Fruit Classification Using Traditional Machine Learning and Deep Learning Approach

Saranya, N.; Srinivasan, K.; Kumar, S. K. Pravin; Rukkumani, V.; Ramya, R.

doi:10.1007/978-3-030-37218-7_10

Cited by 20 publications

(5 citation statements)

References 10 publications

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“…They incorporated a transfer learning technique [12], and the model acquired 99% accuracy. Saranya et al [13] undertook a comparative study in which they trained different machine learning and deep learning models on a public dataset. This dataset contains images of different fruits, such as apples, bananas, oranges, and pomegranates.…”

Section: Introductionmentioning

confidence: 99%

Fruit Image Classification Model Based on MobileNetV2 with Deep Transfer Learning Technique

Gulzar

2023

Sustainability

155

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Due to the rapid emergence and evolution of AI applications, the utilization of smart imaging devices has increased significantly. Researchers have started using deep learning models, such as CNN, for image classification. Unlike the traditional models, which require a lot of features to perform well, CNN does not require any handcrafted features to perform well. It uses numerous filters, which extract required features from images automatically for classification. One of the issues in the horticulture industry is fruit classification, which requires an expert with a lot of experience. To overcome this issue an automated system is required which can classify different types of fruits without the need for any human effort. In this study, a dataset of a total of 26,149 images of 40 different types of fruits was used for experimentation. The training and test set were randomly recreated and divided into the ratio of 3:1. The experiment introduces a customized head of five different layers into MobileNetV2 architecture. The classification layer of the MobileNetV2 model is replaced by the customized head, which produced the modified version of MobileNetV2 called TL-MobileNetV2. In addition, transfer learning is used to retain the pre-trained model. TL-MobileNetV2 achieves an accuracy of 99%, which is 3% higher than MobileNetV2, and the equal error rate of TL-MobileNetV2 is just 1%. Compared to AlexNet, VGG16, InceptionV3, and ResNet, the accuracy is better by 8, 11, 6, and 10%, respectively. Furthermore, the TL-MobileNetV2 model obtained 99% precision, 99% for recall, and a 99% F1-score. It can be concluded that transfer learning plays a big part in achieving better results, and the dropout technique helps to reduce the overfitting in transfer learning.

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Section: Introductionmentioning

confidence: 99%

Fruit Image Classification Model Based on MobileNetV2 with Deep Transfer Learning Technique

Gulzar

2023

Sustainability

155

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“…The fine K-NN with k=1 achieved the best accuracy reached to 96.3%. An automatic sorting and classification of different kinds of fruits using their images were proposed by (Saranya et al, 2019), four categories of fruits such as apple, banana, orange, and pomegranate acquired from Fruits-360 database were utilized to investigate the accuracy of the system, different extracted features such as mean of RGB color, size, height and width were used to build and test the model, in classification phase the K-NN and SVM were implemented as they report an accuracy of 93.8% and 100% respectively. A system for recognition strawberry ripeness was presented in (Anraeni, Indra, Adirahmadi, & Pomalingo, 2021) four different strawberry's classes like ripe, unripe, raw, not strawberry was utilized as a database, features were extracted from RGB image, such as RGB color component value, area, roundness, and centroid value for each channel in RGB, K-NN was implemented in the classification phase to recognize each class the accuracy of this system was reached to 85%.…”

Section: Related Workmentioning

confidence: 99%

A Recognition and Classification of Fruit Images Using Texture Feature Extraction and Machine Learning Algorithms

Behnam Israel,

Ismail Al-Sulaifanie,

Khorsheed Al-Sulaifanie

2024

ACAD J NAWROZ UNIV

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Fruits classification is demanded in some fields, such as industrial agriculture. Automatic fruit classification from their digital image plays a vital role in those fields. The classification encounters several challenges due to capturing fruits’ images from different viewing angle, rotation, and illumination pose. In this paper a framework for recognition and classification of fruits from their images have been proposed depending on texture features, the proposed system rely on three phases; firstly, pre-processing, as images need to be resized, filtered, color convert, and threshold in order to create a fruit mask which is used for fruit’s region of interest segmentation; followed by two methods for texture features extraction, first method utilize Local Binary Pattern (LBP), while the second method uses Principal Component Analysis (PCA) to generate features vector for each fruit image. Classification is the last phase; two supervised machine learning algorithms; K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) are utilized to identity and recognize the fruits images classes. Both methods are tested using 1200 fruits images, from 12 classes acquired from Fruits-360 database. The results show that combining LBP with K-NN, and SVM yields the best accuracy up to 100% and 89.44% respectively, while the accuracy of applying PCA with K-NN and SVM reached to 86.38 % and 85.83% respectively.

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“…If the occlusion area of leaves or branches exceeded 30%, the sample was considered occluded and labeled as type A. Unobstructed samples were labeled as type B. Typically, the picking points are distributed along the mid-line of the geometric center [41,42]. However, since a single lychee fruit weighs about 21.4-31.8 g, the weight can cause the fruit to easily lean to one side due to gravity [43][44][45].…”

Section: Image Acquisitionmentioning

confidence: 99%

Prototype Network for Predicting Occluded Picking Position Based on Lychee Phenotypic Features

Li,

Liao,

Wang

et al. 2023

Agronomy

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The automated harvesting of clustered fruits relies on fast and accurate visual perception. However, the obscured stem diameters via leaf occlusion lack any discernible texture patterns. Nevertheless, our human visual system can often judge the position of harvesting points. Inspired by this, the aim of this paper is to address this issue by leveraging the morphology and the distribution of fruit contour gradient directions. Firstly, this paper proposes the calculation of fruit normal vectors using edge computation and gradient direction distribution. The research results demonstrate a significant mathematical relationship between the contour edge gradient and its inclination angle, but the experiments show that the standard error projected onto the Y-axis is smaller, which is evidently more conducive to distinguishing the gradient distribution. Secondly, for the front view of occluded lychee clusters, a fully convolutional, feature prototype-based one-stage instance segmentation network is proposed, named the lychee picking point prediction network (LP3Net). This network can achieve high accuracy and real-time instance segmentation, as well as for occluded and overlapping fruits. Finally, the experimental results show that the LP3Net based on this study, along with lychee phenotypic features, achieves an average location accuracy reaching 82%, significantly improving the precision of harvesting point localization for lychee clusters.

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Fruit Classification Using Traditional Machine Learning and Deep Learning Approach

Cited by 20 publications

References 10 publications

Fruit Image Classification Model Based on MobileNetV2 with Deep Transfer Learning Technique

Fruit Image Classification Model Based on MobileNetV2 with Deep Transfer Learning Technique

A Recognition and Classification of Fruit Images Using Texture Feature Extraction and Machine Learning Algorithms

Prototype Network for Predicting Occluded Picking Position Based on Lychee Phenotypic Features

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