Classification of Vegetable Plant Pests using Deep Transfer Learning

Vijayakanthan, G.; Kokul, T.; Pakeerathai, S.; Pinidiyaarachchi, U.A.J.

doi:10.1109/iciafs52090.2021.9606176

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…Sci. 2023, 13, 11644 Acknowledgments: This study is supported by the Scientific and Technological Research Council of Türkiye (TÜB ˙ITAK) with the project ID 1919B012300710.…”

Section: Institutional Review Board Statement: Not Applicablementioning

confidence: 99%

“…The authors propose a custom learning model based on CNN and state that the model achieves 91.3% accuracy for food prediction. It can be argued that the classification of ready-to-eat food or unpackaged products of larger sizes becomes more straightforward using basic image processing techniques, as applied in [12,13]. On the other hand, smaller products such as nuts are somewhat more difficult to classify.…”

Section: Introductionmentioning

confidence: 99%

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A Real-Time Nut-Type Classifier Application Using Transfer Learning

Özçevik

2023

Applied Sciences

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Smart environments need artificial intelligence (AI) at the moment and will likely utilize AI in the foreseeable future. Shopping has recently been seen as an environment needing to be digitized, especially for payment processes of both packaged and unpackaged products. In particular, for unpackaged nuts, machine learning models are applied to newly collected dataset to identify the type. Furthermore, transfer learning (TL) has been identified as a promising method to diminish the time and effort for obtaining learning models for different classification problems. There are common TL architectures that can be used to transfer learned knowledge between different problem domains. In this study, TL architectures including ResNet, EfficientNet, Inception, and MobileNet were used to obtain a practical nut-type identifier application to satisfy the challenges of implementing a classifier for unpackaged products. In addition to the TL models, we trained a convolutional neural network (CNN) model on a dataset including 1250 images of 5 different nut types prepared from online-available and manually captured images. The models are evaluated according to a set of parameters including validation loss, validation accuracy, and F1-score. According to the evaluation results, TL models show a promising performance with 96% validation accuracy.

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“…Sci. 2023, 13, 11644 Acknowledgments: This study is supported by the Scientific and Technological Research Council of Türkiye (TÜB ˙ITAK) with the project ID 1919B012300710.…”

Section: Institutional Review Board Statement: Not Applicablementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Real-Time Nut-Type Classifier Application Using Transfer Learning

Özçevik

2023

Applied Sciences

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“…However, pest and disease attacks are still one of the factors that become obstacles in the cultivation of vegetable crops because they can cause a decrease in production yields. Conventional pest classification methods make the process of classifying pests and diseases by farmers very complex, inefficient, and prone to errors, so the results are inaccurate [2]. The more varied types of pests have different impacts on crops, so if farmers incorrectly identify the class of pests, the treatment will be ineffective [2].…”

Section: Introductionmentioning

confidence: 99%

“…Conventional pest classification methods make the process of classifying pests and diseases by farmers very complex, inefficient, and prone to errors, so the results are inaccurate [2]. The more varied types of pests have different impacts on crops, so if farmers incorrectly identify the class of pests, the treatment will be ineffective [2]. The practical and timely detection, identification, and localization of pests is a real challenge associated with precision agriculture.…”

Section: Introductionmentioning

confidence: 99%

Application of pest detection on vegetable crops using the cnn algorithm as a smart farm innovation to realize food security in the 4.0 era

2022

joscex

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Pests and diseases are one of the factors that become obstacles in the cultivation of vegetables because they can cause a decrease in the quality and quantity of production. The more varied types of pests have different impacts on crops, so if farmers incorrectly identify the class of pests, the treatment will be ineffective. Therefore, we need a technology that can classify the types of pests on vegetable crops to maintain the quality and quality of the product as well as the abundant harvest. The classification model of pests on vegetables using the deep learning method using the Convolutional Neural Network (CNN) algorithm with a high level of accuracy is the solution to this problem. The application of artificial intelligence in the agricultural sector also supports smart agriculture in Indonesia. Based on the research that has been carried out, the application of pest classification on vegetable crops made by applying the CNN model using the Inception V3 -k-fold cross validation method has a test accuracy rate of 99%, meaning that the application can perform pest classification correctly.

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Image Processing: A Smart Technology for Early Detection of Crop Pests and Diseases

Pakeerathan

2023

Advanced Technologies and Societal Change

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Classification of Vegetable Plant Pests using Deep Transfer Learning

Cited by 7 publications

References 20 publications

A Real-Time Nut-Type Classifier Application Using Transfer Learning

A Real-Time Nut-Type Classifier Application Using Transfer Learning

Application of pest detection on vegetable crops using the cnn algorithm as a smart farm innovation to realize food security in the 4.0 era

Image Processing: A Smart Technology for Early Detection of Crop Pests and Diseases

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