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AGNO

General Developer category
Participant location: India

Climate change is causing numerous challenges for farmers, and they have no choice but to adopt more sustainable practices to stay in business. They need to optimize resource usage, adapt to changing weather patterns, maintain and improve access to fair markets, and streamline communications to resolve issues, all of which can be especially challenging for small-scale farmers who often lack the resources available to larger farms. The AGNO team from Hexaware sought to help these farmers get better access to data and recommendations that can help them improve their food yields and deal with uncertainty brought about by climate change. Real-time weather forecasts and AI-driven crop management information are provided in their “FARMISTAR” solution: an online marketplace designed to empower farmers with insights. Several IBM AI services including watsonx.ai and Watson Assistant are used to do things like improve the quality of chatbot responses to user queries, provide rain and crop yield predictions, and recommend suitable fertilizers. Future planned enhancements include integrations with IoT devices for real-time monitoring of critical farm conditions, use of IBM Maximo Visual Inspection to help farmers with early detection of crop disease and assessment of harvested crop quality, and additional use of watsonx to improve context-aware and up-to-date educational content for farmers.

Key tech used in their solution:

watsonx.aiwatsonx AssistantWatson Speech to TextWatson Text to SpeechWatson StudioLanguage Translator

Phyto

University category
Participant location: Australia

Over 17 million hectares of land in Indonesia are used for coal mining, contaminating adjacent farmland with heavy metals and thereby reducing crop yield by up to 50%. Land rehabilitation is possible by executing phytoremediation, a process which uses certain crops to extract heavy metals from the soil. However, many farmers do not understand which plants are best for their land. A team of students majoring in a diverse mix of subjects such as Banking, Business Analytics, Finance, Marketing, and Food Science at the University of Sydney came together to form team Phyto. Their solution uses geospatial and weather analytics to design a tailored phytoremediation plan for each farmer. Based on a farmer’s location, the app uses IBM Environmental Intelligence Suite to access satellite images, which are then analyzed to determine contamination levels, soil conditions, moisture levels, and nearby vegetation. A Watson machine learning model then uses IBM Weather Data APIs to access these soil conditions and select the most suitable phytoremediation crop for the farmer to plant. For the farmers, analysis is all done remotely, reducing the need for them to do manual soil analysis on site. Additionally, once the plants are in place, the app includes growing guidelines, weather alerts, and land rehabilitation resources to help ensure the success of the new crop. Users can also use the “connect” feature to engage with community members and experts. Lastly, the “sell” feature provides access to an online marketplace which connects farmers to small local businesses who are sourcing raw materials. With little to no coding experience, the Phyto team was able to learn Watson Studio quickly to build their solution. They hope their solution can also eventually be used for larger scale land rehabilitation projects.

Key tech used in their solution:

Watson Studio

Synergy Squad

Independent Software Vendor/Startup category
Participant location: India, Malaysia

Approximately 1 out of every 3 people in the world does not have access to adequate food. Food waste also contributes to greenhouse gas emissions. The Synergy Squad team from Persistent is hoping their solution can address food insecurity and reduce food waste by making sure less food purchased by consumers goes to waste. Their “Offshelf” solution enables users to track the food in their homes, receive expiry notifications, and share surplus item within the community to reduce the chances of good food going bad. AI recognizes fresh items from a photo or voice description and provides an estimated expiration date, or packaged items can be scanned to input the date. Push notifications would let users know when groceries are about to expire, reducing the likelihood of wasted food. When users cannot make use of their groceries, the solution also includes a platform to facilitate the sharing of surplus food within the local community. Watson Speech to Text is used to help users fill out forms in the application through voice input. The team hopes to add to Offshelf, with additional image recognition support to read expiration dates and other details from food labels faster.

Key tech used in their solution:

Watson Text to Speech