1,691 research outputs found
A Systematic Review and Comparative Meta-analysis of Non-destructive Fruit Maturity Detection Techniques
The global fruit industry is growing rapidly due to increased awareness of the health benefits associated with fruit consumption. Fruit maturity detection plays a crucial role in fruit logistics and maintenance, enabling farmers and fruit industries to grade fruits and develop sustainable policies for enhanced profitability and service quality. Non-destructive fruit maturity detection methods have gained significant attention, especially with advancements in machine vision and spectroscopic techniques. This systematic review provides a concise overview of the techniques and algorithms used in fruit quality grading by farmers and industries. The study reviewed 63 full-text articles published between 2012 and 2023 along with their bibliometric analysis. Qualitative analysis revealed that researchers from various disciplines contributed to this field, with techniques falling into 3 categories: machine vision (mathematical modelling or deep learning), spectroscopy and other miscellaneous approaches. There was a high level of diversity among these categories, as indicated by an I-square value of 88.37% in the heterogeneity analysis. Meta-analysis, using odds ratios as the effect measure, established the relationship between techniques and their accuracy. Machine vision showed a positive correlation with accuracy across different categories. Additionally, Egger's and Begg's tests were used to assess publication bias and no strong evidence of its occurrence was found. This study offers valuable insights into the advantages and limitations of various fruit maturity detection techniques. For employing statistical and meta-analytical methods, key factors such as accuracy and sample size have been considered. These findings will aid in the development of effective strategies for fruit quality assessment
Computer Vision System for Non-Destructive and Contactless Evaluation of Quality Traits in Fresh Rocket Leaves (Diplotaxis Tenuifolia L.)
La tesi di dottorato è incentrata sull'analisi di tecnologie non distruttive per il controllo della
qualitĂ dei prodotti agroalimentari, lungo l'intera filiera agroalimentare. In particolare, la tesi
riguarda l'applicazione del sistema di visione artificiale per valutare la qualitĂ delle foglie di
rucola fresh-cut. La tesi è strutturata in tre parti (introduzione, applicazioni sperimentali e
conclusioni) e in cinque capitoli, rispettivamente il primo e il secondo incentrati sulle
tecnologie non distruttive e in particolare sui sistemi di computer vision per il monitoraggio
della qualitĂ dei prodotti agroalimentari. Il terzo, quarto e quinto capitolo mirano a valutare le
foglie di rucola sulla base della stima di parametri qualitativi, considerando diversi aspetti: (i)
la variabilitĂ dovuta alle diverse pratiche agricole, (ii) la senescenza dei prodotti confezionati
e non, e (iii) lo sviluppo e sfruttamento dei vantaggi di nuovi modelli piĂą semplici rispetto al
machine learning utilizzato negli esperimenti precedenti. Il lavoro di ricerca di questa tesi di
dottorato è stato svolto dall'Università di Foggia, dall'Istituto di Scienze delle Produzioni
Alimentari (ISPA) e dall'Istituto di Tecnologie e Sistemi Industriali Intelligenti per le
Manifatture Avanzate (STIIMA) del Consiglio Nazionale delle Ricerche (CNR). L’attività di
ricerca è stata condotta nell'ambito del Progetto SUS&LOW (Sustaining Low-impact Practices
in Horticulture through Non-destructive Approach to Provide More Information on Fresh
Produce History & Quality), finanziato dal MUR-PRIN 2017, e volto a sostenere la qualitĂ
della produzione e dell'ambiente utilizzando pratiche agricole a basso input e la valutazione
non distruttiva della qualitĂ di prodotti ortofrutticoli.The doctoral thesis focused on the analysis of non-destructive technologies available for the
control quality of agri-food products, along the whole supply chain. In particular, the thesis
concerns the application of computer vision system to evaluate the quality of fresh rocket
leaves. The thesis is structured in three parts (introduction, experimental applications and
conclusions) and in 5 chapters, the first and second focused on non-destructive technologies
and in particular on computer vision systems for monitoring the quality of agri-food products,
respectively. The third, quarter, and fifth chapters aim to assess the rocket leaves based on the
estimation of quality aspects, considering different aspects: (i) the variability due to the
different agricultural practices, (ii) the senescence of packed and unpacked products, and (iii)
development and exploitation of the advantages of new models simpler than the machine
learning used in the previous experiments. The research work of this doctoral thesis was carried
out by the University of Foggia, the Institute of Science of Food Production (ISPA) and the
Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing
(STIIMA) of National Research Council (CNR). It was conducted within the Project
SUS&LOW (Sustaining Low-impact Practices in Horticulture through Non-destructive
Approach to Provide More Information on Fresh Produce History & Quality), funded by MUR-
PRIN 2017, and aimed at sustaining quality of production and of the environment using low
input agricultural practices and non-destructive quality evaluation
A CNN-ELM Classification Model for Automated Tomato Maturity Grading
