Postharvest bitter pit detection and progression evaluation in ‘Honeycrisp’ apples using computed tomography images

Abstract

Bitter pit is a physiological disorder that is defined as brown, corky and roundish lesions, which can develop in apples before and after harvest. This disorder greatly reduces the product utilization value of the fruit, and can result in several million dollar economic loss to the apple industry. Computed Tomography (CT) imaging is a non-destructive and rapid sensing technique that can be applied to packaged apples. In this study, healthy and bitter pit Honeycrisp apples were harvested from two field sites and stored for 63 days. CT images of the sampled apples were collected on 0, 7, 14, 21, 35 and 63 days after harvest. Images were analyzed to estimate the total pit area in each of the individual apples and were related to pit incidence and progression in different stages of storage. Results showed pit development during the storage period in bitter pitted apples. The rate of progression differed in samples collected from different field sites. Further analysis for pit distribution along each of the bitter pit affected apples showed 54% of pits located at the calyx-end of apples in comparison with middle and stem-end. Classification of healthy and bitter pitted apples using logistic regression based method resulted in false negative of 7-21%.

Control efficacy of Ca‐containing foliar fertilizers on bitter pit in bagged ‘Fuji’ apple and effects on the Ca and N contents of apple fruits and leaves

“…In short, the Ca content, N content and N/Ca ratio of pitted tissue was between that of healthy fruit and healthy tissue surrounding the pitted regions with no significant difference. Our results were consistent with those of previous reports and confirmed that BP is related to low Ca contents and high N contents of fruits . The results also indicated that the fruit sampling for the Ca and N analysis in our experiment was feasible; thus the results of Ca and N analysis would be reliable for the evaluation of the efficacy of Ca sprays on the Ca content, N content, and the N/Ca ratio of bagged apple fruits and leaves in our study, completely excluding the influence of the BP exhibition in fruits.…”

“…Bitter pit is a major problem in the apple‐growing industry worldwide and can result in severe economic losses . The universally acknowledged hypothesis associated with BP formation in fruits during production is a deficiency in Ca 2+ due to the improper uptake of nutrients by plants as well as high N content and N/Ca ratios in fruits . The Ca content, N content, and N/Ca ratio in different parts of pitted apple fruits may be different.…”

Construction design of apple sorter

“…The identification of the fruit ends with sending a signal to the controller to open the corresponding latch. Many research works were devoted to issues of apples quality, including: [2,15,16,17,18,19]. However, inspection for damaged, diseased, or contaminated fruit has been hindered by the inability to appropriately orient fruit for imaging and by lack of a method for imaging the whole of the surface of individual fruit [18].…”

“…Also a very important factor in the assessment of the quality of the technological process is the condition of sorted fruit. Therefore, fruit inspection and sorting by human and automated imaging systems on the packaging lines are critical while packing premium quality fruits without any defects [17]. In case of apples, defects and disorders include arthropod damage (codling moth, mealybugs, aphid damage), postharvest diseases (blue mold, gray mold, mildew), physiological disorders (bitter pit, superficial scald, core flush), mechanical and field injuries (bruising, frost, hail damage), and fruit finish (sunburns).…”

X‐ray computed tomography for quality inspection of agricultural products: A review

“…In the same way, Honeycrisp apples with bitter pit before and after harvesting could be identified with CT slice images. And the classification accuracy of bitter pit and healthy fruit ranged between 70% and 96% (Jarolmasjed, Espinoza, Sankaran, & Khot, 2016). Moreover, X-ray CT technology is also applied for the quantification of salt concentrations on meat (Vestergaard, Risum, & Adler-Nissen, 2004).…”