Disease Detection on Plant Leaves using Machine Learning

Objectives

1.Data Collection and Preprocessing: Gather a comprehensive dataset of plant leaf images containing healthy and diseased samples for various plant species. Preprocess the dataset to ensure uniformity and quality.

2.Feature Extraction and Selection: Extract relevant features from the plant leaf images and select the most discriminative ones to improve model performance.

3.Model Development: Develop a machine learning model (e.g., convolutional neural network, support vector machine) for disease detection on plant leaves. Train the model using the preprocessed dataset and optimize its hyperparameters.

4.Validation and Evaluation: Validate the model's performance using various evaluation metrics such as accuracy, precision, recall, and F1-score. Conduct cross-validation to ensure robustness.

Objectives

Methodology

1.Data Collection: Collect a diverse dataset of plant leaf images, including healthy and diseased samples, along with metadata such as plant species, disease type, and severity.

2.Data Preprocessing: Clean and preprocess the dataset by resizing images, handling class imbalance, and augmenting data to enhance model generalization.

3.Feature Extraction: Extract relevant features from the images using techniques such as convolutional neural networks (CNNs) and feature engineering.

4.Model Development: Implement and train machine learning models for disease detection, experimenting with various algorithms and architectures.

5.Validation and Evaluation: Evaluate model performance using standard machine learning metrics and techniques, such as k-fold cross-validation and confusion matrices.

6.Deployment and Testing: Deploy the model and interface in a controlled agricultural environment to gather user feedback and validate real-world performance.

Methodology

Motivation

Our motivation lies in harnessing the power of machine learning to create a robust, accessible, and user-friendly tool that empowers farmers and agricultural professionals to detect and combat plant diseases efficiently. By doing so, we aim to contribute to increased agricultural productivity, reduced crop losses, and improved food security on a global scale. This project aligns with our commitment to sustainable agriculture and the advancement of technology for the greater good.

Motivation

Expected output

Expected output

1.Trained Machine Learning Models: Developed machine learning models capable of accurately detecting plant diseases from images of leaves. These models should be optimized for high accuracy and robustness.

2.User-Friendly Interface or Application: A user-friendly interface or application that allows end-users, such as farmers or agricultural professionals, to easily upload images of plant leaves and receive rapid disease diagnosis results.

3.Comprehensive Dataset: A curated dataset of plant leaf images, containing both healthy and diseased samples, along with metadata for research and validation purposes.

4.Documentation: Thorough project documentation, including data collection procedures, model architectures, and deployment instructions, to ensure reproducibility and future development.

References

References

1.Mohanty, S.P., Hughes, D.P., Salathé, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7 (2016). Article: 1419

2.Fuentes, A., Yoon, S., Kim, S.C., Park, D.S.: A robust deep-learning-based detector for real-time tomato plant diseases and pest recognition. Sensors 17(9), 2022 (2017)

3.Ramcharan, A., Baranowski, K., McCloskey, P., Ahmed, B., Legg, J., Hughes, D.: Deep learning for image-based Cassava disease detection, frontiers in plant science. Front. Plant Sci. 8, 1852(2017)