The potential of in-situ hyperspectral remote sensing for differentiating 12 banana genotypes grown in Uganda

Abstract

Bananas and plantains provide food and income for more than 50 million smallholder farmers in East and Central African (ECA) countries. However, banana productivity generally achieves less than optimal yield potential (< 30%) in most regions, including Uganda. Numerous studies have been undertaken to identify the key challenges that smallholder banana growers face at different stages of the banana value chain, with one of the main constraints being a lack of policy-relevant agricultural data. The World Bank (WB) initiated a methodological survey design aimed at identifying the distribution of banana varieties across a number of key Ugandan growing regions, at the individual household scale. To achieve this outcome a number of approaches including ground-based surveys, DNA tissue collection of selected banana plants and remote sensing were evaluated. For the remote sensing component, the set objectives were to develop statistical models from the hyperspectral reflectance properties of individual leaves that could differentiate typical ECA banana varieties, as well as their parentage (usage). The study also explored the potential of extrapolating the ground-based hyperspectral measures to high-resolution WorldView-3 (WV3) satellite imagery, therefore creating the potential of mapping the distribution of banana varieties at a regional scale. The DNA testing of 43 banana varieties propagated at the National Banana Research Program site at National Agricultural Research Organization (NARO) research station in Kampala, Uganda, identified 12 genetically different varieties. A canonical powered partial least square (CPPLS) model developed from hyperspectral reflectance properties of the sampled banana leaves successfully differentiated BLU, BOG, GON, GRO and KAY genotypes. The Random Forest (RF) algorithm was also evaluated to determine if spectral bands coinciding with those provided by WV3 data could segregate banana varieties. The results suggested that this was achievable and as such presents an opportunity to extrapolate the hyperspectral classifications to broader areas of land. The ability to spectrally differentiate these five genotypes has merit as they are not typical east African varieties. As such, identifying the distribution and density of these varieties across Uganda provides vital information to the banana breeders of NARO of where their new varieties are being disseminated too, data that has been previously difficult to obtain. Although the results from this pilot study indicated that not all banana varieties could be spectrally differentiated, the methodology developed and the positive results that were achieved do present remote sensing as a complimentary technology to the ongoing surveying of banana and other crop types grown within Ugandan household farming systems.