Project report on leaf disease detection system
| dc.contributor.author | Nanditha, K., M., U19KU23S0100 | |
| dc.date.accessioned | 2026-07-11T10:26:40Z | |
| dc.date.available | 2026-07-11T10:26:40Z | |
| dc.date.issued | 2026 | |
| dc.description.abstract | Plant diseases are one of the major challenges faced by farmers and agricultural industries worldwide. Diseases affecting leaves can significantly reduce crop yield, quality, and overall productivity, leading to economic losses and food security concerns. Early identification and diagnosis of leaf diseases are essential for implementing timely treatment measures and preventing the spread of infections. Traditionally, disease detection relies on manual inspection by agricultural experts, which can be time- consuming, costly, and prone to human error, especially in large-scale farming environments. With the advancement of Artificial Intelligence (AI), Machine Learning (ML), and Computer Vision technologies, automated plant disease detection systems have become increasingly effective and accessible. These systems analyze images of plant leaves and identify diseases based on visible symptoms such as discoloration, spots, lesions, and texture changes. Automated solutions help farmers make informed decisions quickly and improve crop management practices. This project presents an **AI-powered Leaf Disease Detection System** developed using **Python**, **Streamlit**, **FastAPI**, **Groq AI Vision Model**, and **Pillow (PIL)**. The system enables users to upload images of plant leaves through a user-friendly web interface. The uploaded image is processed and analyzed using advanced AI vision capabilities to identify potential diseases and provide relevant information about the detected condition. The integration of FastAPI ensures efficient communication between the frontend and backend through REST APIs, while Streamlit offers an interactive and responsive user experience. The primary goal of this project is to provide a simple, accurate, and scalable solution for leaf disease diagnosis that can assist farmers, agricultural researchers, and students. By leveraging modern AI technologies, the system reduces dependency on manual diagnosis and promotes precision agriculture practices. Furthermore, the application demonstrates how cloud-based AI models can be integrated into web applications to solve real-world agricultural problems efficiently. Overall, the Leaf Disease Detection System contributes to sustainable farming by enabling faster disease identification, minimizing crop losses, and supporting data-driven agricultural decision- making. The project showcases the practical application of artificial intelligence in agriculture and highlights the potential of AI-powered vision models in enhancing crop health monitoring and management. | |
| dc.identifier.uri | http://192.168.75.5:4000/handle/123456789/21536 | |
| dc.language.iso | en | |
| dc.publisher | New Horizon College | |
| dc.title | Project report on leaf disease detection system | |
| dc.type | Learning Object |