"Use of ResNet50 architecture for Apple Leaves Disease Prediction"

I'm excited to share that I've successfully completed my latest project focusing on apple leaf disease prediction using advanced deep learning techniques. Leveraging the powerful RESNET50 architecture alongside custom-designed CNN layers, we achieved an impressive accuracy rate of 95%.

The Kaggle dataset included 19,428 annotated apple leaf photos representing various disease stages and environmental circumstances, organized into four unique classifications. Our approach accurately identified diseases like apple scab, black rot, cedar apple rust, and healthy leaves.

Throughout training, our model exhibited convergence after 7 epochs, reaching a plateau in performance. However, we continued training over a total of 20 epochs to ensure thorough optimization and fine-tuning of model parameters. We conducted training and testing with a split of 80% and 20% of the dataset, respectively, ensuring a comprehensive evaluation of model generalization and performance. We performed Data preprocessing where dataset undergo rigorous cleaning and standardization procedures to ensure uniformity across the dataset. This involves techniques such as resizing, cropping, and normalization to achieve consistent image dimensions and color profiles. Preprocessing task preprocessed the images by resizing them to 96x96 dimensions.

Implemented through average pooling, our model showcased significant benefits in terms of computational efficiency and robust feature extraction. By aggregating feature maps, average pooling facilitated a reduction in model complexity while retaining important spatial information, enhancing the overall performance and interpretability of our predictions.

This project not only highlights my expertise in machine learning and computer vision but also underscores my commitment to addressing real-world challenges in agriculture through innovative solutions. Looking forward to continuing this journey of exploration and impact.

#MachineLearning #DeepLearning #ResNet50 #ComputerVision #Agriculture #Innovation #computerscience