PhosopNet: An improved grain localization and classification by image augmentation

Mookdarsanit, Pakpoom; Mookdarsanit, Lawankorn

doi:10.12928/telkomnika.v19i2.18321

Cited by 2 publications

(2 citation statements)

References 41 publications

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“…Accordingly, many researchers have proposed grain classification (sometimes with localization) methods based on convolutional neural networks (CNNs) for grain purity inspection by an image. However, those papers are necessary to have a large number of labeling that was too expensive to be manually collected [11]. An article describes how to determine rice seed varieties using image processing techniques and artificial neural networks (ANNs) based on extracted color features.…”

Section: Introductionmentioning

confidence: 99%

Determining the Rice Seeds Quality Using Convolutional Neural Network

Hidayat

Rahmawati

Prabowo

et al. 2023

JOIV : Int. J. Inform. Visualization

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Seed inspection is crucial for plant nurseries and farmers as it ensures seed quality when growing seedlings. It is traditionally accomplished by expert inspectors filtering samples manually, but there are some challenges, such as cost, accuracy, and large numbers. Speed and accuracy were the main conditions for increasing agricultural productivity. Machine learning is a sub-science of Artificial Intelligence that can be applied in research on the classification of rice seed quality. The pipeline of a machine learning system is dataset collection, training, validation, and testing. Model making begins with taking data on the characteristics of rice seeds based on physical parameters in the form of seed shape and color. The dataset used is two thousand images divided into two categories, namely superior seeds and non-superior seeds. Training and Validation was conducted using the Convolutional Neural Network (CNN) algorithm with the concept of cross-validation on Google Collaboratory notebooks. The ratio split of train data and validation data in modeling from a dataset is 80:20. The result of the model formed is a model with the development of a Deep Convolutional Neural Network (Deep CNN) that can classify the digital image data of rice seeds from the results of data calls uploaded into the system. The results of the experiment conducted on 30 test data can be analyzed so that the system can classify superior and non-superior seeds with a precision value of 93% and a recall of 95%.

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Section: Introductionmentioning

confidence: 99%

Determining the Rice Seeds Quality Using Convolutional Neural Network

Hidayat

Rahmawati

Prabowo

et al. 2023

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

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“…Archana 18 introduced a low-cost quality assessment method of Sorghum grains for a digital image processing system, which can measure the sorghum grain shape and categorize Sorghum grains according to their quality based on their shape. Pakpoom et al 19 applied image augmentation, including rotation, brightness adjustment, and horizontal flipping, to generate a larger number of grain images from fewer data and realized image-based grain purity detection. The results indicated that image augmentation improved the performances of the convolutional neural network and bag-of-words model.…”

Section: Introductionmentioning

confidence: 99%

Semantic segmentation-based mechanized harvesting soybean quality detection

2022

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Crushing rate and impurity rate are important quality indicators of mechanically harvested soybeans. Intelligent quality detection of mechanically harvested soybeans based on machine vision is of great significance to evaluate soybean quality accurately and rapidly. This study proposes an improved U-Net method for identifying intact soybean grains, crushing soybean grains, and impurities. Based on the accurate identification of soybean components and using the quantitative model of soybean crushing rate and impurity rate, the quality of soybean samples can be detected in real-time. To this end, a soybean quality inspection system is designed to realize the dynamic collection and detection of soybean samples. The test results show that the comprehensive evaluation index values of the improved U-Net segmentation algorithm in identifying intact soybean grains, crushing soybean grains, and impurities are 93.04%, 89.40%, and 96.49%, respectively. Compared with the traditional U-Net model, the performance of the indicators is improved by 3.23%, 0.17% and 0.72%, respectively. Compared with manual detection, the maximum absolute error of the crushing rate detection of the soybean quality detection system is 0.57%, and the maximum absolute error of the impurity rate detection is 0.69%. The proposed soybean quality inspection system can be used as an effective tool for real-time online inspection of soybean quality.

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PhosopNet: An improved grain localization and classification by image augmentation

Cited by 2 publications

References 41 publications

Determining the Rice Seeds Quality Using Convolutional Neural Network

Determining the Rice Seeds Quality Using Convolutional Neural Network

Semantic segmentation-based mechanized harvesting soybean quality detection

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