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/
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 IndustrialAI@adelaide.edu.au.