CGMap：一种使用无人机进行作物间隙映射的地理空间感知深度学习框架
CGMap: A Geospatially Aware Deep Learning Framework for Crop Gap Mapping Using UAV
- Plaksha University（普拉卡沙大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对印度作物发芽监测易出错的问题，提出基于深度学习的CGMap框架，利用YOLOV8架构和无人机图像，结合最小生成树技术，精确计算甘蔗发芽情况，输出地理空间发芽图，助力提高产量和资源合理分配。
AI中文摘要：
在印度，作物发芽主要通过目视检查和人工计数来监测，尽管这在确定最终产量潜力方面起着关键作用，但容易出错。本文重点介绍了一种基于深度学习的流程，该流程使用目标检测方法和无人机图像来评估并精确计算田间甘蔗的发芽情况。该方法使用预训练的人工智能模型查找发芽植物样本并识别限制田间生产力的间隙（即“秃斑”）。这里使用的技术依赖于YOLOV8架构，该架构在精心挑选的印度不同农业气候区拍摄的无人机照片数据集上进行训练。本文提出了一种新颖的方向归一化技术，该技术使用最小生成树（MST）来考虑种植几何形状的变化，从而能够在各种田间布局中可靠地提取行和列。通过将检测到的幼苗转换为空间点云，可以根据预期的植物间距推断出发芽间隙。最终输出以知名文本（WKT）格式导出的地理空间发芽图，它可以轻松整合到糖厂和农学家使用的GIS平台中，以指导移植计划。基于算法提供的见解进行及时干预可以显著提高产量，从而带来更高的利润，支持资源的合理分配，避免浪费，并增强长期可持续性。
英文摘要：
In India, crop germination is primarily monitored by visual inspection and manual counting, which are prone to errors, despite their crucial role in determining eventual yield potential. This paper highlights a deep learning based pipeline which uses object detection methods and drone imagery to assess and provide a precise count of sugarcane germination in fields. The approch uses a pre-trained AI model to find germinated plant sampling and identify gaps, also known as ``bald spots'', which restricts field productivity. The techniques used here relies on the YOLOV8 architecture, which was trained on a carefully selected dataset of UAV photos taken in various agroclimatic zones of India. Here, we bring upon a novel orientation-normalization technique that uses minimum Spanning Trees (MST) to account for variations in planting geometry, allowing for dependable row and column extraction across a variety of field layouts. By converting detected seedlings into spatial point clouds, emergence gaps can be inferred from the anticipated spacing between plants. A geospatial germination map exported in Well-Known Text (WKT) format is the end result, and it can be easily incorporated into GIS platforms used by sugar mills and agronomists to direct transplant initiatives. Timely interventions based on the insights provided by the algorithm can significantly increase yield, resulting in higher profits. Hence, support proper allocation of resources, avoid wastage, and enhance long-term sustainability.
