Deep Learning based Fruit Freshness Classification and Detection with CMOS Image sensors and Edge processors

Ananthanarayana, Tejaswini; Ptucha, Raymond; Kelly, Seán

doi:10.2352/issn.2470-1173.2020.12.fais-172

Cited by 12 publications

(7 citation statements)

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“…Many attempts have been made for fruit recognition and classification in robot harvesting and farming using the deep learning approach [ 16 , 17 , 18 ]. A previous study [ 19 ] proposed an improved MobileNetv2 with ImageNet weights and fine-tuning by freezing the first 130 layers of MobileNetV2 and training the remaining 25 layers for fruit classification. They obtained real-time performance using a 13MP AR1335 camera connected to an NVidia Jetson Xavier and achieved 97% accuracy in the fruit classification of six classes: fresh/rotten apples, fresh/rotten bananas, and fresh/rotten oranges.…”

Section: Related Workmentioning

confidence: 99%

“…However, in the majority of the studies [ 18 , 19 , 20 , 21 , 22 , 23 , 24 ], the dataset consisted of a single fruit species under identical illumination conditions, rendering the conclusions less convincing. A further drawback of the existing datasets is that the vast majority of them contain only a small number of fruit types and no vegetable varieties.…”

Section: Related Workmentioning

confidence: 99%

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Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning

Mukhiddinov

Muminov

2022

Sensors

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Classification of fruit and vegetable freshness plays an essential role in the food industry. Freshness is a fundamental measure of fruit and vegetable quality that directly affects the physical health and purchasing motivation of consumers. In addition, it is a significant determinant of market price; thus, it is imperative to study the freshness of fruits and vegetables. Owing to similarities in color, texture, and external environmental changes, such as shadows, lighting, and complex backgrounds, the automatic recognition and classification of fruits and vegetables using machine vision is challenging. This study presents a deep-learning system for multiclass fruit and vegetable categorization based on an improved YOLOv4 model that first recognizes the object type in an image before classifying it into one of two categories: fresh or rotten. The proposed system involves the development of an optimized YOLOv4 model, creating an image dataset of fruits and vegetables, data argumentation, and performance evaluation. Furthermore, the backbone of the proposed model was enhanced using the Mish activation function for more precise and rapid detection. Compared with the previous YOLO series, a complete experimental evaluation of the proposed method can obtain a higher average precision than the original YOLOv4 and YOLOv3 with 50.4%, 49.3%, and 41.7%, respectively. The proposed system has outstanding prospects for the construction of an autonomous and real-time fruit and vegetable classification system for the food industry and marketplaces and can also help visually impaired people to choose fresh food and avoid food poisoning.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning

Mukhiddinov

Muminov

2022

Sensors

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“…A multi-class classifier based on VGG-16 and Inception-V3 was built by Ashraf et al (2019) for detecting fresh and rotten fruits. Researchers also practiced the advantages of CNNs in classifying the freshness of apples, bananas, and oranges ( Ananthanarayana et al, 2020 ).…”

Section: Convolutional Neural Network-based Fresh Fruit Detectionmentioning

confidence: 99%

Application of Convolutional Neural Network-Based Detection Methods in Fresh Fruit Production: A Comprehensive Review

et al. 2022

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As one of the representative algorithms of deep learning, a convolutional neural network (CNN) with the advantage of local perception and parameter sharing has been rapidly developed. CNN-based detection technology has been widely used in computer vision, natural language processing, and other fields. Fresh fruit production is an important socioeconomic activity, where CNN-based deep learning detection technology has been successfully applied to its important links. To the best of our knowledge, this review is the first on the whole production process of fresh fruit. We first introduced the network architecture and implementation principle of CNN and described the training process of a CNN-based deep learning model in detail. A large number of articles were investigated, which have made breakthroughs in response to challenges using CNN-based deep learning detection technology in important links of fresh fruit production including fruit flower detection, fruit detection, fruit harvesting, and fruit grading. Object detection based on CNN deep learning was elaborated from data acquisition to model training, and different detection methods based on CNN deep learning were compared in each link of the fresh fruit production. The investigation results of this review show that improved CNN deep learning models can give full play to detection potential by combining with the characteristics of each link of fruit production. The investigation results also imply that CNN-based detection may penetrate the challenges created by environmental issues, new area exploration, and multiple task execution of fresh fruit production in the future.

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“…Deep features extracted using CNN can be effective in detecting and analyzing the freshness of different varieties of food items [33]. A fruit classification approach is presented in [34], where images obtained from the CMOS sensors and Kaggle fruit360 dataset [30] are used to train a CNN model to identify six categories of fruits including fresh and rotten apples, bananas, and oranges. Moreover, augmentations techniques of scaling, rotation, translation, Gaussian noise addition, brightness variation have also been applied to the obtained images [34].…”

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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Deep Learning based Fruit Freshness Classification and Detection with CMOS Image sensors and Edge processors

Abstract: Fast track article for IS&T International Symposium on Electronic Imaging 2020: Food and Agricultural Imaging Systems proceedings.

Cited by 12 publications

References 0 publications

Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning

Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning

Application of Convolutional Neural Network-Based Detection Methods in Fresh Fruit Production: A Comprehensive Review

Fruits and Vegetables Freshness Categorization Using Deep Learning

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