Apple Leaf Disease Diagnosis Using Machine Learning 🍃
In my final year of engineering, I undertook a research project focused on leveraging machine learning techniques to enhance agricultural productivity by diagnosing apple leaf diseases. The project’s core objective was to analyze the performance of various transfer learning models in accurately identifying different apple leaf diseases.
📊 Project Objective
To achieve this, I trained a machine learning model using transfer learning, a technique that utilizes pre-trained models and fine-tunes them for specific tasks. The model was designed to identify four distinct classes of apple leaf diseases. The project involved a comprehensive performance analysis of multiple transfer learning models to determine the most effective approach for accurate disease diagnosis.
🧪 Methodology
- Transfer Learning: Utilized pre-trained models such as VGG16, ResNet50, and InceptionV3.
- Fine-Tuning: Adjusted these models to improve their performance on the specific task of identifying apple leaf diseases.
- Classes: Focused on four distinct classes of apple leaf diseases.
💻 Web Application
Additionally, I developed a web application to deploy the trained model, enabling users to upload images of apple leaves and receive instant disease diagnoses. This deployment aimed to provide an accessible tool for farmers and agricultural professionals to identify and manage apple leaf diseases more effectively, ultimately contributing to better crop health and yield.
- User Interface: Simple and intuitive for ease of use by farmers.
- Real-Time Diagnosis: Immediate results upon uploading the image.
- Accessibility: Available to anyone with internet access.
🌟 Impact
The project aims to provide a significant positive impact on agricultural productivity by enabling timely and accurate diagnosis of apple leaf diseases. This tool helps in:
- Early Detection: Allowing for early intervention and management.
- Improved Crop Health: Leading to better yields and quality.
- Supporting Farmers: By providing a reliable and easy-to-use diagnostic tool.
Links 🔗
Google Collab
Thank you for considering my project. I look forward to discussing how my skills and experience can contribute to innovative solutions in your organization. 🌟