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 2 publications

(2 citation statements)

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“…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%

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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

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

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Non-Destructive Monitoring of External Quality of Date Palm Fruit (Phoenix dactylifera L.) During Frozen Storage Using Digital Camera and Flatbed Scanner

Noutfia,

Ropelewska,

Jóźwiak

et al. 2024

Sensors

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The emergence of new technologies focusing on “computer vision” has contributed significantly to the assessment of fruit quality. In this study, an innovative approach based on image analysis was used to assess the external quality of fresh and frozen “Mejhoul” and “Boufeggous” date palm cultivars stored for 6 months at −10 °C and −18 °C. Their quality was evaluated, in a non-destructive manner, based on texture features extracted from images acquired using a digital camera and flatbed scanner. The whole process of image processing was carried out using MATLAB R2024a and Q-MAZDA 23.10 software. Then, extracted features were used as inputs for pre-established algorithms–groups within WEKA 3.9 software to classify frozen date fruit samples after 0, 2, 4, and 6 months of storage. Among 599 features, only 5 to 36 attributes were selected as powerful predictors to build desired classification models based on the “Functions-Logistic” classifier. The general architecture exhibited clear differences in classification accuracy depending mainly on the frozen storage period and imaging device. Accordingly, confusion matrices showed high classification accuracy (CA), which could reach 0.84 at M0 for both cultivars at the two frozen storage temperatures. This CA indicated a remarkable decrease at M2 and M4 before re-increasing by M6, confirming slight changes in external quality before the end of storage. Moreover, the developed models on the basis of flatbed scanner use allowed us to obtain a high correctness rate that could attain 97.7% correctness in comparison to the digital camera, which did not exceed 85.5%. In conclusion, physicochemical attributes can be added to developed models to establish correlation with image features and predict the behavior of date fruit under storage.

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

Cited by 2 publications

References 62 publications

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

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

Non-Destructive Monitoring of External Quality of Date Palm Fruit (Phoenix dactylifera L.) During Frozen Storage Using Digital Camera and Flatbed Scanner

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