Defect Detection on Andean Potatoes using Deep Learning and Adaptive Learning

Casano, Celso De La Cruz; Sanchez, Miguel Catano; Rojas, Freddy J.; Vicente-Ramos, Wagner

doi:10.1109/eircon51178.2020.9254023

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…The paper presents two methods of teaching a neural network and classifying potatoes in terms of defects: learning with a teacher whose training on a set consisting of 4211 27 × 27 pixels of healthy and unhealthy zones allowed for 87.73% accuracy of the prediction model, and adaptive learning by means of which, using 1795 data, the prediction accuracy was 88.2%. It was noticed that the adaptive network uses a smaller set of data needed to train the network than a classic neural network, and its accuracy is slightly higher [ 93 ].…”

Section: Application Of Artificial Intelligence Methodsmentioning

confidence: 99%

The Need for Machines for the Nondestructive Quality Assessment of Potatoes with the Use of Artificial Intelligence Methods and Imaging Techniques

Danielak

Przybył

Koszela

2023

Sensors

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This article describes chemical and physical parameters, including their role in the storage, trade, and processing of potatoes, as well as their nutritional properties and health benefits resulting from their consumption. An analysis of the share of losses occurring during the production process is presented. The methods and applications used in recent years to estimate the physical and chemical parameters of potatoes during their storage and processing, which determine the quality of potatoes, are presented. The potential of the technologies used to classify the quality of potatoes, mechanical and ultrasonic, and image processing and analysis using vision systems, as well as their use in applications with artificial intelligence, are discussed.

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Section: Application Of Artificial Intelligence Methodsmentioning

confidence: 99%

The Need for Machines for the Nondestructive Quality Assessment of Potatoes with the Use of Artificial Intelligence Methods and Imaging Techniques

Danielak

Przybył

Koszela

2023

Sensors

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“…Most agricultural products must be sorted prior to their sale. Agricultural product grading [3], defect screening [4,5], crop health inspection [6], and pest monitoring [7] are labor-intensive tasks. Diversity in agricultural products increases the complexity of product defects.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Model for the Inspection of Coffee Bean Defects

Chang

Huang

2021

Applied Sciences

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The detection of coffee bean defects is the most crucial step prior to bean roasting. Existing defect detection methods used in the specialty coffee bean industry entail manual screening and sorting, require substantial human resources, and are not standardized. To solve these problems, this study developed a deep learning algorithm to detect defects in coffee beans. The results reveal that when the pooling layer was used to enhance features and reduce neural dimensionality, some of the coffee been features were lost or misclassified. Therefore, a novel dimensionality reduction method was adopted to increase the ability of feature extraction. The developed model also overcame the drawbacks of padding causing blurred image boundaries and the dead neurons causing impeding feature propagation. Images of eight types of coffee beans were used to train and test the proposed detection model. The proposed method was verified to reduce the bias when classifying defects in coffee beans. The detection accuracy rate of the proposed model was 95.2%. When the model was only used to detect the presence of defects, the accuracy rate increased to 100%. Thus, the proposed model is highly accurate in coffee bean defect detection in the classification of eight types of coffee beans.

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Performance Evaluation of the Infected Rice Leaves Using RCNN

Jayanthi,

Vanarasan,

et al. 2023

2023 International Conference on System, Computation, Automation and Networking (ICSCAN)

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Defect Detection on Andean Potatoes using Deep Learning and Adaptive Learning

Cited by 3 publications

References 9 publications

The Need for Machines for the Nondestructive Quality Assessment of Potatoes with the Use of Artificial Intelligence Methods and Imaging Techniques

The Need for Machines for the Nondestructive Quality Assessment of Potatoes with the Use of Artificial Intelligence Methods and Imaging Techniques

Deep Learning Model for the Inspection of Coffee Bean Defects

Performance Evaluation of the Infected Rice Leaves Using RCNN

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