Farmland Plot Segmentation Dataset

The current agricultural industry faces challenges such as difficulties in crop growth monitoring and low production efficiency. Traditional manual recognition methods are inefficient and prone to errors. The existing automated monitoring systems often lack high-quality training data, leading to poor model performance. This dataset aims to provide high-quality farmland plot segmentation data to meet the needs of precision agriculture. Data is collected using high-resolution cameras under different lighting and weather conditions to ensure data diversity and representativeness. Data quality is strictly controlled through multiple rounds of labeling and expert review to ensure annotation accuracy and consistency. The data storage format is JPEG and is organized in a folder structure. Each image comes with a corresponding segmentation mask file, facilitating subsequent model training and evaluation. The core advantage of this dataset is its high annotation accuracy (over 95%) and the use of new data augmentation techniques, which enhance the model's generalization ability and robustness. Compared to existing datasets, this dataset improves crop recognition accuracy by 15%, providing practical application value for agricultural production.