Ghamkhar Kioumars, Irie Kenji, Hagedorn Michael, Hsiao Jeffrey, Fourie Jaco, Gebbie Steve, Hoyos-Villegas Valerio, George Richard, Stewart Alan, Inch Courtney, Werner Armin, Barrett Brent
1Forage Science, Grasslands Research Centre, AgResearch, Palmerston North, New Zealand.
2Lincoln Agritech Ltd, Lincoln, New Zealand.
Plant Methods. 2019 Jul 10;15:72. doi: 10.1186/s13007-019-0456-2. eCollection 2019.
In-field measurement of yield and growth rate in pasture species is imprecise and costly, limiting scientific and commercial application. Our study proposed a LiDAR-based mobile platform for non-invasive vegetative biomass and growth rate estimation in perennial ryegrass ( L.). This included design and build of the platform, development of an algorithm for volumetric estimation, and field validation of the system. The LiDAR-based volumetric estimates were compared against fresh weight and dry weight data across different ages of plants, seasons, stages of regrowth, sites, and row configurations.
The project had three phases, the last one comprising four experiments. Phase 1: a LiDAR-based, field-ready prototype mobile platform for perennial ryegrassrecognition in single row plots was developed. Phase 2: real-time volumetric data capture, modelling and analysis software were developed and integrated and the resultant algorithm was validated in the field. Phase 3. LiDAR Volume data were collected via the LiDAR platform and field-validated in four experiments. Expt.1: single-row plots of cultivars and experimental diploid breeding populations were scanned in the southern hemisphere spring for biomass estimation. Significant (< 0.001) correlations were observed between LiDAR Volume and both fresh and dry weight data from 360 individual plots (R = 0.89 and 0.86 respectively). Expt 2: recurrent scanning of single row plots over long time intervals of a few weeks was conducted, and growth was estimated over an 83 day period. Expt 3: recurrent scanning of single-row plots over nine short time intervals of 2 to 5 days was conducted, and growth rate was observed over a 26 day period. Expt 4: recurrent scanning of paired-row plots over an annual cycle of repeated growth and defoliation was conducted, showing an overall mean correlation of LiDAR Volume and fresh weight of R = 0.79 for 1008 observations made across seven different harvests between March and December 2018.
Here we report development and validation of LiDAR-based volumetric estimation as an efficient and effective tool for measuring fresh weight, dry weight and growth rate in single and paired-row plots of perennial ryegrass for the first time, with a consistently high level of accuracy. This development offers precise, non-destructive and cost-effective estimation of these economic traits in the field for ryegrass and potentially other pasture grasses in the future, based on the platform and algorithm developed for ryegrass.
牧场物种产量和生长率的实地测量不准确且成本高昂，限制了其科学和商业应用。我们的研究提出了一种基于激光雷达的移动平台，用于多年生黑麦草（Lolium perenne L.）非侵入性营养生物量和生长率的估算。这包括平台的设计与构建、体积估算算法的开发以及系统的实地验证。将基于激光雷达的体积估算值与不同年龄、季节、再生阶段、地点和行配置的植物的鲜重和干重数据进行比较。
该项目有三个阶段，最后一个阶段包括四个实验。阶段1：开发了一种基于激光雷达的、可在田间使用的原型移动平台，用于单行地块多年生黑麦草的识别。阶段2：开发并集成了实时体积数据采集、建模和分析软件，并在实地对所得算法进行了验证。阶段3：通过激光雷达平台收集激光雷达体积数据，并在四个实验中进行了实地验证。实验1：在南半球春季对品种和实验二倍体育种群体的单行地块进行扫描，以估算生物量。在360个单独地块的激光雷达体积与鲜重和干重数据之间观察到显著（<0.001）相关性（R分别为0.89和0.86）。实验2：对单行地块进行长达数周的长时间间隔的重复扫描，并在83天内估算生长情况。实验3：对单行地块进行9个2至5天的短时间间隔的重复扫描，并在26天内观察生长率。实验4：对双行地块进行重复生长和落叶的年度周期的重复扫描，结果显示，在2018年3月至12月期间的七次不同收获中，对1008次观测数据而言，激光雷达体积与鲜重的总体平均相关性为R = 0.79。
我们首次报告了基于激光雷达的体积估算的开发与验证，它是一种高效且有效的工具，用于测量多年生黑麦草单行和双行地块的鲜重、干重和生长率，且精度始终很高。基于为黑麦草开发的平台和算法，这一进展为未来田间黑麦草以及潜在的其他牧草的这些经济性状提供了精确、无损且经济高效的估算。
