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

Irina Torres; María-Teresa Sánchez; Byoung-Kwan Cho; Ana Garrido-Varo; Dolores Pérez-Marín

doi:10.1117/12.2559102

22 April 2020 Setting up a methodology to distinguish between green oranges and leaves using hyperspectral imaging

Irina Torres, María-Teresa Sánchez, Byoung-Kwan Cho, Ana Garrido-Varo, Dolores Pérez-Marín

Proceedings Volume 11421, Sensing for Agriculture and Food Quality and Safety XII; 114210X (2020) https://doi.org/10.1117/12.2559102
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

The citrus sector is one of the most dynamic and important agricultural sectors. For the international market, it is of great interest the estimation of crop yield prior to harvest, since this yield estimation at the immature green stage could influence the future market price and allow producers to plan the harvest in advance. The aim of this work was to stablish the first steps to set up a methodology for the selection of the relevant bands to distinguish between green oranges and leaves and to detect external defects, which will allow citrus yield to be estimated on tree. Images were acquired from oranges and leaves from an orchard in Jeju island (Jeju, Republic of Korea), using a hyperspectral reflectance imaging system working in the range 400–1000 nm. Analysis of variance (ANOVA) and principal component analysis (PCA) were used to select the main wavelengths for this purpose; next, a band ratio coupled with a simple thresholding method was applied. The system correctly classified over the 90% of the pixels for both objectives, confirming that it is possible to use just few wavelengths to estimate harvest yield in oranges, although further studies are needed for the application of this system in the field, where other factors must be taken into account, such as sun-light illumination, shadows, etc. Therefore, this research can be considered as a preliminary step for designing a multispectral system capable of being mounted on unmanned aerial vehicles (UAVs) to estimate orange yield and defects.

Citation Download Citation

Irina Torres, María-Teresa Sánchez, Byoung-Kwan Cho, Ana Garrido-Varo, and Dolores Pérez-Marín "Setting up a methodology to distinguish between green oranges and leaves using hyperspectral imaging", Proc. SPIE 11421, Sensing for Agriculture and Food Quality and Safety XII, 114210X (22 April 2020); https://doi.org/10.1117/12.2559102

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KEYWORDS

Hyperspectral imaging

Defect detection

Reflectivity

Imaging systems

Principal component analysis

Multispectral imaging

Absorption

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