Deep Learning for Automatic Quality Grading of Mangoes: Methods and Insights

Wu, Shih-Lun; Tung, Hsiao-Yen; Hsu, Yuan‐Yih

doi:10.1109/icmla51294.2020.00076

Cited by 25 publications

(17 citation statements)

References 26 publications

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“…The overall analysis explains the betterment of the implemented grading model with optimal features. It can be explained as: for the healthy–diseased category, the proposed model with optimal features has attained betterment in terms of accuracy (maximization measure), which is 55.88%, 10.42%, 11.05% and 24.45% superior to conventional CNN with all features and conventional CNN with features in [21], auto encoder + all features [40] and RNN + all features [41] respectively. The overall analysis is formulated regarding the other maximization measures as well and demonstrates the superiority of this work with better prediction.…”

Section: Resultsmentioning

confidence: 99%

“…Table 5 explains the performance analysis of implemented CNN against the conventional CNN model with all features and the relevant features in [21], auto encoder + all features [40] RNN + all features [41]. Moreover, healthy–diseased, ripe–unripe, and big–medium–very big are the three test cases under where the analysis is formulated.…”

Section: Resultsmentioning

confidence: 99%

“…Table 4 depicts the performance analysis of proposed CNN with proposed features regarding the type I measures. The analysis is made over conventional CNN with all features and the relevant features in [21], auto encoder + all features [40] and features RNN + all features [41]. Typically, three test case analyses are exploited for this comparison healthy‐diseased, ripe–unripe, and big–medium–very big.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Optimized deep learning model for mango grading: Hybridizing lion plus firefly algorithm

Tripathi

Maktedar

2021

IET Image Processing

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This paper intends to present an automated mango grading system under four stages (1) pre-processing, (2) feature extraction, (3) optimal feature selection and (4) classification. Initially, the input image is subjected to the pre-processing phase, where the reading, sizing, noise removal and segmentation process happens. Subsequently, the features are extracted from the pre-processed image. To make the system more effective, from the extracted features, the optimal features are selected using a new hybrid optimization algorithm termed the lion assisted firefly algorithm (LA-FF), which is the combination of LA and FF, respectively. Then, the optimal features are given for the classification process, where the optimized deep convolutional neural network (CNN) is deployed. As a major contribution, the configuration of CNN is fine-tuned via selecting the optimal count of convolutional layers. This obviously enhances the classification accuracy in grading system. For finetuning the convolutional layers in the deep CNN, the LA-FF algorithm is used so that the classifier is optimized. The grading is evaluated on the basis of healthydiseased, ripeunripe and bigmediumvery big cases with respect to type I and type II measures and the performance of the proposed grading model is compared over the other state-of-the-art models. 1 INTRODUCTION Mango (Mangifera indica L.) belongs to the family Anacardiaceae. These are cultivated commercially and extensively in India, tropical Australia, Thailand, Philippines, Hawaii, the lowlands of SouthEast Africa, and in the lowlands of South and Central America. When exporting the mangoes over other countries, the grading [1-4] is essential for quality consideration. Conventionally, the fruit grading is handled by those trained inspectors and this is considered to be labour-intensive, time-consuming, and inefficient. The majority of the countries consider the size feature for mango grading. Still, it remains to be a complex task due to inappropriate grading. Therefore, the automatic grading process [5-7] is very necessary and helpful. On grading the mangoes, the features such as shape, size, firmness, maturity, and visual defects have to be essentially considered. Due to the advancement of technologies, the grading can be effectively made using image processing and computer vision systems [3-8]. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Optimized deep learning model for mango grading: Hybridizing lion plus firefly algorithm

Tripathi

Maktedar

2021

IET Image Processing

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“…Cherries are prone to abnormal shapes during growth, so some researchers used a modified AlexNet model to classify cherries according to growth shapes ( Momeny et al, 2020 ). Wu S. et al (2020) combined and investigated several deep learning methods for detecting visible mango defects and found that VGG-16 has a dominant position by combining and investigating several DL methods. De Luna et al (2019) also demonstrated that the VGG-16 model has better performance in tomato defect inspection.…”

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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“…It contains significant amounts of vitamin A and vitamin C [2]. It is called the "King of fruits" because of its alluring aroma, flavorful pulp and high nutritional value that attract many mango lovers from around the world [1] [3]. In 2021, mango was the third most traded tropical fruit after pineapple and avocado in terms of quantities exported.…”

Section: Introductionmentioning

confidence: 99%

Mango Diseases Classification Solutions Using Machine Learning or Deep Learning: A Review

Faye¹,

Diop²,

Dione³

2022

JCC

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The mango crop suffers from several diseases that reduce both the production and the quality of the mangoes. This also reduces its price on the international market. Diagnosis of these diseases remains difficult in many countries due to poverty and lack of infrastructure. Plant pathologists use several techniques to identify these diseases. But these techniques are time consuming and relatively expensive for mango growers and the solutions proposed are often not very accurate and sometimes biased. In the last decade, researchers have proposed several solutions in the field of automatic diagnosis of mango diseases. Such solutions are based on Machine Learning (ML) and Deep Learning (DL) algorithms. In this paper, we divided these solutions into two groups: solutions based on classical ML algorithms, and those based on DL. In recent years, DL, especially Convolutional Neural Network (CNN) has become the most widely used method by researchers because of its impressive performance. The critical analysis of the proposed solutions has allowed us to identify their limits and potential challenges in mango disease automatic diagnosis.

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Deep Learning for Automatic Quality Grading of Mangoes: Methods and Insights

Cited by 25 publications

References 26 publications

Optimized deep learning model for mango grading: Hybridizing lion plus firefly algorithm

Optimized deep learning model for mango grading: Hybridizing lion plus firefly algorithm

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

Mango Diseases Classification Solutions Using Machine Learning or Deep Learning: A Review

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