Green Banana Maturity Classification and Quality Evaluation Using Hyperspectral Imaging

Abstract

Physiological maturity of bananas is of vital importance in determination of their quality and marketability. This study assessed, with the use of a Vis/NIR hyperspectral imaging (400–1000 nm), the feasibility in differentiating six maturity levels (maturity level 2, 4, and 6 to 9) of green dwarf banana and characterizing their quality changes during maturation. Spectra were extracted from three zones (pedicel, middle and apex zone) of each banana finger, respectively. Based on spectra of each zone, maturity identification models with high accuracy (all over 91.53% in validation set) were established by partial least squares discrimination analysis (PLSDA) method with raw spectra. A further generic PLSDA model with an accuracy of 94.35% for validation was created by the three zones’ spectra pooled to omit the effect of spectra acquisition position. Additionally, a spectral interval was selected to simplify the generic PLSDA model, and an interval PLSDA model was built with an accuracy of 85.31% in the validation set. For characterizing some main quality parameters (soluble solid content, SSC; total acid content, TA; chlorophyll content and total chromatism, ΔE*) of banana, full-spectra partial least squares (PLS) models and interval PLS models were, respectively, developed to correlate those parameters with spectral data. In full-spectra PLS models, high coefficients of determination (R2) were 0.74 for SSC, 0.68 for TA, and fair of 0.42 as well as 0.44 for chlorophyll and ΔE*. The performance of interval PLS models was slightly inferior to that of the full-spectra PLS models. Results suggested that models for SSC and TA had an acceptable predictive ability (R2 = 0.64 and 0.59); and models for chlorophyll and ΔE* (R2 = 0.34 and 0.30) could just be used for sample screening. Visualization maps of those quality parameters were also created by applying the interval PLS models on each pixel of the hyperspectral image, the distribution of quality parameters in which were basically consistent with the actual measurement. This study proved that the hyperspectral imaging is a useful tool to assess the maturity level and quality of dwarf bananas.

Nondestructive detection of tomato quality based on multiregion combination model

“…The total soluble solid content and total titratable acidity content of tomatoes are measured by the hand‐held digital display PAL‐BXIACID F5 of Japan ATAGO Company (Resolution: Brix .1%, acid .01%). Drop tomato juice of different parts on the sensor of the digital display, wait 1–2 s to get the data, some studies have shown the specific operation of the experiment (Chu et al, 2022; Lu et al, 2022). The vitamin C content of tomatoes can be obtained by direct iodine titration experiment (Agbemavor et al, 2014).…”

Bio-Inspired Robots and Structures toward Fostering the Modernization of Agriculture

“…Machine vision imitates the ability of most animals to see, while stereo vision is inspired by the calculation of depth information from the views of different points in space, which was thought to be confined only to primates and mammals but has also been demonstrated in other animals, such as birds and amphibians [ 95 ]. These artificial vision mechanisms can be used to enhance the actual systems serving agricultural tasks [ 96 ], including maturity detection for ripe fruit harvesting [ 57 , 97 ] or for vegetables [ 98 ] and similar classification actions [ 99 , 100 ]. Pest and disease detection is also a process typically based on visual techniques [ 52 , 101 , 102 ] and has a direct impact on the quality and quantity of the fruits being collected.…”

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

“…The reduction of redundantinformation based on the chemometric wavelength selection algorithm is furtherinvestigated in the above study to remove redundant information betweenadjacent wavelengths and simplify the model with the potential for onlineapplication. Moreover, PLS-DA and SVM can also beused in the current study to establish spectra with the classification ofripeness of easy-to-ripen fruits such as banana, strawberry, and durian, andthe classification accuracy of the models established based on full-spectrumdata is over 95% [37][38][39][40][41][42][43][44][45][46].In addition, in the harvesting process of easy-to-bump fruit-based food, as thefruit is affected by external factors such as impact, extrusion, or abrasion,its surface will be bruised. As the fruit matures, the internal bruised tissuewill rapidly oxidize and rot, resulting in food loss and waste.…”