Stamford John D, Vialet-Chabrand Silvere, Cameron Iain, Lawson Tracy
School of Life Sciences, University of Essex, Colchester, CO4 3SQ, Essex, UK.
Horticulture and Product Physiology, Department of Plant Sciences, Wageningen University & Research, 16, 6700 AA, Wageningen, The Netherlands.
Plant Methods. 2023 Jan 30;19(1):9. doi: 10.1186/s13007-023-00981-8.
Spectral imaging is a key method for high throughput phenotyping that can be related to a large variety of biological parameters. The Normalised Difference Vegetation Index (NDVI), uses specific wavelengths to compare crop health and performance. Increasing the accessibility of spectral imaging systems through the development of small, low cost, and easy to use platforms will generalise its use for precision agriculture. We describe a method for using a dual camera system connected to a Raspberry Pi to produce NDVI imagery, referred to as NDVIpi. Spectral reference targets were used to calibrate images into values of reflectance, that are then used to calculated NDVI with improved accuracy compared with systems that use single references/standards.
NDVIpi imagery showed strong performance against standard spectrometry, as an accurate measurement of leaf NDVI. The NDVIpi was also compared to a relatively more expensive commercial camera (Micasense RedEdge), with both cameras having a comparable performance in measuring NDVI. There were differences between the NDVI values of the NDVIpi and the RedEdge, which could be attributed to the measurement of different wavelengths for use in the NDVI calculation by each camera. Subsequently, the wavelengths used by the NDVIpi show greater sensitivity to changes in chlorophyll content than the RedEdge.
We present a methodology for a Raspberry Pi based NDVI imaging system that utilizes low cost, off-the-shelf components, and a robust multi-reference calibration protocols that provides accurate NDVI measurements. When compared with a commercial system, comparable NDVI values were obtained, despite the fact that our system was a fraction of the cost. Our results also highlight the importance of the choice of red wavelengths in the calculation of NDVI, which resulted in differences in sensitivity between camera systems.
光谱成像技术是高通量表型分析的关键方法，可与多种生物学参数相关联。归一化植被指数（NDVI）利用特定波长来比较作物的健康状况和生长表现。通过开发小型、低成本且易于使用的平台来提高光谱成像系统的可及性，将使其在精准农业中的应用更加普及。我们描述了一种使用连接到树莓派的双相机系统生成NDVI图像的方法，称为NDVIpi。使用光谱参考目标将图像校准为反射率值，然后用于计算NDVI，与使用单一参考/标准的系统相比，其精度有所提高。
NDVIpi图像在测量叶片NDVI方面表现出色，与标准光谱法相比性能强劲。NDVIpi还与相对更昂贵的商业相机（Micasense RedEdge）进行了比较，两台相机在测量NDVI方面具有可比的性能。NDVIpi和RedEdge的NDVI值存在差异，这可能归因于每台相机用于NDVI计算的不同波长的测量。随后，NDVIpi使用的波长对叶绿素含量变化的敏感度高于RedEdge。
我们提出了一种基于树莓派的NDVI成像系统的方法，该系统利用低成本的现成组件和强大的多参考校准协议，可提供准确的NDVI测量。与商业系统相比，尽管我们的系统成本仅为其几分之一，但仍获得了可比的NDVI值。我们的结果还强调了在NDVI计算中选择红色波长的重要性，这导致了相机系统之间敏感度的差异。
