Identification of weed seeds species in mixed sample with wheat grains using SIFT algorithm

Wafy, Maged; Ibrahim, Hashem; Kamel, Enas

doi:10.1109/icenco.2013.6736468

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…Later, studies focus on hand crafted features such as Scale Invariant Feature Transform (SIFT) [12,13] and the histograms of gradient orientation (HOG) [14], which achieve high accuracy on controlled conditions for one seed on contrastive background.…”

Section: Related Workmentioning

confidence: 99%

Product Based Classification of Bulk Food Grains using Bag of Visual Words and Deep Features

Ali¹,

Mohammed²,

Nour³

2021

Kafrelsheikh Journal of Information Sciences

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The goal of this research is to compare between the performance of the traditional machine learning classification algorithm using Bag of Visual Words (BoVW) method and off-the-shelf deep features extracted by VGG-19, and Inception-V3 models and trained SVMs using the extracted features. By comparing the AUC, sensitivity, and specificity of SVM with VGG-19 and Inception-V3, we can conclude that off-the-shelf deep features has an important impact on food grains image classification.

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Section: Related Workmentioning

confidence: 99%

Product Based Classification of Bulk Food Grains using Bag of Visual Words and Deep Features

Ali¹,

Mohammed²,

Nour³

2021

Kafrelsheikh Journal of Information Sciences

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“…In 2005, the team established a dataset of 236 kinds of 10,310 images and explored the influence of different feature combinations on different classifiers 17 . Wafy et al 18 . extracted SIFT features from RGB images for seed classification.…”

Section: Introductionmentioning

confidence: 99%

“…16 In 2005, the team established a dataset of 236 kinds of 10,310 images and explored the influence of different feature combinations on different classifiers. 17 Wafy et al 18 extracted SIFT features from RGB images for seed classification. In 2019, Kwoopnaan et al 19 compared substandard and high-quality maize seeds through 30 features and demonstrated that color features are reliable features to distinguish their quality.…”

Section: Introductionmentioning

confidence: 99%

Research on plant seeds recognition based on fine-grained image classification

Yuan,

Dong,

et al. 2023

J. Electron. Imag.

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Seed phenomics is a comprehensive assessment of complex seed traits, and seed classification is an indispensable step. Plant seed recognition is of great significance in agricultural production, ecological environment, and biodiversity. However, some traditional artificial plant seed classification methods are expensive, time consuming, and laborious. Therefore, there is a need that cannot be ignored for a method to improve the situation. Artificial intelligence is making a huge impact on various fields through its perception, reasoning, and learning capabilities. A challenge in pratacultural research, the rapid auto-identification of plant seeds, might be better resolved by the integration of computer vision. For the lack of a public seed dataset for the training of models, we established a dataset called LZUPSD, which includes images of 88 different species of seeds. We explored methods to achieve fine-grained seed classification using convolutional neural networks and tried to apply a transformer to it. The method has the highest accuracy of more than 95%. The method is able to identify plant seeds automatically with high speed, low cost, and high accuracy. It results in a more efficient plant seed recognition method. At the same time, we have established a platform where users can upload pictures to obtain seed information. In addition, our dataset will be released to the public in the next phase in order to share with interested researchers.

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“…Here we used the following five steps for image processing , they were image preprocessing ,Shrinkage Morphological operation , Edge detection , Object measurements , and object classification . The authors of [13] discussed Here in this research they used the SIFT algorithm to point out pot seeds species in a sample with cereal grains. The results were surprising, as we found that some weed seeds were very similar in size and shape to wheat grains.…”

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confidence: 99%

Literature Survey on Grain Type Prediction Using Machine Learning

Reddy¹,

Gupta²,

Raj³

et al. 2023

IJRASET

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Classification of grains according to their type becomes very much necessary because there are multiple grains available in the market today. Classification of Grains according their types manually is not efficient. Classification becomes a really wearisome task when it comes to doing it hand-operated instead of automating the process. Manually classifying would be a grueling task. There is a need for mentally keen and quick system which can get better of this difficulty by automating these processes. It should be able to recognize and label the every single grain according to their respective type automatically. Therefore we conducted a literature survey on how various seeds were classified and various analysis which were used to obtain the various desired result. Here we observed that various researchers used k-means, CNN and various other algorithms.

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Identification of weed seeds species in mixed sample with wheat grains using SIFT algorithm

Cited by 6 publications

References 7 publications

Product Based Classification of Bulk Food Grains using Bag of Visual Words and Deep Features

Product Based Classification of Bulk Food Grains using Bag of Visual Words and Deep Features

Research on plant seeds recognition based on fine-grained image classification

Literature Survey on Grain Type Prediction Using Machine Learning

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