Revise README.md

README.md

@@ -2,25 +2,29 @@
 # AgriField3D: A Curated 3D Point Cloud Dataset of Field-Grown Plants From A Maize Diversity Panel
 
 ## Overview
-The application of artificial intelligence (AI) in three-dimensional (3D) agricultural research, particularly for maize, 
-has been limited by the scarcity of large-scale, diverse datasets. While 2D image datasets are abundant, they cannot capture 
-essential structural details such as leaf architecture, plant volume, and spatial arrangements that 3D data can provide. 
-To address this limitation, we present a curated dataset of 3D point clouds of fully field-grown maize plants with diverse 
-genetic backgrounds designed to be AI-ready for advancing agricultural research. Our dataset comprises over 1,000 high-quality 
-point clouds representing diverse maize varieties collected using a Terrestrial Laser Scanner. To enhance the usability of the 
-dataset, we performed graph-based segmentation to isolate individual leaves and stalk point clouds. Each leaf is assigned a 
-consistent color label across all plants based on its order from bottom to top (e.g., all first leaves are the same color, 
-all second leaves are the same color, and so on). Similarly, all stalks are assigned a single, distinct color. A rigorous 
-quality control process was undertaken to manually correct any errors in the segmentation and leaf ordering. This ensures 
-accurate segmentation and consistent color labeling and facilitates precise leaf counting and structural analysis. Additionally, 
-the dataset includes metadata describing point cloud quality, the number of leaves, and the presence of tassels and maize cobs. 
-To support diverse AI applications, we provide code that can sub-sample each point cloud to create a user-defined resolution 
-(100k, 50k, 10k points, etc.) through uniform downsampling. All versions were manually quality-checked to ensure the preservation 
-of topology and plant structure. This dataset lays the foundation for leveraging 3D data to enable advanced applications in agricultural 
-research, particularly for maize phenotyping and plant structure studies.
-## Dataset Structure
-
-The dataset consists of two compressed `.zip` files, which contain the 3D point cloud data:
+The use of artificial intelligence (AI) in three-dimensional (3D) agricultural research, especially for maize, 
+has been limited due to the lack of large-scale, diverse datasets. While 2D image datasets are widely available, 
+they fail to capture key structural details like leaf architecture, plant volume, and spatial arrangements—information 
+that 3D data can provide. To fill this gap, we present a carefully curated dataset of 3D point clouds representing fully 
+field-grown maize plants with diverse genetic backgrounds. This dataset is designed to be AI-ready, offering valuable 
+insights for advancing agricultural research.
+
+Our dataset includes over 1,000 high-quality point clouds of maize plants, collected using a Terrestrial Laser Scanner. 
+These point clouds encompass various maize varieties, providing a comprehensive and diverse dataset. To enhance usability, 
+we applied graph-based segmentation to isolate individual leaves and stalks. Each leaf is consistently color-labeled based 
+on its position in the plant (e.g., all first leaves share the same color, all second leaves share another color, and so on). 
+Similarly, all stalks are assigned a unique, distinct color.
+
+A rigorous quality control process was applied to manually correct any segmentation or leaf-ordering errors, ensuring 
+accurate segmentation and consistent labeling. This process facilitates precise leaf counting and structural analysis. 
+In addition, the dataset includes metadata describing point cloud quality, leaf count, and the presence of tassels and maize cobs.
+
+To support a wide range of AI applications, we also provide code that allows users to sub-sample the point clouds, 
+creating versions with user-defined resolutions (e.g., 100k, 50k, 10k points) through uniform downsampling. 
+Every version of the dataset has been manually quality-checked to preserve plant topology and structure. 
+This dataset sets the stage for leveraging 3D data in advanced agricultural research, particularly for maize phenotyping and plant structure studies.
+
+## Dataset Directory Structure
 
 ```
 AgriField3D/