Abstract
In the coming decades, Sub-Saharan Africa (SSA) faces challenges to sustainably increase food production while keeping pace with continued population growth. Conservation agriculture (CA) has been proposed to enhance soil health and productivity to respond to this situation. Maize is the main staple food in SSA. To increase maize yields, the selection of suitable genotypes and management practices for CA conditions has been explored using remote sensing tools. They 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 in which Red-Green-Blue (RGB) and multispectral indexes were evaluated for assessing maize performance under conventional ploughing (CP) and CA practices. 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 any negative impact on the performance of the indexes. Most of the calculated indexes (Green Area (GA) and Normalized Difference Vegetation Index (NDVI)) were significantly affected by tillage conditions increasing their values from CP to CA. Indexes 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 indexes with yield were improved by applying a soil-mask derived from a NDVI threshold with the aim of corresponding pixels with vegetation. The results of this study highlight the applicability of remote sensing approaches based on RGB images to the assessment of crop performance and hybrid choice.
Highlights
Traditional practices of land preparation involve soil tillage through moldboard ploughing to soften the seedbed, ensure uniform germination, remove weed plants, and release soil nutrients through mineralization and oxidation
The results suggest, even at early crop growth stages, that the different RGB and multispectral indexes have the potential to effectively assess yield differences under Conservation agriculture (CA) conditions, even if their performance is lower than under conventional ploughing (CP) conditions
This is assumed to be mainly due to the residue cover effect on the measurements; applying a soil mask to the images could help in overcoming this technical problem, which may be best accomplished by the fusion of high resolution RGB with multispectral and/or thermal data or by employing advanced image segmentation algorithms not explored in this study
Summary
Traditional practices of land preparation involve soil tillage through moldboard ploughing to soften the seedbed, ensure uniform germination, remove weed plants, and release soil nutrients through mineralization and oxidation. This mechanical disturbance is leading to a decline in organic matter, an increase of the loss of water by runoff, and, to soil erosion [1]. Sub-Saharan Africa (SSA) is expected to be vulnerable due to the range of projected impacts: e.g., the multiple stresses and low adaptive capacity of current cropping systems, as well as population increase [3]. One of the most effective pathways for adaptation is to focus on breeding new varieties and on changing crop management [5,6,7,8]
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