A little box that can predict the amount of harmful aflatoxin contained in a handful of sample groundnuts… sounds like a far-fetched notion? Not anymore. A collaboration between Pure Scan AI and ICRISAT to create a portable aflatoxin detector has won the Inspire Challenge by the CGIAR Big Data Platform at the recent Big Data Convention, earning a US$ 100,000 grant to build and scale up the device. Utilizing the blacklight fluorescence feature of aflatoxin, this device captures the fluorescence by cameras with filters. Images are processed and the fluorescence degree and pattern are fed into a learning model that predicts the quantity of aflatoxin present in the sample to an accuracy of 1 part per billion error margin. This is the first time ICRISAT has won an Inspire Challenge award.
While more work needs to be done to bring this innovation to the farmer – e.g. an android app and a web platform have to be built, the innovators are hopeful that the device will soon enable farmers to access online marketplaces for a fair price on their high-quality produce free of aflatoxin. For more details on the product, click here for an explainer video on the Rapid Low-Cost Aflatoxin detection using AI.
Aflatoxin – a carcinogenic mycotoxin found in groundnut (and other produce e.g. maize, chillies, rice, tree nuts etc.) produced by a fungus Aspergillus flavus – can cause liver damage, malnutrition, immune suppression and cancer. Aflatoxin contamination is also responsible for millions of dollars in trade loss for farmers, processors and exporters. At present, there is a dearth of affordable and accessible tests to detect aflatoxin in agricultural produce. ; also, there is inadequate transparency in sales of these products, making traceability of contaminated products difficult. The above aflatoxin detection device hopes to leverage artificial intelligence and big data to resolve the above challenges, giving farmers a good price for their safe produce.
Work on development of this device began two years ago with Dr Srikanth Rupavatharam from the Digital Agriculture and Youth (DAY) theme, and Dr Hari Kishan Sudini from the Integrated Crop Management (ICM) theme at ICRISAT. This project seeks to improve an existing low-cost device (<USD50) for rapid aflatoxin detection in peanuts and maize using image processing under UV light.
The aflatoxin reading happens by placing a handful of peanuts into the box/device, which connects to an android application (to be built). A web platform will also be built to enable industries and buyers to view aflatoxin contents and other allied data and inform their choice to bid on identified peanut lots. Grading and segregation of groundnuts will lead to price discovery in export markets and industrial opportunities for alternative use of contaminated produce.
“Detecting and quantifying aflatoxins in a raw sample using fluorescence properties coupled with artificial intelligence is a non-invasive method which can be used for grading and segregating contaminated agricultural produce leading to higher returns to the grower by targeting export markets,” says Dr Srikanth.
It is significant to note that postharvest handling and processing of peanuts − stripping, shelling, drying, etc. – are mostly done by women. Handling aflatoxin-contaminated peanuts on a daily basis puts them under high risk. The model’s effect on grading, better practices and also reduction in aflatoxin will result in better health for women. This would help lower the amount of aflatoxin that enters our food chain and have multitudes of impact on population health.
“Food safety issues, especially due to aflatoxins, are a serious concern for safe food and fair trade worldwide,” says Dr Hari Sudini. “Rapid, precise and non-destructive diagnostic tools, which are still elusive, are pivotal for effective management of this problem. Our project will address these issues and contributes for a safe and secure food.”
The Inspire Challenge stimulates CGIAR centers and external partners to link high technology with agriculture and development to deliver impact in vulnerable regions of the world. It encourages participants to leverage digital innovations viz. artificial intelligence, machine learning, robotics, etc. to make life-changing transformations for marginal populations globally.
There is potential to scale up grants of up to 250 k after successful piloting supported by Big Data platform.
Other winners of this co mpetition are:
- Croppie – the PhotoCropping app
- N-ALLyzer: From Nitrogen to ALL other nutrients
- The ClimaCell Locust Project
- “Hola Talia”- Boosting extension service through AI
- Big data in resilience of rangeland communities
Another innovation co-developed by ICRISAT – Rapid plant disease detection phenotyping – also made it to the 15 finalists in this year’s Challenge. It used hyperspectral imaging and AI for automated early stress detection in chickpea crop, with the potential for large-scale automated stress phenotyping. Click here to see the video: https://youtu.be/OHHfzLIzhPI
The theme for this year’s Big Data Convention (19-23 October) was Digital Dynamism for Adaptive Food Systems, with side-events and discussions examining how modern technologies and digital tools could help food systems become more responsive to crises (like the recent global pandemic) that affect food and nutrition security for millions, and more resilient to rebuild themselves quickly and efficiently. Due to restrictions on travel, this year’s Convention was held virtually, with hundreds of delegates logging in from across all the CGIAR centers and their partners around the world. This was the first event that was held under the aegis of OneCGIAR.
For more on our work in digital technology for agriculture, click here: http://exploreit.icrisat.org/profile/knowledge%20management/79