Feature extraction of hyperspectral images for detecting immature green citrus fruit

At an early immature growth stage of citrus, a hyperspectral camera of 369–1042 nm was employed to acquire 30 hyperspectral images in order to detect immature green fruit within citrus trees under natural illumination conditions. First, successive projections algorithm (SPA) were implemented to select 677, 804, 563, 962, and 405 nm wavebands and to construct multispectral images from the original hyperspectral images for further processing. Then, histogram threshold segmentation using NDVI of 804 and 677 nm was implemented to remove image backgrounds. Three slope parameters, calculated from the pairs 405 and 563 nm, 563 and 677 nm, and 804 and 962 nm were used to construct a classifier to identify the potential citrus fruit. Then, a marker-controlled watershed segmentation based on wavelet transform was applied to obtain potential fruit areas. Finally, a green fruit detection model was constructed according to Grey Level Co-occurrence Matrix (GLCM) texture features of the independent areas. Three supervised classifiers, logistic regression, random forest and support vector machine (SVM) were developed using texture features. The detection accuracies were 79%, 75%, and 86% for the logistic regression, random forest, and SVM models, respectively. The developed algorithm showed a great potential for identifying immature green citrus for an early yield estimation.