利用多日期航空图像和正交马赛克中的自动注释和迁移学习分析作物生长情况

Agronomy Pub Date : 2024-09-07 DOI:10.3390/agronomy14092052
Shubham Rana, Salvatore Gerbino, Ehsan Akbari Sekehravani, Mario Brandon Russo, Petronia Carillo
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引用次数: 0

摘要

作物生长监测是精准农业的一个重要方面,对于优化产量预测和资源分配至关重要。传统的作物生长监测方法耗费大量人力且容易出错。本研究采用基于对象的图像分析方法,利用多日期航空图像和正射影像马赛克引入了一个自动分割流水线,以监测花椰菜作物(Brassica Oleracea var.该方法采用 YOLOv8、带有改进去噪锚框的接地检测变换器(DINO)以及用于自动注释和分割的 "任意分割模型"(SAM)。YOLOv8 模型是利用航空图像数据集进行训练的,这有助于训练接地检测模型框架。这种方法可生成自动注释和分割掩码,对作物行进行分类,以便进行时间监测和生长估算。研究结果采用了多模式监测方法,突出了这一自动化系统在提供准确的作物生长分析方面的效率,促进了作物管理和可持续农业实践方面的知情决策。结果表明,航空图像和正射影像马赛克之间的生长模式具有一致性和可比性,随着时间的推移,有显著的快速扩张期和轻微的波动期。结果还表明,观测时间与观测方法之间存在相关性,这为未来整合此类技术提供了可能,旨在根据自动得出的作物行间时间分割掩码提高作物生长监测的准确性。
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Crop Growth Analysis Using Automatic Annotations and Transfer Learning in Multi-Date Aerial Images and Ortho-Mosaics
Growth monitoring of crops is a crucial aspect of precision agriculture, essential for optimal yield prediction and resource allocation. Traditional crop growth monitoring methods are labor-intensive and prone to errors. This study introduces an automated segmentation pipeline utilizing multi-date aerial images and ortho-mosaics to monitor the growth of cauliflower crops (Brassica Oleracea var. Botrytis) using an object-based image analysis approach. The methodology employs YOLOv8, a Grounding Detection Transformer with Improved Denoising Anchor Boxes (DINO), and the Segment Anything Model (SAM) for automatic annotation and segmentation. The YOLOv8 model was trained using aerial image datasets, which then facilitated the training of the Grounded Segment Anything Model framework. This approach generated automatic annotations and segmentation masks, classifying crop rows for temporal monitoring and growth estimation. The study’s findings utilized a multi-modal monitoring approach to highlight the efficiency of this automated system in providing accurate crop growth analysis, promoting informed decision-making in crop management and sustainable agricultural practices. The results indicate consistent and comparable growth patterns between aerial images and ortho-mosaics, with significant periods of rapid expansion and minor fluctuations over time. The results also indicated a correlation between the time and method of observation which paves a future possibility of integration of such techniques aimed at increasing the accuracy in crop growth monitoring based on automatically derived temporal crop row segmentation masks.
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