Setting up a methodology to distinguish between green oranges and leaves using hyperspectral imaging

Abstract

The estimation of green citrus fruit yield is a key parameter for growers and the industry. The early estimation of orange yield at the immature green stage could influence the future market price and allow producers to plan the harvest in advance, thus reducing costs. This research can be considered as a preliminary step for designing low-cost spectral cameras capable of being mounted on unmanned aerial vehicles (UAVs) to estimate orange yield and defects. Images were acquired from oranges and leaves from an orchard in Jeju Island (Jeju, Republic of Korea), using two hyperspectral reflectance imaging systems, one working in the range 400–1000 nm (visible/near infrared, Vis/NIR) and the other between 900 and 2500 nm (short-wave infrared, SWIR). The main objective of the research was to set up a methodology to select the relevant bands - from the two spectral ranges studied - to distinguish between green oranges and leaves and to detect defects, which will allow citrus yield to be estimated. Analysis of variance (ANOVA) and principal component analysis (PCA) were used to select the key wavelengths for this purpose; next, a band ratio coupled with a simple thresholding method was applied. This study showed that the Vis/NIR hyperspectral imaging correctly classified 96.97% and 92.93% of the pixels, respectively, to distinguish between green oranges and leaves and to detect defects, while with the SWIR system, the percentage of pixels correctly classified for these two objectives were 74.79% and 89.31%, respectively. These results confirm that it is possible to use a low number of wavelengths to estimate harvest yield in oranges, which could pave the way for the future development of low-cost and low-weight equipment for the detection of green and sound fruit.