RipeSmart
REDUCING MANGO WASTE: A DYNAMIC PRICING SOLUTION FOR SUSTAINABLE SUPPLY CHAINS
Welcome to our project dedicated to reducing mango waste and optimizing pricing strategies through the power of artificial intelligence. We have developed an innovative mobile application that accurately predicts the ripeness of mangoes, enabling dynamic pricing to minimize waste and maximize value. Our solution is designed to benefit farmers, retailers, and consumers by enhancing the efficiency and sustainability of the agricultural supply chain. Join us in revolutionizing the way we manage agricultural produce, creating a more sustainable future for everyone.
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Methodology
Solution Architecture Our solution architecture ensures efficient processing and accurate ripeness prediction of mangoes, enabling dynamic pricing to reduce waste. The mobile application, developed using React Native, allows users to easily upload mango images. These images are processed by a backend server built with FastAPI and Uvicorn, ensuring fast and reliable performance. Our YOLOv8 machine learning model, hosted on Hugging Face, is trained on a dataset of over 30,000 mango images. The data preprocessing pipeline includes techniques such as resizing, normalization, rotation, and scaling to prepare the images for accurate prediction. The model then predicts the ripeness of the mangoes, allowing for dynamic pricing adjustments to optimize sales and reduce waste. All images and prediction results are securely stored in the cloud, ensuring data privacy and scalability. This architecture integrates mobile app development, advanced AI, and secure cloud storage to create an effective solution for reducing mango waste and enhancing the efficiency of the agricultural supply chain.
solution architecture daigram
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data collection
Vision
To revolutionize the agricultural industry by integrating cutting-edge AI technologies, reducing food waste, and creating sustainable and efficient supply chains that benefit farmers, retailers, and consumers globally.
Mission
To develop an innovative mobile application that leverages artificial intelligence to accurately predict the ripeness of mangoes, optimize dynamic pricing strategies, and minimize food waste. We aim to empower stakeholders in the agricultural supply chain with advanced tools and insights to make informed decisions and enhance overall productivity and sustainability.
End Goal
To successfully implement a scalable and user-friendly mobile application that significantly reduces mango waste and optimizes pricing, setting a precedent for similar AI-driven solutions in the agricultural sector. Our ultimate aim is to create a more sustainable and efficient agricultural ecosystem, benefiting all participants in the supply chain and contributing to global food security.
About
Our project aims to address the issue of mango waste and optimize pricing strategies by developing an innovative mobile application. The objective is to leverage artificial intelligence to accurately predict the ripeness stages of mangoes, enabling dynamic pricing to reduce waste and maximize value throughout the supply chain. This solution is designed to benefit farmers, retailers, and consumers by enhancing the efficiency and sustainability of the agricultural supply chain, ultimately contributing to reduced food waste and optimized market practices.
Tools & Technologies
In our research, we employed a range of advanced technologies to develop an efficient and scalable solution for mango waste reduction and dynamic pricing optimization. The mobile application was built using React Native, providing a seamless and intuitive user interface for uploading mango images. For the backend, we utilized FastAPI to create a robust and high-performance API, while Uvicorn served as the ASGI server to handle asynchronous processing. The machine learning model was hosted on Hugging Face, enabling easy deployment and management. We trained a YOLOv8 model on a dataset of over 30,000 mango images, applying various data preprocessing techniques such as resizing, normalization, rotation, scaling, and brightness adjustment to enhance model accuracy and robustness. This comprehensive approach ensured reliable image processing, secure data storage, and accurate ripeness prediction, paving the way for effective dynamic pricing strategies.
Our Research Team
Mohamed Safras
Research Member
Dynamic and results-oriented Software Engineer with a proven track record of delivering high-quality code and leading teams to success. With over 1 year of industry experience in Software Engineering and Web Application Development, I excel in ensuring code artifacts meet the highest quality standards while adhering to set processes and agile practices. My leadership abilities shine through as I motivate team members to follow best practices and maintain productivity using engineering tools and technologies.
Fathima Akeela
Research Member
As an aspiring software development engineer, I am currently pursuing my undergraduate studies at the University of Sri Jayewardenepura, within the Faculty of Technology. My academic journey has been marked by a strong foundation in software engineering principles, combined with hands-on experience in developing innovative solutions.
Nuwan Kuruwitaarachchi, PhD
Supervisor
As a dedicated academic and researcher, I am a Senior Lecturer at the University of Sri Jayewardenepura, where I bring over a decade of experience in teaching, research, and industry collaboration. My passion for advancing knowledge and fostering innovation in my field has driven my commitment to excellence in both my academic and professional pursuits.
Nimal Skandhakumar, PhD
Co-Supervisor
I am a Security Awareness professional, a researcher, and a university academic. I leverage my diverse skills and knowledge to help organisations build a strong cyber resilience culture through engaging and impactful security awareness programs and campaigns.I have over 10 years of consulting experience in IT infrastructure projects, which has given me a strong background in networking and information security.
Contact US
REDUCING MANGO WASTE: A DYNAMIC PRICING SOLUTION FOR SUSTAINABLE SUPPLY CHAINS
For further information about our research on reducing mango waste and implementing dynamic pricing strategies, or to discuss potential collaborations and partnerships, please feel free to contact us through the following channels