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Crop pest identification using deep network based extracted features and MobileENet in smart agriculture

229
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July 15, 2024
Published Date

Research Abstract & Technology Focus

AbstractAgriculture has been considered an important source of food for humans throughout history. Plant pests cause significant damage to crops and reduce the productivity of global crop yields. Therefore, it is important to identify the plant pest at an earlier stage in order to minimize crop losses and use pesticides optimally. This paper develops the MobileENet deep learning architecture for accurate plant pest identification with less computational effort. The input images are pre‐processed, and the features are extracted using a deep convolutional encoder–decoder network (DCEDN). The proposed classification approach solves the problems of over‐fitting regularization, batch normalization, and dropout layers. Due to the minimum computing size and factorization process, the classification performance is increased. It extracts discriminatory feature information by eliminating redundant background information. The performance of the proposed approach is evaluated on the IP102 dataset, and the performance is compared with existing deep learning‐based approaches. The performance metrics, such as accuracy, precision, recall, and so forth, are considered to evaluate the performance of the proposed plant pest identification approach. The accuracy performance of the proposed approach is improved to 98.83% with less information loss.
identification network extracted features mobileenet smart
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What is the core focus of the research titled 'Crop pest identification using deep network based extracted features and MobileENet in smart agriculture'?

This literature focuses on: AbstractAgriculture has been considered an important source of food for humans throughout history. Plant pests cause significant damage to crops and reduce the productivity of global crop yields. Therefore, it is important to identify the plant pe...

Are there open-source GitHub repositories related to Crop pest identification using deep network based extracted features and MobileENet in smart agriculture?

Yes, open-source projects like QuipNetwork/quip-node-manager (A simple GUI client to manage a Quip Network node) are actively building upon these concepts.

Which startups are commercializing the technology behind Crop pest identification using deep network based extracted features and MobileENet in smart agriculture?

Products like tasteit are bringing this to market. Their focus is: The food social network to meet people over food.

What other academic literature is closely related to 'Crop pest identification using deep network based extracted features and MobileENet in smart agriculture'?

Yes, highly correlated activity was mapped. An entry titled 'Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture' discusses this: Abstract Plant diseases cause significant damage to agriculture, leading to substantial yield losses and posing a major threat to food se...

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