Deep Learning in Agriculture: Leaf Classification Project Overview

Importance in Agriculture

In today’s agricultural sector, rapid and accurate identification of plant species and their health status is paramount. Diseases in crops can significantly impact yield and, consequently, the overall food supply. This project’s goal to classify leaf images and assess their health has far-reaching implications:

• Early Disease Detection: By identifying diseased plants early, farmers can take prompt actions to mitigate the spread of disease, potentially saving large portions of crops.
• Smart Farming: Integrating this technology with drones or automated systems can lead to efficient monitoring of large fields, enabling farmers to identify and address issues swiftly and on a much larger scale.

For a comprehensive understanding of this project, including details of the neural network architecture and code to train, evaluate, and make classifications, please visit my GitHub repository.

erickbr15 / dlpytorch-leaves-classification

The core of this project revolves around the development of specialized neural network models for two key tasks: species classification and health status assessment of plant leaves.

Custom ResNet18 Neural Network for Leaf Classification and Health Assessment

Project Methodology Overview

The core of this project revolves around the development of specialized neural network models for two key tasks: species classification and health status assessment of plant leaves.

• Species Classification Model: A robust model was designed to identify the species of a plant from its leaf image. This model can classify leaves into various species, providing a fundamental layer of analysis.
• Health Status Classification Models: For each species with sufficient data, an individual model was trained to determine the health status of the leaf. These models are capable of differentiating at least two classes, offering a deeper insight into the plant's condition.

Repository and Structure

The repository is structured as follows to facilitate navigation and understanding:

• /models: Stores the trained model files, which can be used for direct predictions without the need for retraining.

  Model	File
  Leaf

  …

View on GitHub

Project Details and Neural Network Architecture

Approach

I developed a two-fold neural network model: the first to identify the plant species from leaf images, and the second to determine the health status of these identified species.
This dual-model approach enhances accuracy and provides a comprehensive understanding of plant health.

Deep Neural Network Architecture

• Customized ResNet18 Model: The backbone of this project is a customized version of the well-known ResNet18 architecture.
• Network Adaptation: The initial and final layers of ResNet18 were modified. The input layer was adjusted to accommodate the shape of the leaf image tensors. Similarly, the output layer was tailored to produce results in line with the number of classes in our dataset.
• Application in Models: This adapted architecture forms the basis for both species identification and health status classification models.

Implementation details

Custom Dataset and Neural Network Architecture

• Custom Dataset Creation: Developed a dataset that processes images into tensors of shape 3x224x224, conforming to the required input structure for neural network models.
• Class Balance Analysis: Employed bar charts to assess the balance among classes, identifying those needing synthetic data augmentation.
• Synthetic Data Generation: Crafted a data augmentation generator, allowing for customizable transformations to balance classes effectively.
• DataLoader Setup: Established DataLoaders for training, validation, and testing, with data distribution set at 80% for training, 2% for validation, and 18% for testing.
• Network Customization: Adapted ResNet18 architecture to suit our needs, modifying it to handle 3x224x224 tensors and configuring the output to manage 14 classes. Training Method and Process: Utilized an Adam optimizer with a 0.001 learning rate and Cross Entropy Loss function. The model underwent 10 epochs of training, focusing on experimental and validation purposes.
• Infrastructure Note: Training was performed using CUDA/GPUs on Colab, with a recommendation to utilize the entire dataset on more advanced infrastructure for future iterations.

Performance and Predictive Analysis

• Model Performance: Evaluated using the test dataset to ensure reliability and accuracy.
• Predictive Methodology: Demonstrated through a coded example, showcasing how the persisted model can infer results from an image file.

Potential Applications

Imagine fields surveyed by drones, capturing images of crops. These images, when processed through our models, can provide valuable insights into the health of each plant. This real-time data can guide farmers to take immediate action wherever necessary, transforming the way we approach agriculture and crop management.

Why This Matters

This project is not just an academic exercise but a step towards revolutionizing agricultural practices using deep learning. By accurately identifying diseases and assessing plant health on a large scale, we can significantly increase efficiency in farming, leading to better crop yields and sustainable farming practices.