What can artificial intelligence approaches bring to an improved and efficient harvesting and postharvest handling of date fruit (Phoenix dactylifera L.)? A review

Noutfia, Younes; Ropelewska, Ewa

doi:10.1016/j.postharvbio.2024.112926

Cited by 1 publication

(2 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At this complete stage of maturity, the fruit usually has a reduced moisture content of less than 20–25%, and it is considered appropriate for consumption in many regions and for storage under specific and mastered conditions [ 4 , 5 ]. However, some physiological and physical disorders can occur at this stage, mainly related to skin separation and sugar spots.…”

Section: Introductionmentioning

confidence: 99%

“…Also, infrared drying can be more beneficial compared with conventional drying due to rapid processing, shorter heating times, chemical compound preservation, and reduced risk of flavor loss [ 15 ]. Artificial intelligence and image analysis can be considered innovative and alternative approaches to assess the quality of date fruit before and after drying instead of tedious, time-consuming, and destructive analyses [ 5 ]. In doing so, machine vision enables the quantitative analysis of the quality of the qualitative criteria of the sample.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Exploration of Convective and Infrared Drying Effect on Image Texture Parameters of ‘Mejhoul’ and ‘Boufeggous’ Date Palm Fruit Using Machine Learning Models

Noutfia,

Ropelewska

2024

Foods

Self Cite

View full text Add to dashboard Cite

Date palm (Phoenix dactylifera L.) fruit samples belonging to the ‘Mejhoul’ and ‘Boufeggous’ cultivars were harvested at the Tamar stage and used in our experiments. Before scanning, date samples were dried using convective drying at 60 °C and infrared drying at 60 °C with a frequency of 50 Hz, and then they were scanned. The scanning trials were performed for two hundred date palm fruit in fresh, convective-dried, and infrared-dried forms of each cultivar using a flatbed scanner. The image-texture parameters of date fruit were extracted from images converted to individual color channels in RGB, Lab, XYZ, and UVS color models. The models to classify fresh and dried samples were developed based on selected image textures using machine learning algorithms belonging to the groups of Bayes, Trees, Lazy, Functions, and Meta. For both the ‘Mejhoul’ and ‘Boufeggous’ cultivars, models built using Random Forest from the group of Trees turned out to be accurate and successful. The average classification accuracy for fresh, convective-dried, and infrared-dried ‘Mejhoul’ reached 99.33%, whereas fresh, convective-dried, and infrared-dried samples of ‘Boufeggous’ were distinguished with an average accuracy of 94.33%. In the case of both cultivars and each model, the higher correctness of discrimination was between fresh and infrared-dried samples, whereas the highest number of misclassified cases occurred between fresh and convective-dried fruit. Thus, the developed procedure may be considered an innovative approach to the non-destructive assessment of drying impact on the external quality characteristics of date palm fruit.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%