Advanced Image Preprocessing & Augmentation Pipeline for All Type Agriculture Crop Disease Datasets (15 Techniques Applied)
Advanced Image Preprocessing & 15-Step Augmentation Pipeline for Agriculture Crop Disease Datasets Working with agricultural images—especially fruit and leaf disease datasets —comes with its own set of challenges. Lighting variations, inconsistent contrast, noise, and different camera positions can easily confuse a machine-learning model . To handle this, here is a complete preprocessing and augmentation workflow designed for agriculture datasets such as apples, mangoes, grapes, wheat leaves, rice leaves, and more. This pipeline generates 15 automatic transformations per image, making your dataset much richer and far more suitable for training deep-learning models like CNNs , Vision Transformers , and hybrid SVM-ViT models . 1. Install the Required Python Packages We install only the necessary tools such as OpenCV , Pillow , and tqdm . These handle image processing, resizing, and progress visualization. # 📦 Install Required Packages !pip install opencv-python-...