Sharma Neelesh, Banerjee Bikram Pratap, Hayden Matthew, Kant Surya
Agriculture Victoria, Grains Innovation Park, 110 Natimuk Rd, Horsham, VIC 3400, Australia.
AgriBio, Centre for AgriBioscience, Agriculture Victoria, 5 Ring Road, Melbourne, VIC 3083, Australia.
Plants (Basel). 2023 Jan 9;12(2):317. doi: 10.3390/plants12020317.
Advanced plant phenotyping techniques to measure biophysical traits of crops are helping to deliver improved crop varieties faster. Phenotyping of plants using different sensors for image acquisition and its analysis with novel computational algorithms are increasingly being adapted to measure plant traits. Thermal and multispectral imagery provides novel opportunities to reliably phenotype crop genotypes tested for biotic and abiotic stresses under glasshouse conditions. However, optimization for image acquisition, pre-processing, and analysis is required to correct for optical distortion, image co-registration, radiometric rescaling, and illumination correction. This study provides a computational pipeline that optimizes these issues and synchronizes image acquisition from thermal and multispectral sensors. The image processing pipeline provides a processed stacked image comprising RGB, green, red, NIR, red edge, and thermal, containing only the pixels present in the object of interest, e.g., plant canopy. These multimodal outputs in thermal and multispectral imageries of the plants can be compared and analysed mutually to provide complementary insights and develop vegetative indices effectively. This study offers digital platform and analytics to monitor early symptoms of biotic and abiotic stresses and to screen a large number of genotypes for improved growth and productivity. The pipeline is packaged as open source and is hosted online so that it can be utilized by researchers working with similar sensors for crop phenotyping.
先进的植物表型分析技术用于测量作物的生物物理特性，有助于更快地培育出改良作物品种。使用不同传感器进行图像采集并结合新颖的计算算法对植物进行表型分析，正越来越多地用于测量植物特性。热成像和多光谱成像为在温室条件下对经受生物和非生物胁迫的作物基因型进行可靠的表型分析提供了新机会。然而，需要对图像采集、预处理和分析进行优化，以校正光学畸变、图像配准、辐射定标和光照校正。本研究提供了一个计算流程，可优化这些问题并同步热成像和多光谱传感器的图像采集。图像处理流程提供了一个经过处理的堆叠图像，包括RGB、绿色、红色、近红外、红边和热成像，仅包含感兴趣对象（如植物冠层）中的像素。植物热成像和多光谱成像中的这些多模态输出可以相互比较和分析，以提供互补的见解并有效地开发植被指数。本研究提供了数字平台和分析方法，以监测生物和非生物胁迫的早期症状，并筛选大量基因型以提高生长和生产力。该流程被打包为开源并在线托管，以便使用类似传感器进行作物表型分析的研究人员可以使用。
