Innovative Models Built Based on Image Textures Using Traditional Machine Learning Algorithms for Distinguishing Different Varieties of Moroccan Date Palm Fruit (Phoenix dactylifera L.)

Abstract

The aim of this study was to develop the procedure for the varietal discrimination of date palm fruit using image analysis and traditional machine learning techniques. The fruit images of ‘Mejhoul’, ‘Boufeggous’, ‘Aziza’, ‘Assiane’, and ‘Bousthammi’ date varieties, converted to individual color channels, were processed to extract the texture parameters. After performing the attribute selection, the textures were used to build models intended for the discrimination of different varieties of date palm fruit using machine learning algorithms from Functions, Bayes, Lazy, Meta, and Trees groups. Models were developed for combining image textures selected from a set of all color channels and for sets of textures selected for individual color spaces and color channels. The models, including combined textures selected from all color channels, distinguished all five varieties with an average accuracy reaching 98%, and ‘Bousthammi’ and ‘Mejhoul’ were completely correctly discriminated for the SMO (Functions) and IBk (Lazy) machine learning algorithms. By reducing the number of varieties, the correctness of the date palm fruit classification increased. The models developed for the three most different date palm fruit varieties ‘Boufeggous’, ‘Bousthammi’, and ‘Mejhoul’ revealed an average discrimination accuracy of 100% for each algorithm used (SMO, Naive Bayes (Bayes), IBk, LogitBoost (Meta), and LMT (Trees)). In the case of individual color spaces and channels, the accuracies were lower, reaching 97.3% for color space RGB and SMO and LMT algorithms for all five varieties and 99.63% for Naive Bayes and IBk for the ‘Boufeggous’, ‘Bousthammi’, and ‘Mejhoul’ date palm fruits. The results can be used in practice to develop vision systems for sorting and distinguishing the varieties of date palm fruit to authenticate the variety of the fruit intended for further processing.

Pemanfaatan Teknologi Informasi dalam Optimalisasi Produksi Tanaman Pangan: Studi Bibliometrik Skala Nasional

“…Selain itu, citra multispektral berbasis UAV telah digunakan untuk menilai status kesehatan tanaman di ladang gandum dan gandum, mencapai akurasi keseluruhan rata-rata 94% dalam membedakan antara area tanaman yang kuat dan tertekan (Vlachopoulos et al, 2021). Algoritma pembelajaran mesin telah digunakan dalam berbagai aplikasi pertanian, seperti membedakan varietas buah yang berbeda (Noutfia & Ropelewska, 2022), dan menganalisis kualitas tanah (Shaheen & Iqbal, 2018). Fenotip tanaman throughput tinggi, yang melibatkan penggunaan…”

DPXception: a lightweight CNN for image-based date palm species classification

“…In recent years, deep learning has shown remarkable success in image classification tasks in plant species identification and other domains ( Kussul et al., 2017 ; He et al., 2018 ; Kamilaris and Prenafeta-Boldú, 2018 ; Li et al., 2018 ; Zhang et al., 2021 ; Bouguettaya et al., 2022 ). As a result, deep learning methods have emerged as a promising alternative for date palm variety identification ( Haidar et al., 2012 ; Altaheri et al., 2019 ; Nasiri et al., 2019 ; Albarrak et al., 2022 ; Jintasuttisak et al., 2022 ; Alsirhani et al., 2023 ; Noutfia and Ropelewska, 2023a ; Noutfia and Ropelewska, 2023b ). Deep learning is a branch of machine learning that uses artificial neural networks with multiple layers to learn complex features from large amounts of data ( LeCun et al., 2015 ; Goodfellow et al., 2016 ).…”

“…Deep learning methods have several advantages over traditional and molecular methods: they are fast and easy to use; they do not require expert knowledge or manual intervention; and they can work with any parts of the date palm plant (such as leaves, stems, or fruits). Recent deep learning methods have been proposed to identify date palm species based on their fruits ( Haidar et al., 2012 ; Altaheri et al., 2019 ; Nasiri et al., 2019 ; Albarrak et al., 2022 ; Jintasuttisak et al., 2022 ; Alsirhani et al., 2023 ; Noutfia and Ropelewska, 2023a ; Noutfia and Ropelewska, 2023b ). The main drawback of these methods is that they are designed for harvesting purposes when fruits are present; however, they cannot be used to identify date palm species when the fruits are not present or visible.…”

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

“…In doing so, machine vision enables the quantitative analysis of the quality of the qualitative criteria of the sample. Machine learning (ML) models can be successful in the classification of different samples of date fruit based on selected image parameters using several algorithms and models (e.g., traditional ML algorithms) [ 16 ]. Furthermore, image textures and determined geometric parameters using image analysis can be useful in the objective characterization of date fruit [ 17 ].…”