Machine learning optimised hyperspectral remote sensing retrieves cotton nitrogen status

Hyperspectral imaging spectrometers mounted on unmanned aerial vehicle (UAV) can capture high spatial and spectral resolution to provide cotton crop nitrogen status for precision agriculture. The aim of this research was to explore machine learning use with hyperspectral datacubes over agricultural fields. Hyperspectral imagery was collected over a mature cotton crop, which had high spatial (~5.2 cm) and spectral (5 nm) resolution over the spectral range 475–925 nm that allowed discrimination of individual crop rows and field features as well as a continuous spectral range for calculating derivative spectra. The nominal reflectance and its derivatives clearly highlighted the different treatment blocks and were strongly related to N concentration in leaf and petiole samples, both in traditional vegetation indices (e.g., Vogelman 1, R2 = 0.8) and novel combinations of spectra (R2 = 0.85). The key hyperspectral bands identified were at the red-edge inflection point (695–715 nm). Satellite multispectral was compared against the UAV hyperspectral remote sensing’s performance by testing the ability of Sentinel MSI to predict N concentration using the bands in VIS-NIR spectral region. The Sentinel 2A Green band (B3; mid-point 559.8 nm) explained the same amount of variation in N as the hyperspectral data and more than the Sentinel Red Edge Point 1 (B5; mid-point 704.9 nm) with the lower 10 m resolution Green band reporting an R2 = 0.85, compared with the R2 = 0.78 of downscaled Sentinel Red Edge Point 1 at 5 m. The remaining Sentinel bands explained much lower variation (maximum was NIR at R2 = 0.48). Investigation of the red edge peak region in the first derivative showed strong promise with RIDAmid (R2 = 0.81) being the best index. The machine learning approach narrowed the range of bands required to investigate plant condition over this trial site, greatly improved processing time and reduced processing complexity. While Sentinel performed well in this comparison and would be useful in a broadacre crop production context, the impact of pixel boundaries relative to a region of interest and coarse spatial and temporal resolution impacts its utility in a research capacity

A bird's eye-view of smallholder productivity : Current measurement shortfalls, farmer perceptions and rationality on rainfed family farms in Ghana

Smallholder farming, which is largely rainfed and relies on mostly rudimentary tools, predominates Ghana’s agricultural sector. The sector’s importance to the national economy is exemplified in not only the proportion of the active national labour force engaged in it but also in terms of its export earnings and service as a source of food for the vast majority of the population. However, the sectors is also plagued with historically low productivity. Statistics from the country’s national statistical service shows that while more than six tons per hectare is achievable for maize, what farmers actually obtain from their plots is less than a third of this. Being the most important staple crop and given the continuously increasing population growth rate, these low yield levels are most worrying. This is particularly so if the agriculture sector is expected to play the historically important role of being the engine of economic growth for the national economy as it has been for other countries.Through this thesis, I aim to augment our present understanding of crop productivity levels on smallholder family farms. I do this by showing the limitations of current methods of yield measurement, analysing the factors contributing to current yield levels and variability, as well as analysing farmers’ perspectives on their current productivity levels. Using a multidisciplinary framework, I employ a mixed methods approach to analyse data from field and household surveys as well as aerial photographs and photo-elicitation interviews. For inspiration, I also draw on a number of theories; Boserup’s theory on agricultural intensification in the face of population growth and Chayanov’s theory of the smallholder economy help provide the frame for the thesis. The more practical induced innovation model of agricultural development and the sustainable livelihood approach help provide the bridge to the empirical work.The thesis comprises four articles, which are preceded by a kappa. I argue that current measurement approaches do not adequately capture the dynamism of smallholder farms and that the use of new remote sensing tools as employed in this work could be critical to improving the reliability of agricultural statistics in such complex farming systems. I also argue that the factors contributing to current yield levels are varied and inconsistent across yield measures and villages even in the same agroecological regions. I further argue that while management activities such as the timing of planting and quantity of fertilizer applied are important immediate determinants of yield levels, they are often underpinned by some socioeconomic factors relating to labour and land tenure dynamics. The thesis further establishes that, by and large, farmers are content with current productivity levels and this attitude is rationally based on their experience and knowledge of poorly functioning agricultural inputs and outputs market. These findings have significant implications for the future of these small farms in terms of their own survival as well as their ability to continue to play critical roles in the economies of developing countries in Sub-Saharan Africa