Banana ripeness stage identification: a deep learning approach

Saranya, N.; Srinivasan, K.; Kumar, S. K. Pravin

doi:10.1007/s12652-021-03267-w

Cited by 51 publications

(24 citation statements)

References 15 publications

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“…The output tensor (12,12,28) is fed through the flatten layer which maps the input matrix into a 1-D vector of 18432 values. This vector is passed through two fully connected layers each of them consisting of 128 neurons and finally, we get 13 probabilistic values and thus the classification is performed.…”

Section: Proposed Depthwise Cnn Modelmentioning

confidence: 99%

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A classification model based on depthwise separable convolutional neural network to identify rice plant diseases

Prottasha

Reza

2022

IJECE

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<p><span>Every year a number of rice diseases cause major damage to crop around the world. Early and accurate prediction of various rice plant diseases has been a major challenge for farmers and researchers. Recent developments in the convolutional neural networks (CNNs) have made image processing techniques more convenient and precise. Motivated from that in this research, a depthwise separable convolutional neural network based classification model has been proposed for identifying 12 types of rice plant diseases. Also, 8 different state-of-the-art convolution neural network model has been fine-tuned specifically for identifying the rice plant diseases and their performance has been evaluated. The proposed model performs considerably well in contrast to existing state-of-the-art CNN architectures. The experimental analysis indicates that the proposed model can correctly diagnose rice plant diseases with a validation and testing accuracy of 96.5% and 95.3% respectively while having a substantially smaller model size.</span></p>

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Section: Proposed Depthwise Cnn Modelmentioning

confidence: 99%

“…The statistical analysis shows that using Adam optimizer their proposed model achieved a validation accuracy of 95.4%. Similar CNN model has been proposed for mango [10], wheat [11], banana [12], apple [13], and peach [14] classification.…”

mentioning

confidence: 99%

A classification model based on depthwise separable convolutional neural network to identify rice plant diseases

Prottasha

Reza

2022

IJECE

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“…In [84], 4 distinct banana ripeness stages were categorized utilizing the proposed CNN architecture and also compared with the other existing CNN models employing transfer learning. To achieve better classification results using the CNN model, a large count of training images was required.…”

Section: ) Shelf-life and Ripeness Level Detectionmentioning

confidence: 99%

“…Spectral reflectance of banana was used as performance visualization [65]. [64]- [66], [73], [75], [92] SSE (Sum of Squares of Errors) [64], [65] RMSE (Root Mean Squares of Errors) [64], [66] MEAN, Standard Deviation [7], [73], [75] Chi-Square, Information Gain, Gain Ratio [58] SSC (Soluble Sugar Content) [4] CA (Classification Accuracy) [41], [45]- [47], [53], [83]- [85], [87] Precision [45], [53], [57], [84], [87] Recall [57], [84], [87] F-Score ROC [46], [53], [57], [84], [87] [44], [45] Success Rate, Average error [9] RMSEC (RMSE for Calibration) [73] Sensitivity, Precision, Specificity, Accuracy, FPR (false positive rate) [10], [42], [45], [47] RMSECV ( RMSE for Cross Validation) [73] Classification Error [87] Hyperspectral data analysis, optimal wavelength selection, and multiple regression models were used to evaluate the banana fruit quality and their maturity stages. Even if visualization is an ...…”

Section: ) Implementation Of DL Models Using Visualization Techniquesmentioning

confidence: 99%

A Systematic Literature Review of Machine Learning Techniques Deployed in Agriculture: A Case Study of Banana Crop

et al. 2022

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for a project on "Application of IoT in Agriculture Sector" through the ICPS division under grant ID DST-319."ABSTRACT Agricultural productivity is the asset on which the world's economy thoroughly relies. This is one of the major causes that disease identification in fruits and plants occupies a salient role in farming space, as having disease disorders in them is obvious. There is a need to carry genuine supervision to avoid crucial consequences in vegetation; otherwise, corresponding vegetation standards, quantity, and productiveness gets affected. At present, a recognition system is required in the food handling industries to uplift the effectiveness of productivity to cope with demand in the community. The study has been carried out to perform a systematic literature review of research papers that deployed machine learning (ML) techniques in agriculture, applicable to the banana plant and fruit production. Thus; it could help upcoming researchers in their endeavors to identify the level and kind of research done so far. The authors investigated the problems related to banana crops such as disease classification, chilling injuries detection, ripeness, moisture content, etc. Moreover, the authors have also reviewed the deployed frameworks based on ML, sources of data collection, and the comprehensive results achieved for each study. Furthermore, ML architectures/techniques were evaluated using a range of performance measures. It has been observed that some studies used the PlantVillage dataset, a few have used Godliver and Scotnelson dataset, and the rest were based on either real-field image acquisition or on limited private datasets. Hence, more datasets are needed to be acquired to enhance the disease identification process and to handle the other kind of problems (e.g. chilling injuries detection, ripeness, etc.) present in the crops. Furthermore, the authors have also carried out a comparison of popular ML techniques like support vector machines, convolutional neural networks, regression, etc. to make differences in their performance. In this study, several research gaps are addressed, allowing for increased transparency in identifying different diseases even before symptoms arise and also for monitoring the abovementioned problems related to crops.

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“…the grape images collected in the controlled environment to simulate the fruit classification in an automatic fruit sorting system or in a laboratory. In general, the image acquisition and lighting equipment of the fruit sorting system or laboratory are in fixed positions, so the obtained images are less interfered by the background and lighting [75][76][77][78][79]. In this domain, we captured images of different varieties of grapes with a whiteboard background using a stationary camera and light source.…”

Section: • Domain-controlled Environment (Domain-ce) Includesmentioning

confidence: 99%

A cross-domain fruit classification method based on lightweight attention networks and unsupervised domain adaptation

Wang

Zhang

Yan

et al. 2022

Complex Intell. Syst.

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Image-based fruit classification offers many useful applications in industrial production and daily life, such as self-checkout in the supermarket, automatic fruit sorting and dietary guidance. However, fruit classification task will have different data distributions due to different application scenarios. One feasible solution to solve this problem is to use domain adaptation that adapts knowledge from the original training data (source domain) to the new testing data (target domain). In this paper, we propose a novel deep learning-based unsupervised domain adaptation method for cross-domain fruit classification. A hybrid attention module is proposed and added to MobileNet V3 to construct the HAM-MobileNet that can suppress the impact of complex backgrounds and extract more discriminative features. A hybrid loss function combining subdomain alignment and implicit distribution metrics is used to reduce domain discrepancy during model training and improve model classification performance. Two fruit classification datasets covering several domains are established to simulate common industrial and daily life application scenarios. We validate the proposed method on our constructed grape classification dataset and general fruit classification dataset. The experimental results show that the proposed method achieves an average accuracy of 95.0% and 93.2% on the two datasets, respectively. The classification model after domain adaptation can well overcome the domain discrepancy brought by different fruit classification scenarios. Meanwhile, the proposed datasets and method can serve as a benchmark for future cross-domain fruit classification research.

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Banana ripeness stage identification: a deep learning approach

Cited by 51 publications

References 15 publications

A classification model based on depthwise separable convolutional neural network to identify rice plant diseases

A classification model based on depthwise separable convolutional neural network to identify rice plant diseases

A Systematic Literature Review of Machine Learning Techniques Deployed in Agriculture: A Case Study of Banana Crop

A cross-domain fruit classification method based on lightweight attention networks and unsupervised domain adaptation

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