The inspiration behind LeafHack stems from a shared passion for sustainability and a desire to empower individuals to take control of their food sources. Witnessing the rising grocery costs and the environmental impact of conventional agriculture, we were motivated to create a solution that not only addresses these issues but also lowers the barriers to home gardening, making it accessible to everyone.
Our team introduces "LeafHack" an application that leverages computer vision to detect the health of vegetables and plants. The application provides real-time feedback on plant health, allowing homeowners to intervene promptly and nurture a thriving garden. Additionally, the images uploaded can be stored within a database custom to the user. Beyond disease detection, LeafHack is designed to be a user-friendly companion, offering personalized tips and fostering a community of like-minded individuals passionate about sustainable living
LeafHack was built using a combination of cutting-edge technologies. The core of our solution lies in the custom computer vision algorithm, ResNet9, that analyzes images of plants to identify diseases accurately. We utilized machine learning to train the model on an extensive dataset of plant diseases, ensuring robust and reliable detection. The database and backend were built using Django and Sqlite. The user interface was developed with a focus on simplicity and accessibility, utilizing next.js, making it easy for users with varying levels of gardening expertise
We encountered several challenges that tested our skills and determination. Fine-tuning the machine learning model to achieve high accuracy in disease detection posed a significant hurdle as there was a huge time constraint. Additionally, integrating the backend and front end required careful consideration. The image upload was a major hurdle as there were multiple issues with downloading and opening the image to predict with. Overcoming these challenges involved collaboration, creative problem-solving, and continuous iteration to refine our solution.
We are proud to have created a solution that not only addresses the immediate concerns of rising grocery costs and environmental impact but also significantly reduces the barriers to home gardening. Achieving a high level of accuracy in disease detection, creating an intuitive user interface, and fostering a sense of community around sustainable living are accomplishments that resonate deeply with our mission.
Throughout the development of LeafHack, we learned the importance of interdisciplinary collaboration. Bringing together our skills, we learned and expanded our knowledge in computer vision, machine learning, and user experience design to create a holistic solution. We also gained insights into the challenges individuals face when starting their gardens, shaping our approach towards inclusivity and education in the gardening process.
We plan to expand LeafHack's capabilities by incorporating more plant species and diseases into our database. Collaborating with agricultural experts and organizations, we aim to enhance the application's recommendations for personalized gardening care.