Dual-band feature selection for maturity classification of specialty crops by hyperspectral imaging

• URL: http://arxiv.org/abs/2405.09955v2
• Date: Fri, 17 May 2024 08:35:51 GMT
• Title: Dual-band feature selection for maturity classification of specialty crops by hyperspectral imaging
• Authors: Usman A. Zahidi, Krystian Łukasik, Grzegorz Cielniak,
• Abstract summary: The maturity classification of specialty crops such as strawberries and tomatoes is an essential agricultural downstream activity. Recent advancements in Deep Learning have produced encouraging results in color images for maturity classification applications. We present a feature extraction method to compute yet distinctive features for the maturity classification.
• Score: 5.905721043072562
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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