Cotton Leaf Disease Prediction using ResNet-152v2

Introduction

Cotton is one of the most important cash crops in many countries, and the detection and prediction of cotton leaf diseases is crucial for the productivity and profitability of cotton farming. In this project, we aim to build a deep learning model using ResNet-152v2 architecture to predict cotton leaf diseases.

Data

The dataset used for this project consists of images of cotton leaves with different diseases. The data has been pre-processed and divided into training and testing sets. The number of classes in the dataset depends on the number of different diseases present in the images.

Model Architecture

We have used the ResNet-152v2 architecture for this project. ResNet-152v2 is a deep residual network architecture with 152 layers designed to address the vanishing gradients problem in very deep networks. Residual connections are used in this architecture to allow information to be passed from earlier layers to later layers, alleviating the vanishing gradients problem.

Model Training

The model is trained using the training data and optimized using a suitable loss function and optimizer. The model is then evaluated on the testing data to determine its performance.

Conclusion

In this project, we have demonstrated the use of ResNet-152v2 architecture for cotton leaf disease prediction. The results show that the ResNet-152v2 architecture is well suited for this task and provides promising results. Further improvements can be made by fine-tuning the hyperparameters, using data augmentation techniques, and incorporating additional information into the model.