  The maturity classification of specialty crops such as strawberries and
tomatoes is an essential agricultural downstream activity for selective
harvesting and quality control (QC) at production and packaging sites. Recent
advancements in Deep Learning (DL) have produced encouraging results in color
images for maturity classification applications. However, hyperspectral imaging
(HSI) outperforms methods based on color vision. Multivariate analysis methods
and Convolutional Neural Networks (CNN) deliver promising results; however, a
large amount of input data and the associated preprocessing requirements cause
hindrances in practical application. Conventionally, the reflectance intensity
in a given electromagnetic spectrum is employed in estimating fruit maturity.
We present a feature extraction method to empirically demonstrate that the peak
reflectance in subbands such as 500-670 nm (pigment band) and the wavelength of
the peak position, and contrarily, the trough reflectance and its corresponding
wavelength within 671-790 nm (chlorophyll band) are convenient to compute yet
distinctive features for the maturity classification. The proposed feature
selection method is beneficial because preprocessing, such as dimensionality
reduction, is avoided before every prediction. The feature set is designed to
capture these traits. The best SOTA methods, among 3D-CNN, 1D-CNN, and SVM,
achieve at most 90.0 % accuracy for strawberries and 92.0 % for tomatoes on our
dataset. Results show that the proposed method outperforms the SOTA as it
yields an accuracy above 98.0 % in strawberry and 96.0 % in tomato
classification. A comparative analysis of the time efficiency of these methods
is also conducted, which shows the proposed method performs prediction at 13
Frames Per Second (FPS) compared to the maximum 1.16 FPS attained by the
full-spectrum SVM classifier.
