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

Agriculture 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 u...

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Bibliographic Details
Published in:Land degradation & development Vol. 35; no. 11; pp. 3642 - 3652
Main Authors: K S, Guruprakash, P, Siva Karthik, A, Ramachandran, K, Gayathri
Format: Journal Article
Language:English
Published: Chichester, UK John Wiley & Sons, Ltd 15.07.2024
Wiley Subscription Services, Inc
Subjects:
Accuracy
Agriculture
Classification
Crop damage
crop pest identification
Crop yield
Crops
data collection
deep convolutional encoder–decoder network
Deep learning
Digital agriculture
Feature extraction
Food contamination
Food plants
Food sources
image enhancement
Information processing
land degradation
MobileENet
Performance evaluation
Performance measurement
pest identification
Pesticides
Pests
Plant extracts
plant pests
Regularization
ISSN:1085-3278, 1099-145X
Online Access:Get full text
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Summary:Agriculture 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.
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ISSN:1085-3278
1099-145X
DOI:10.1002/ldr.5157