Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production
Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production
复制标题
DOI：
10.1038/s41438-019-0151-5
复制
发表时间：
2019-06-01
影响因子：
8.7
通讯作者：
Zhou, Ji
中科院分区：
文献类型：
--
作者：
Bauer, Alan;Bostrom, Aaron George;Zhou, Ji
作物研究人员、种植者和农民经常使用航空图像来监测生长季节的作物。为了从田间采集的大规模航空影像中提取有意义的信息，需要高通量的表型分析解决方案，这不仅可以对关键作物性状进行高质量的测量，还可以支持专业人员做出及时可靠的作物管理决策。在这里，我们报告AirSurf，一个自动化和开源的分析平台，结合了现代计算机视觉，最新的机器学习和模块化软件工程，以便从超大型航空图像中测量产量相关的表型。为了量化配备标准化差异植被指数（NDVI）传感器的固定翼轻型飞机获取的数百万个实地植被，我们通过结合计算机视觉算法和深度学习分类器来定制AirSurf，该分类器使用超过100，000个标记的生菜信号进行训练。量身定制的平台AirSurf-Lettuce能够以高准确度（>98%）对冰山生菜进行评分和分类。此外，已经开发了新的分析功能，以绘制整个田地的莴苣尺寸分布图，基于该分析功能，已经识别了相关的全球定位系统（GPS）标记的收获区域，以使种植者和农民能够进行精确的农业测量，以提高实际产量以及收获前的作物适销性。
Aerial imagery is regularly used by crop researchers, growers and farmers to monitor crops during the growing season. To extract meaningful information from large-scale aerial images collected from the field, high-throughput phenotypic analysis solutions are required, which not only produce high-quality measures of key crop traits, but also support professionals to make prompt and reliable crop management decisions. Here, we report AirSurf, an automated and open-source analytic platform that combines modern computer vision, up-to-date machine learning, and modular software engineering in order to measure yield-related phenotypes from ultra-large aerial imagery. To quantify millions of in-field lettuces acquired by fixed-wing light aircrafts equipped with normalised difference vegetation index (NDVI) sensors, we customised AirSurf by combining computer vision algorithms and a deep-learning classifier trained with over 100,000 labelled lettuce signals. The tailored platform, AirSurf-Lettuce, is capable of scoring and categorising iceberg lettuces with high accuracy (>98%). Furthermore, novel analysis functions have been developed to map lettuce size distribution across the field, based on which associated global positioning system (GPS) tagged harvest regions have been identified to enable growers and farmers to conduct precision agricultural practises in order to improve the actual yield as well as crop marketability before the harvest.