A Texture-Based Analysis and Classification of Fruits Using Digital and Thermal Images

Bhole, Varsha; Kumar, Arun; Bhatnagar, Divya

doi:10.1007/978-981-15-0630-7_33

Cited by 11 publications

(8 citation statements)

References 9 publications

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“…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%

“…Every day, we observed 41 mangoes and photographed RGB as well as thermal images of the same. The videos and images have been axiomatically captured by regular smartphone camera and Seek Thermal camera from 360° as proposed by [7]. The captured RGB and thermal images have a resolution in pixels of 2322 x 4128 and 720 x 1280 respectively.…”

Section: Sample Collection and Preparationmentioning

confidence: 99%

“…But in recent years, varieties of non-destructive techniques like computer vision, x-ray imaging, hyperspectral imaging, visible imaging, thermal imaging, etc. [7][8][9] have come up in the agricultural segment where utilization of thermal imaging is at the budding stage. So in this research, we introduce the deep thermal imaging technique to determine the shelf life of fruit, specifically mango fruit.…”

Section: Introductionmentioning

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: Related Workmentioning

confidence: 99%

Section: Sample Collection and Preparationmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Acquiring an image is a crucial step for obtaining high-quality images and developing an automatic grading system for fruits. The images have been acquired automatically by the system as proposed by Bhole et al [5] and saved on a computer. Every day, we have taken RGB and Thermal images of almost 41 mangoes.…”

Section: Image Acquisitionmentioning

confidence: 99%

“…In the aforesaid grading system variety of color feature representation methods have been used as external factor of the fruit. Color representation comprises RGB [34], color histogram [12], CIE Lab [27], thresholding [32], surface texture [5,15], statistical analysis [3,4].…”

Section: Introductionmentioning

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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Designing AI-Based Non-invasive Method for Automatic Detection of Bovine Mastitis

Lakshitha,

Sajja

2024

Communications in Computer and Information Science

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A Texture-Based Analysis and Classification of Fruits Using Digital and Thermal Images

Cited by 11 publications

References 9 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

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

Designing AI-Based Non-invasive Method for Automatic Detection of Bovine Mastitis

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