Maturity status classification of papaya fruits based on machine learning and transfer learning approach

Behera, Santi Kumari; Rath, Amiya Kumar; Sethy, Prabira Kumar

doi:10.1016/j.inpa.2020.05.003

Cited by 114 publications

(57 citation statements)

References 21 publications

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“…The preliminary result in this study adheres to the findings of [ 25 ] where the CNN with early fused RGB and NIR obtained a higher F1-score than the one using the NIR images alone, and even with that of [ 25 , 26 ] in which the deep learning methods using only RGB images obtained better results than the early fused multimodal technique. However, looking at the validation loss and accuracy, there is still room for improvement, and it is very interesting to note that the results of the CNN with only RGB images and only HS data cubes improved when the RGB images and HS data cubes were concatenated.…”

Section: Discussionsupporting

confidence: 88%

“…Among the multimodal deep learning models used in this study, MD-VGG16, MD-AlexNet, and MD-VGG19 were the top-performing models. These findings correspond to the studies of [ 26 ], which used only RGB images of three maturity stages of papaya and transfer learning on the deep learning models, and to [ 49 ], which utilized the first three principal components of hyperspectral images of strawberries and a pretrained AlexNet. From reference [ 26 , 49 ], VGG19 and AlexNet displayed superior performance, respectively.…”

Section: Discussionsupporting

confidence: 87%

“…Recently, the role of deep learning in non-destructive methods of agriculture-related tasks is becoming increasingly important especially with the rise of smart and precision agriculture. Deep learning is highly explored today for its potential in various agricultural applications such as fruit detection and classification [ 25 ], fruit maturity estimation [ 26 ], plant disease detection [ 27 , 28 , 29 ], and early bruise or damage detection [ 15 , 30 ]. This approach can provide higher and more precise evaluation results compared to traditional and manual techniques.…”

Section: Introductionmentioning

confidence: 99%

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Multimodal Deep Learning and Visible-Light and Hyperspectral Imaging for Fruit Maturity Estimation

Garillos-Manliguez

Chiang

2021

Sensors

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Fruit maturity is a critical factor in the supply chain, consumer preference, and agriculture industry. Most classification methods on fruit maturity identify only two classes: ripe and unripe, but this paper estimates six maturity stages of papaya fruit. Deep learning architectures have gained respect and brought breakthroughs in unimodal processing. This paper suggests a novel non-destructive and multimodal classification using deep convolutional neural networks that estimate fruit maturity by feature concatenation of data acquired from two imaging modes: visible-light and hyperspectral imaging systems. Morphological changes in the sample fruits can be easily measured with RGB images, while spectral signatures that provide high sensitivity and high correlation with the internal properties of fruits can be extracted from hyperspectral images with wavelength range in between 400 nm and 900 nm—factors that must be considered when building a model. This study further modified the architectures: AlexNet, VGG16, VGG19, ResNet50, ResNeXt50, MobileNet, and MobileNetV2 to utilize multimodal data cubes composed of RGB and hyperspectral data for sensitivity analyses. These multimodal variants can achieve up to 0.90 F1 scores and 1.45% top-2 error rate for the classification of six stages. Overall, taking advantage of multimodal input coupled with powerful deep convolutional neural network models can classify fruit maturity even at refined levels of six stages. This indicates that multimodal deep learning architectures and multimodal imaging have great potential for real-time in-field fruit maturity estimation that can help estimate optimal harvest time and other in-field industrial applications.

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Section: Discussionsupporting

confidence: 88%

Section: Discussionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

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Multimodal Deep Learning and Visible-Light and Hyperspectral Imaging for Fruit Maturity Estimation

Garillos-Manliguez

Chiang

2021

Sensors

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“…We’re going to process video data more efficiently. At present, computer vision technology has been widely used in agricultural research [ 30 , 31 , 32 , 33 ], such as crop pest detection [ 34 , 35 , 36 ] or pest activity detection [ 37 ], crop disease detection [ 38 ], identification of crop growth [ 39 , 40 ], crop yield prediction [ 41 ], and animal behavior detection [ 26 , 27 , 42 ]. The first four kinds of applications can get good results by processing and analyzing only a few clear images.…”

Section: Discussionmentioning

confidence: 99%

Application of Spatio-Temporal Context and Convolution Neural Network (CNN) in Grooming Behavior of Bactrocera minax (Diptera: Trypetidae) Detection and Statistics

Zhang

Zhan

et al. 2020

Insects

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Statistical analysis and research on insect grooming behavior can find more effective methods for pest control. Traditional manual insect grooming behavior statistical methods are time-consuming, labor-intensive, and error-prone. Based on computer vision technology, this paper uses spatio-temporal context to extract video features, uses self-built Convolution Neural Network (CNN) to train the detection model, and proposes a simple and effective Bactrocera minax grooming behavior detection method, which automatically detects the grooming behaviors of the flies and analysis results by a computer program. Applying the method training detection model proposed in this paper, the videos of 22 adult flies with a total of 1320 min of grooming behavior were detected and analyzed, and the total detection accuracy was over 95%, the standard error of the accuracy of the behavior detection of each adult flies was less than 3%, and the difference was less than 15% when compared with the results of manual observation. The experimental results show that the method in this paper greatly reduces the time of manual observation and at the same time ensures the accuracy of insect behavior detection and analysis, which proposes a new informatization analysis method for the behavior statistics of Bactrocera minax and also provides a new idea for related insect behavior identification research.

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“…Several other studies have been done to classify fruits other than dates. In 2020, Behera, S. K., A. K. Rath, et al [14] introduced two methods based on ML techniques to classify papaya fruit maturity stages. They used a very small dataset with 300 papaya fruit images, consisting of 100 images of each of the three maturity stages.…”

Section: Literature Reviewmentioning

confidence: 99%

IHDS: Intelligent Harvesting Decision System for Date Fruit Based on Maturity Stage Using Deep Learning and Computer Vision

et al. 2020

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Maturity status classification of papaya fruits based on machine learning and transfer learning approach

Cited by 114 publications

References 21 publications

Multimodal Deep Learning and Visible-Light and Hyperspectral Imaging for Fruit Maturity Estimation

Multimodal Deep Learning and Visible-Light and Hyperspectral Imaging for Fruit Maturity Estimation

Application of Spatio-Temporal Context and Convolution Neural Network (CNN) in Grooming Behavior of Bactrocera minax (Diptera: Trypetidae) Detection and Statistics

IHDS: Intelligent Harvesting Decision System for Date Fruit Based on Maturity Stage Using Deep Learning and Computer Vision

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