An efficient ANFIS based pre-harvest ripeness estimation technique for fruits

Sandeep, K.; Randhawa, Sukhchandan; Malhi, Avleen

doi:10.1007/s11042-021-10741-2

Cited by 8 publications

(3 citation statements)

References 22 publications

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“…Fruit skin color is often considered a prominent and highly utilized quality indicator that significantly affects consumer’s acceptance [ 30 ]. A previous study found that the varying contents of natural chlorophyll and pigments lead to the fruit color change during ripening [ 31 ].…”

Section: Resultsmentioning

confidence: 99%

Quality Assessment and Ripeness Prediction of Table Grapes Using Visible–Near-Infrared Spectroscopy

Ping

Yang

Zhou

et al. 2023

Foods

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Ripeness significantly affects the commercial values and sales of fruits. In order to monitor the change in grapes’ quality parameters during ripening, a rapid and nondestructive method of visible–near-infrared spectral (Vis-NIR) technology was utilized in this study. Firstly, the physicochemical properties of grapes in four different ripening stages were explored, with increasing color in redness/greenness (a*) and Chroma (C*) as well as soluble solids (SSC) content as ripening advanced, and decreasing values in color of lightness (L*), yellowness/blueness (b*), hue angle (h*), hardness, and total acid (TA) content. Based on these results, spectral prediction models for SSC and TA in grapes were established. Effective wavelengths were selected using the competitive adaptive weighting algorithm (CARS), and six common preprocessing methods were applied to pretreat the spectral data. Partial least squares regression (PLSR) was applied to establish models on the basis of effective wavelengths and full spectra. The models of SSC and TA with full spectral data using PLSR after 1st derivative preprocessing both obtained the best results. For SSC, these yielded optimum results when the coefficients of determination of PLSR models for the calibration set (RCal2) and the prediction set (RPre2) were 0.97 and 0.93, respectively; the root mean square error for the calibration set (RMSEC) and the prediction set (RMSEP) were 0.62% and 1.27%, respectively; and the RPD was 4.09. As for TA, the optimum values of RCal2, RPre2, RMSEC, RMSEP, and RPD were 0.97, 0.94, 0.88 g/L, 1.96 g/L, and 4.55, respectively. The results indicated that Vis-NIR spectroscopy is an effective tool for the rapid and non-destructive detection of SSC and TA in grapes.

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Section: Resultsmentioning

confidence: 99%

Quality Assessment and Ripeness Prediction of Table Grapes Using Visible–Near-Infrared Spectroscopy

Ping

Yang

Zhou

et al. 2023

Foods

View full text Add to dashboard Cite

show abstract

“…Support Vector Machine (SVM) is a commonly used classification machine learning algorithm. It maps input data to a higher-dimensional space in order to identify the optimal hyperplane that separates classes [13,14,15]. Utilizing SVMbased models for fruit classification has yielded notable results.…”

Section: Review Of Existing Models Used For Multivariate Classificati...mentioning

confidence: 99%

DQLRFMG: Design of an Augmented Fusion of Deep Q Learning with Logistic Regression and Deep Forests for Multivariate Classification and Grading of Fruits

Said,

Joshi

2023

IJRITCC

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Accurate categorization and grading of fruits are essential in numerous fields, including agriculture, food processing, and distribution. This paper addresses the need for an advanced model capable of classifying and grading fruits more effectively than existing methods. Traditional approaches are limited by their lower precision, accuracy, recall, area under the curve (AUC), and delay. In order to overcome these obstacles, the proposed model combines the capabilities of Deep Q Learning (DQL) for classification and Logistic Regression (LR) with Deep Forests for fruit grading process. Three distinct datasets were used to evaluate the model: the Kaggle - Fruits 360 Dataset, the FRont Experimental System for High throughput plant phenotyping Datasets, and ImageNet samples. In multiple respects, comparative analysis demonstrates that the proposed model outperforms existing methods. Specifically, it achieves a remarkable 4.9% improvement in precision, 5.5% improvement in accuracy, 4.5% improvement in recall, 3.9% improvement in AUC, and an 8.5% reduction in delay levels. Utilizing the strengths of both DQL and LR with Deep Forests, the proposed model achieves its superior performance. DQL, a technique for reinforcement learning, provides the ability to learn and make decisions based on the feedback from the environment. By combining DQL and LR, the classification accuracy is improved, allowing for the precise identification of fruit varieties including Mango, Apple. Papaya, etc. In addition, Deep Forests, a novel framework for ensemble learning, is utilized for fruit grading. Deep Forests utilizes decision trees to effectively capture complex patterns in the data, allowing for dependable and robust fruit grading. Experimental findings indicate that the combination of DQL and LR with Deep Forests yields remarkable performance improvements in fruit classification and grading tasks. Improved precision, accuracy, recall, AUC, and delay indicate the model's superiority over existing methods. This research contributes to the field of fruit classification and grading by developing a sophisticated model that can support a variety of applications in the agriculture, food processing, and distribution industries.

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“…Among the three models, Googlenet shows the best performance compared to the rest of the models. A fruit ripeness estimation technique first resizes the images to the same size and then perform segmentation to extract the image from the background [37]. In the next step, color features are extracted, and an adaptive neuro-fuzzy inference system is used for identification of different stages of fruit ripeness.…”

Section: Related Workmentioning

confidence: 99%

Fruits and Vegetables Freshness Categorization Using Deep Learning

Fahad¹,

Tahir²,

Rasheed³

et al. 2022

Computers, Materials &Amp; Continua

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The nutritional value of perishable food items, such as fruits and vegetables, depends on their freshness levels. The existing approaches solve a binary class problem by classifying a known fruit\vegetable class into fresh or rotten only. We propose an automated fruits and vegetables categorization approach that first recognizes the class of object in an image and then categorizes that fruit or vegetable into one of the three categories: purefresh, medium-fresh, and rotten. We gathered a dataset comprising of 60K images of 11 fruits and vegetables, each is further divided into three categories of freshness, using hand-held cameras. The recognition and categorization of fruits and vegetables are performed through two deep learning models: Visual Geometry Group (VGG-16) and You Only Look Once (YOLO), and their results are compared. VGG-16 classifies fruits and vegetables and categorizes their freshness, while YOLO also localizes them within the image. Furthermore, we have developed an android based application that takes the image of the fruit or vegetable as input and returns its class label and its freshness degree. A comprehensive experimental evaluation of proposed approach demonstrates that the proposed approach can achieve a high accuracy and F1score on gathered FruitVeg Freshness dataset. The dataset is publicly available for further evaluation by the research community.

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An efficient ANFIS based pre-harvest ripeness estimation technique for fruits

Cited by 8 publications

References 22 publications

Quality Assessment and Ripeness Prediction of Table Grapes Using Visible–Near-Infrared Spectroscopy

Quality Assessment and Ripeness Prediction of Table Grapes Using Visible–Near-Infrared Spectroscopy

DQLRFMG: Design of an Augmented Fusion of Deep Q Learning with Logistic Regression and Deep Forests for Multivariate Classification and Grading of Fruits

Fruits and Vegetables Freshness Categorization Using Deep Learning

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