最近，来自法国的科学家利用Airphen多光谱成像系统发表了题为Using UAV Borne, Multi-Spectral Imaging for the Field Phenotyping of Shoot Biomass, Leaf Area Index and Height of West African Sorghum Varieties under Two Contrasted Water Conditions的文章。 

Then after, we perform another flflight with an Airphen multispectral camera (hiphen, Avignon, France, https://www.hiphen-plant.com/, accessed on 15 December 2020) equipped with an 8 mm focal length lens and acquiring 1280 × 960 pixel images). The Airphen comprises six individual cameras equipped with fifilters centered on 450, 530, 560, 675, 730 and 850 nm, with a spectral resolution of 10 nm. For each camera (RGB and MS), the flflight lasted about 15 min with around 10 min of preparing the second flflight. The cameras captured images at one-second intervals and recorded them in JPG and Tiff format on the SD memory card. The drone did round trip spaced of 4 m that allow a side and forward overlapping fraction of 0.75. To reduce effects of ambient light condition, we limited flflight to clear and cloudless days between 10:00 to 12:00 A.M. (Greenwich Mean Time, GMT) that allowed to reduce the plants shadow effect as its contribution between rows in mature stages can greatly affect spectral measures.

Using UAV Borne, Multi-Spectral Imaging for the Field Phenotyping of Shoot Biomass, Leaf Area Index and Height of West African Sorghum Varieties under Two Contrasted Water Conditions

byBoubacar Gano1,2,*,Joseph Sékou B.Dembele1,2,Adama Ndour1,Delphine Luquet3,4,Gregory Beurier3,4,Diaga Diouf2andAlain Audebert1,3,4,*

1Centre d’Etude Régional pour l’Amélioration de l’Adaptation à la Sécheresse (CERAAS), Institut Sénégalais de Recherches Agricoles (ISRA), Route de Khombole, Thiès BP 3320, Senegal

2Laboratoire Campus de Biotechnologies Végétales, Département de Biologie Végétale, Faculté des Sciences et Techniques, Université Cheikh Anta Diop de Dakar, Dakar BP 5005, Senegal

3Centre de coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), UMR AGAP Institut, F-34398 Montpellier, France

4UMR AGAP Institut, Université Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France

Agronomy 2021, 11(5), 850; https://doi.org/10.3390/agronomy11050850

Received: 21 January 2021 / Revised: 19 February 2021 / Accepted: 22 February 2021 / Published: 27 April 2021

(This article belongs to the Section Innovative Cropping Systems)

Abstract

Meeting food demand for the growing population will require an increase to crop production despite climate changes and, more particularly, severe drought episodes. Sorghum is one of the cereals most adapted to drought that feed millions of people around the world. Valorizing its genetic diversity for crop improvement can benefit from extensive phenotyping. The current methods to evaluate plant biomass, leaves area and plants height involve destructive sampling and are not practical in breeding. Phenotyping relying on drone based imagery is a powerful approach in this context. The objective of this study was to develop and validate a high throughput field phenotyping method of sorghum growth traits under contrasted water conditions relying on drone based imagery. Experiments were conducted in Bambey (Senegal) in 2018 and 2019, to test the ability of multi-spectral sensing technologies on-board a UAV platform to calculate various vegetation indices to estimate plants characteristics. In total, ten (10) contrasted varieties of West African sorghum collection were selected and arranged in a randomized complete block design with three (3) replicates and two (2) water treatments (well-watered and drought stress). This study focused on plant biomass, leaf area index (LAI) and the plant height that were measured weekly from emergence to maturity. Drone flights were performed just before each destructive sampling and images were taken by multi-spectral and visible cameras. UAV-derived vegetation indices exhibited their capacity of estimating LAI and biomass in the 2018 calibration data set, in particular: normalized difference vegetative index (NDVI), corrected transformed vegetation index (CTVI), seconded modified soil-adjusted vegetation index (MSAVI2), green normalize difference vegetation index (GNDVI), and simple ratio (SR) (r2 of 0.8 and 0.6 for LAI and biomass, respectively). Developed models were validated with 2019 data, showing a good performance (r2 of 0.92 and 0.91 for LAI and biomass accordingly). Results were also promising regarding plant height estimation (RMSE = 9.88 cm). Regression plots between the image-based estimation and the measured plant height showed a r2 of 0.83. The validation results were similar between water treatments. This study is the first successful application of drone based imagery for phenotyping sorghum growth and development in a West African context characterized by severe drought occurrence. The developed approach could be used as a decision support tool for breeding programs and as a tool to increase the throughput of sorghum genetic diversity characterization for adaptive traits.

Keywords: sorghum; drought tolerance; West Africa; phenotyping; UAV platform; vegetation indices; multi-spectral; RGB cameras