Description of Invention
The task of monitoring and managing rice crops poses significant challenges, especially considering the vast expanses of land dedicated to their cultivation. Traditional methods, reliant on manual labor and visual inspection, are time-consuming and often imprecise, leading to sub-optimal agricultural outcomes. This project aims to implement advanced image processing techniques such as data masking, data annotation, and data augmentation applied to drone-captured images of rice fields in Malaysia, a critical region for rice production. By leveraging TensorFlow and PyTorch, tools for training deep learning models, this study attempts to improve the accuracy for image classification, object detection, and segmentation of rice crops, which are crucial for effective crop management.