3 篇博文 含有标签「Agriculture」

Project Overview​

This tutorial introduces a powerful standalone Python tool for agricultural image quantification analysis. The tool provides automated detection and measurement of plant samples from images, including morphological and color metrics.

1. Flight Path Design and Image Acquisition​

UAV image acquisition employs a CCO (Cross-Complementary Overlap) flight path design strategy. This strategy enhances viewpoint diversity through multi-directional cross-flight paths to strengthen geometric constraints for 3D reconstruction.

Key Design Points:​

• Multi sets of flight paths in different directions: Ensures multi-angle capture of crop canopy structure
• Forward and side overlap rates higher than conventional orthophoto requirements: Provides sufficient matching points for SfM reconstruction
• Image acquisition primarily serves 3D reconstruction goals: Rather than only satisfying orthophoto mosaic requirements

Based on industry-grade UAV platforms, multi-view RGB images of farmland are collected to provide unified data sources for subsequent orthophoto mosaic and 3D modeling.

Overview​

Root Quantify is a Python-based tool designed for processing root system images. It provides a complete pipeline for analyzing root images with the following capabilities:

Key Features:​

• Batch Processing: Automatically iterates through all images in a selected folder
• Interactive ROI Selection: Users click to select polygon vertices to define the region of interest (ROI)
• Digital Image Processing: Performs background estimation, shadow removal, binarization, and inversion
• Manual Correction: Allows manual editing of processed ROI with adjustable brush size
• Dual-Window Preview: Left window shows original image, right window handles ROI selection and processing
• Automatic Archiving: Saves processed images and moves originals to prevent reprocessing