sharing and intelligence-rich applications that can close the skills gap.
Industrial organisations are accumulating massive volumes of data but deriving business value from only a small slice of it. Organisations are switching their focus from mass data accumulation to strategic industrial data management, homing in on data integration, mobility, and accessibility—with the goal of using AI-enabled technologies to unlock value hidden in these unoptimised and underutilised sets of industrial data.
Adopting new technologies unlocks new business models that are integral to sustainability, market competitiveness, and new corporate strategies. The more that competitors digitally transform to reap these advantages, the more organisations that don’t transform will be left behind.
Some of the use cases for industrial AI include:
- Self-aware, smart equipment that can independently measure performance to generate alerts when degradation reaches a critical point or performance is reduced for any reason.
- Creating ‘smarter’ software for regulatory compliance monitoring in banks and financial institutions.
- Robotics and automation on the production floor that can replace human involvement, thereby increasing efficiency and boosting production while improving human safety.
- Complex supply chain management that increases visibility into every step of the process, including tracking raw materials, inventory, warehouse management, logistics, and last-mile distribution.
Footnotes
1 https://www.technologyreview.com/2021/06/28/1026960/the-future-starts-with-industrial-ai/
Contact us
To express your interest in AIML’s Industrial AI program, please contact AIMLIndustrialAI@adelaide.edu.au