Tomatoes are popular around the world due to their high nutritional value. Tomatoes are also one of the world’s most widely cultivated and profitable crops. The distribution and marketing of tomatoes depend highly on their quality. Estimating tomato ripeness is an essential step in determining shelf life and quality. With the abundant supply of tomatoes on the market, it is exceedingly difficult to estimate tomato ripeness using human graders. To address this issue and improve tomato quality inspection and sorting, automated tomato maturity classification models based on different features have been developed. However, current methods heavily rely on human-engineered or handcrafted features. Convolutional neural networks have emerged as the preferred technique for general object recognition problems because they can automatically detect and extract valuable features by directly working on input images. This paper proposes a CNN-ELM classification model for automated tomato maturity grading that combines CNNs’ automated feature learning capabilities with the efficiency of extreme learning machines to perform fast and accurate classification even with limited training data. The results showed that the proposed CNN-ELM model had a classification accuracy of 96.67% and an F1-score of 96.67% in identifying six maturity stages from the test data
Deep Learning for Fruit Grading: A State-of-the-Art Review
In the food industry, grading fruit quality is a critical responsibility. Throughout this process, fruits are sorted and categorized in by their quality. Fruit grading can be done using both machine learning and visual assessment. Visual inspection is subjective and can be influenced by human prejudice. Machine learning can produce more accurate and unbiased results. Deep learning-based methods can be used to evaluate the fruit quality by teaching a neural network to recognize various quality parameters like size, color, and defects. Deep learning methodologies for evaluating fruit quality offer further benefits. They are neutral and accurate, and they can manage enormous amounts of data. They can also save labor expenses and improve the efficiency of the grading process. Deep learning methods are useful for evaluating fruit quality, but they have several drawbacks. These include an intricate neural network, overfitting, and a lack of high-quality training data. Addressing these issues is crucial for the success of deep learning in fruit quality evaluation. In this paper, various significant deep-learning methods for evaluating fruit quality are described. The methods' advantages and disadvantages are also discussed. The study gives the researcher pointers on how to improve current strategies or create fresh ones to improve performance in terms of training effectiveness, accuracy, etc
Internal quality assessment of tomato fruits using image color analysis
Nondestructive optical methods based on image analysis have been used for determining quality of tomato fruit. It is rapid and requires less sample preparation. A samples of fresh tomatoes were picked at different maturity stages, and determining chromaticity values (L*,a*,b*,a*/b*,h˚and ΔE) by image analysis and colorimeter. Total soluble solids (TSS), were measured by refractometer, lycopene extracting and expressed as mg/kg fresh tomato (FW). Results indicated that, during ripening both L*, b*, h˚, and ΔE tendency to decline, opposite tendency was determined with a*, a*/b* ratio, TSS and lycopene content. Chromaticity values have an important impact in internal quality parameters. Where, avg. of TSS, entire class and lycopene content had a positive linear correlation with a*/b* ratio. Contrary correlation was determined between avg. of TSS, entire class and both h˚ and ΔE. Meanwhile, h˚ and ΔE, had a negative logarithmic correlation with lycopene content. On the other hand, there were positive correlation between chromaticity values performed by image analysis technology and colorimeter. Where, on determining avg. of TSS, entire class, and lycopene content, correlations were linear with a*/b* ratio, and logarithmic with ΔE. Meanwhile, h˚ had alogarithmic correlation on determining avg. of TSS, entire class, and exponential correlation on determining lycopene content
Effectiveness of Deep Feature Extraction Algorithm in Determining the Maturity of Fruits: A Review
Intelligent farming technology helps farmers overcome tough obstacles in the farming process, such as increased sup-plier costs, a lack of labour, customer satisfaction, and more. Artificial Intelligence (AI) is a remarkable technology in smart farming because it deeply understands the issue and can help farmers make decisions. This article's main objective is to identify and examine the concepts and techniques of Convolutional Neural Networks (CNN) technology that could aid in classifying the ripeness stages of fruit in intelligent farming. This paper systematically reviews 18 previous works for classifying the ripeness stages of fruit. This review outlines the most commonly used algorithms, activation functions, optimisation functions, and platforms for algorithm implementation. In addition, found that not all algorithms are suitable for even near-equivalent processes. Therefore, this study suggests the intensity of the CNN algorithms concerning various metrics to find the suitability for the operations/applications. Finally, this paper offers some future research directions in the ripeness classification of fruits
Sensors for product characterization and quality of specialty crops—A review
This review covers developments in non-invasive techniques for quality analysis and inspection of specialty
crops, mainly fresh fruits and vegetables, over the past decade up to the year 2010. Presented and
discussed in this review are advanced sensing technologies including computer vision, spectroscopy,
X-rays, magnetic resonance, mechanical contact, chemical sensing, wireless sensor networks and radiofrequency
identification sensors. The current status of different sensing systems is described in the
context of commercial application. The review also discusses future research needs and potentials of
these sensing technologies. Emphases are placed on those technologies that have been proven effective
or have shown great potential for agro-food applications. Despite significant progress in the development
of non-invasive techniques for quality assessment of fruits and vegetables, the pace for adoption of these
technologies by the specialty crop industry has been slow
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