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Phenotyping conservation agriculture management effects on ground and aerial remote sensing assessments of maize hybrids performance in Zimbabwe
1, 2 , 3 , 4 , 4 , * 3 , 3
1  Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Avda. Diagonal 643, 08028 Barcelona, Spain
2  Master's degree in Environmental Agrobiology, Faculty of Biology, University from Barcelona, Avda. Diagonal 643, 08028 Barcelona, Spain
3  Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Barcelona
4  International Maize and Wheat Improvement Center, CIMMYT Southern Africa Regional Office, Harare, Zimbabwe


In the coming decades, Sub-Saharan Africa (SSA) faces the challenge of increasing the rate of food production, in a sustainable manner, to keep pace with the continued population growth. To do so, conservation agriculture (CA) is being proposed for developing countries in order to enhance soil health and productivity. On the other hand, maize is the main staple in SSA.  Thus, attempts to increase maize yields have to be focused in the selection of suitable genotypes and management practices for CA conditions, in which remote sensing tools may play a fundamental role towards overcoming the traditional limitations of data collection and processing in large scale phenotyping studies. We present the result of a study where Red-Green-Blue (RGB) and multispectral indices were evaluated for assessing maize performance under conventional ploughing (CP) and CA practices, and where the CA strategies resulted in higher yields. Eight hybrids under different planting densities and tillage practices were tested. The measurements were conducted on seedlings at ground level (0.8 m) and from an unmanned aerial vehicle (UAV) platform (30 m), causing a platform proximity effect on the images resolution that did not have a negative impact on the performance of the indices. Most of the indices calculated were significantly affected by the tillage conditions increasing their values from CP to CA, as the Green Area (GA) or the Normalized Difference Vegetation Index (NDVI). Indices derived from the RGB-images related to canopy greenness performed better at assessing yield differences, potentially due to the greater resolution of the RGB compared with the multispectral data, although this performance was more precise for CP than CA. The correlations of the multispectral indices with yield were improved by applying a soil-mask derived from a NDVI threshold and then aim the measurements on pixels corresponding to vegetation. This study highlights the applicability of remote sensing approaches based in the use of RGB images to assess crops performance and hybrid choice.

Keywords: maize; remote sensing; plant phenotyping; UAV; RGB; multispectral; conservation agriculture; Sub-Saharan Africa.