Prediction of leaf disease Using ANN and CNN

Overview

Predicting leaf diseases using Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs) is a fascinating academic project that combines deep learning techniques with agricultural sciences. Here’s a structured outline for such a project:

1. Introduction

• Background: Introduce the importance of plant health and the impact of leaf diseases on agricultural productivity.
• Objective: State the goal of the project, which is to predict leaf diseases using ANN and CNN models.

2. Dataset Collection and Preprocessing

• Data Source: Specify where you collected the dataset from (e.g., plant pathology databases, agricultural research institutes).
• Dataset Description: Describe the characteristics of the dataset, including types of leaf diseases and healthy leaf samples.
• Data Preprocessing: Outline steps such as image normalization, resizing, and augmentation to prepare the data for model training.

3. Methodology

• Artificial Neural Network (ANN):

  • Explain the architecture of the ANN model for leaf disease prediction.
  • Detail the layers (input layer, hidden layers, output layer) and activation functions used.
  • Discuss how you handle image data as inputs to the ANN.

• Convolutional Neural Network (CNN):

  • Describe the CNN architecture tailored for image classification of leaf diseases.
  • Include convolutional layers, pooling layers, and fully connected layers.
  • Mention any techniques like transfer learning using pre-trained models (e.g., ResNet, VGG) if applicable.

• Training and Validation:

  • Explain the training process, including batch size, learning rate, and number of epochs.
  • Split the dataset into training, validation, and test sets for evaluation.

4. Evaluation Metrics

• Define the evaluation metrics used to assess model performance (e.g., accuracy, precision, recall, F1-score).
• Compare the performance of ANN and CNN models in predicting leaf diseases.

5. Results and Discussion

• Present the results of model training and validation.
• Discuss the strengths and weaknesses of ANN and CNN models for leaf disease prediction.
• Interpret which model architecture performs better and why.

6. Conclusion and Future Work

• Summarize the key findings of your project.
• Discuss potential improvements or extensions to the current work (e.g., integrating more diverse datasets, enhancing model interpretability).
• Highlight the practical applications and implications of your findings in agriculture and plant pathology.