Analysis of Convolutional Neural Network Using Pre-Trained Squeezenet Model for Classification of Thermal Fruit Images

Bhole, Varsha; Kumar, Arun

doi:10.1201/9781003052098-80

Cited by 14 publications

(8 citation statements)

References 13 publications

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“…The purpose of current study is to predict shelf-life of mangoes using state-of-the-art non-destructive approach, namely thermal imaging with three pre-trained models via transfer learning techniques. It has been ascertained that transfer learning requires small image datasets for training to gain high accuracy [30,35]. Within this research, data collection has been pursued, as is explicated in Sect.…”

Section: Methodsmentioning

confidence: 99%

“…However, limited work has been implemented using thermal imaging on very few fruits like apple [24][25][26][27], citrus [28], peach [29], mango [30,31,32], blueberries [33], dates [34]. Recently, some researchers have proposed the applications in the agricultural domain related to computer vision with thermal imaging technique like defect detection and classification of fruits [7,8,33,35], grading of fruits through maturity [30,31,34] and identifying the shell life [32].…”

Section: Related Workmentioning

confidence: 99%

“…In the artificial intelligence research field, deep learning (DL) with CNN comes as a handy tool [30,35]. But, deep learning study and training a CNN from scratch requires good quality as well as large amount of data.…”

Section: Related Workmentioning

confidence: 99%

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A Transfer Learning-based Approach to Predict the Shelf life of Fruit

Bhole

Kumar

2021

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Shelf-life prediction for fruits based on the visual inspection and with RGB imaging through external features becomes more pervasive in agriculture and food business. In the proposed architecture, to enhance the accuracy with low computational costs we focus on two challenging tasks of shelf life (remaining useful life) prediction: 1) detecting the intrinsic features like internal defects, bruises, texture, and color of the fruits; and 2) classification of fruits according to their remaining useful life. To accomplish these tasks, we use the thermal imaging technique as a baseline which is used as non-destructive approach to find the intrinsic values of fruits in terms of temperature parameter. Further to improve the classification tasks, we combine it with a transfer learning approach to forecast the shelf life of fruits. For this study, we have chosen „Kesar? (Mangifera Indica Linn cv. Kesar) mangoes and for the purpose of classification, our designed dataset images are categorized into 19 classes viz. RUL-1 (Remaining Useful Life-1) to RUL-18 (Remaining Useful Life-18) and No-Life as after harvesting, the storage span of „Kesar? is near about 19 days. A comparative analysis using SqueezeNet, ShuffleNet, and MobileNetv2 (which are prominent CNN based lightweight models) has been performed in this study. The empirical results show a highest achievable accuracy of 98.15±0.44% with an almost a double speedup in training the entire process by using thermal images.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A Transfer Learning-based Approach to Predict the Shelf life of Fruit

Bhole

Kumar

2021

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“…The model was created using deep learning to classify the aflatoxin contamination level in cocoa beans. Four different pre-trained CNN types were used in the process: SqueezeNet [44], GoogLeNet [45], ResNet50 [46], and AlexNet [47]. The CNN architecture for the classification is presented in the following Fig.…”

Section: ) Deep Learning Modelingmentioning

confidence: 99%

The Classification of Aflatoxin Contamination Level in Cocoa Beans using Fluorescence Imaging and Deep learning

Sadimantara,

Argo,

Sucipto

et al. 2024

Journal of Robotics and Control

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Aflatoxin contamination in cacao is a significant problem in terms of trade losses and health effects. This calls for the need for a non-invasive, precise, and effective detection strategy. This research contribution is to determine the best deep-learning model to classify the aflatoxin contamination level in cocoa beans based on fluorescence images and deep learning to improve performance in the classification. The process involved inoculating and incubating Aspergillus flavus (6mL/100g) to obtain aflatoxin-contaminated cocoa beans for 7 days during the incubation period. Liquid Mass Chromatography (LCMS) was used to quantify the aflatoxin in order to categorize the images into different levels including “free of aflatoxin”, “contaminated below the limit”, and “contaminated above the limit”. 300 images were acquired through a mini studio equipped with UV lamps. The aflatoxin level was classified using several pre-trained CNN approaches which has high accuracy such as GoogLeNet, SqueezeNet, AlexNet, and ResNet50. The sensitivity analysis showed that the highest classification accuracy was found in the GoogLeNet model with optimizer: Adam and learning rate: 0.0001 by 96.42%. The model was tested using a testing dataset and obtain accuracy of 96% based on the confusion matrix. The findings indicate that combining CNN with fluorescence images improved the ability to classify the amount of aflatoxin contamination in cacao beans. This method has the potential to be more accurate and economical than the current approach, which could be adapted to reduce aflatoxin's negative effects on food safety and cacao trade losses.

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“…In the current study, we propose CNN based SqueezeNet model to classify RGB and thermal fruit images for prediction of size, maturity, and grade of 'Kesar' mango fruits. The SqueezeNet incorporates total 68 layers; which is 18 layers deep coupled with 72 connections [6]. The proposed fine-tuned CNN based SqueezeNet framework is demonstrated in Figure 3 which initiates with convolution layer (conv1), proceed with eight fire modules i.e.…”

Section: Squeezenet Modelmentioning

confidence: 99%

Mango Quality Grading using Deep Learning Technique: Perspectives from Agriculture and Food Industry

Bhole

Kumar

2020

Proceedings of the 21st Annual Conference on Information Technology Education

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India is an agrarian country, agriculture business is major source of income. India holds the first rank in mango (Mangifera Indica Linn) production worldwide. The precise grading of the fruit acts extensively in agricultural sector for the commercial development of India. Prior to bring the agricultural products to the market, it is essential to classify and grade them automatically without manual intervention. In this research study, we have designed and implemented deep learningcentered non-destructive mango sorting and grading system. The designed quality assessment scheme comprises of two phases: developing hardware and software. The hardware is built to photograph the RGB and thermal images of mango fruits from all the directions (360°) automatically. From these images, designed software classifies mangoes into three grades according to quality viz. Extra class, Class-I, and Class-II. Mango grading has been done by using parameters such as defects, shape, size and maturity. In the present work, transfer learning based pretrained SqueezeNet model has been employed to assess grading of mangoes. The test result reveals that classification accuracy of proposed system is 93.33% and 92.27% with the training time of 30.03 and 7.38 minutes for RGB and thermal images respectively and shows four times speed up through thermal imaging.

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Analysis of Convolutional Neural Network Using Pre-Trained Squeezenet Model for Classification of Thermal Fruit Images

Cited by 14 publications

References 13 publications

A Transfer Learning-based Approach to Predict the Shelf life of Fruit

A Transfer Learning-based Approach to Predict the Shelf life of Fruit

The Classification of Aflatoxin Contamination Level in Cocoa Beans using Fluorescence Imaging and Deep learning

Mango Quality Grading using Deep Learning Technique: Perspectives from Agriculture and Food Industry

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