Towards real-time speckle image processing for mealiness assessment in apple fruit

Abstract

Biospeckle activity (during 1-25 s) of 'Red Delicious' apples was recorded under 680 and 780 nm laser diodes. The time history speckle pattern (THSP) images were configured and evaluated using the common biospeckle and texture descriptors. Several artificial neural networks (ANN) models were developed to predict the stiffness and juiciness of apple fruits. At the recording time of 1 s and 680 nm laser diode, the ANN models developed by texture features showed better performance in prediction of stiffness (r = 0.70 and SEP = 8.4 kN) and juiciness (r = 0.68 and SEP = 2.3 cm2) as compared to the common biospeckle features-based ones (r = 0.25 and SEP = 11.3 kN m −1 for stiffness and r = 0.29 and SEP = 3.1 cm 2 for juiciness). Furthermore, classification of apples into fresh, semi-mealy, and mealy groups was carried out. At the recording time of 25 s and 680 nm laser, the ANN models developed by texture features classified fresh, semi-mealy, and mealy apples with accuracies of 86.3, 65.9, and 95.5%, respectively. Reduction of the recording time to 1 s resulted in the classification accuracies of 88.2, 68.5, and 85.5% for mealy, semi-mealy, and mealy apples compared to the common biospeckle features-based ANN models with the accuracies of 82.8, 50.7, and 46.8%, respectively. These results indicated that the texture descriptors had better performance at shorter recording times in comparison with the biospeckle features which are commonly used in evaluation of biospeckle images, even though more improvements are still required.

Cell wall modifications lead to cultivar differences in apple (Malus domestica) fruit mealiness

“…Mealiness is considered a significant deterioration of fruit flesh texture, as it is commonly associated with a dry texture, a soft, unpleasant taste in the mouth, and a lack of juiciness and aroma (Bowen et al, 2019;Hunter et al, 2021). Mealiness has been widely investigated in numerous fruit during the storage and softening process, such as in apples (Arefi et al, 2017), pears (Cronje, 2019;Dong et al, 2018;Dong et al, 2019), squash fruit (Sameshima et al, 2018), tomatoes (Hunter et al, 2021), and peaches (NiloPoyanco et al, 2019;Tatsuki et al, 2021). In apples, the level of mealiness relies greatly on growing or storage conditions, size of fruit, date of harvesting, and cultivar type (Mollazade and Arefi, 2017).…”

Early prediction of internal bruising in potatoes by biospeckle laser technique

“…Over time, the successive patterns of biosepckle become susceptible to be tracked, and the changes of biospeckle patterns are associated with biological activity (Braga, 2017;Stoykova et al, 2017). The correlation between the biological activity of a biological materials and variation of the biospeckle pattern over time can be observed in several relevant researches in medicine that studied the monitoring of melanoma (González-Peña et al, 2014) in the evaluation of the activity of bacteria and parasites (Ansari et al, 2016;Grassi et al, 2016) and other variety of applications in agriculture (Zdunek et al, 2014;Retheesh et al, 2016;Sutton and Punja, 2017;Arefi et al, 2018). Szymanska-Chargot et al (2012) and Gao et al (2016) presented the biospeckle laser as a feasible way to detect bruising process in apples and potatoes after mechanical impacts.…”