Energy-Efficient Data Transmission to Detect Pest in Cauliflower Farm

Sneha, J. Adeline; Chakravarthi, Rekha

doi:10.1007/978-981-15-7394-1_60

Cited by 1 publication

(2 citation statements)

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“…The following comparative methods are considered: Adaboost (Liu et al , 2016), SVM (Liu et al , 2016), deep-CNN (Nam and Hung, 2018), GOA-based deep-CNN (Mirjalili et al , 2018) and MFO-based deep-CNN (Mirjalili, 2015) are compared with the proposed MFGHO-based deep-CNN classifier, with respect to the data aggregation strategies, namely, O-SEED + Rider-Cat swarm optimization (RCSO) (Shyjith et al , 2020), genetic spider monkey optimization (GMSO) + spider monkey optimization (SMO) (Soundaram and Arumugam, 2020), TOPSIS + fuzzy multi-criteria clustering and bio-inspired energy efficiency routing (FMCB-ER) (Mehta and Saxena, 2020), FABC + water wave optimization (WWO) (Kumar and Kumar, 2016; Zheng, 2015), protruder optimization, ABC + invasive weed optimization (IWO) (Misaghi and Yaghoobi, 2019; Zheng, 2015), artificial bee colony (ABC) + protruder optimization (Misaghi and Yaghoobi, 2019; Zheng, 2015; Karaboga and Basturk, 2008) and FABC + protruder optimization algorithm [23] (Misaghi and Yaghoobi, 2019; Zheng, 2015).…”

Section: Resultsmentioning

confidence: 99%

“…However, this strategy suffers with the difficulty of sensing the images remotely for the prediction process. The routing protocols based on energy efficiency concept are performed by various researchers (Mirjalili, 2015; Mirjalili et al , 2018; Misaghi and Yaghoobi, 2019; Zheng, 2015; Shyjith et al , 2020; Soundaram and Arumugam, 2020; Mehta and Saxena, 2020; Karaboga and Basturk, 2008) through the bio-inspired algorithms, where the convergence of the optimizations to the global optimal solution is minimal with the need for an algorithm that possesses higher tendency toward the local optima avoidance criterion. Similarly, in Nam and Hung (2018), Dadheech et al (2022), Sneha and Rekha (2020) and Rajmohan et al (2021), the machine learning and deep learning models are presented, which suffered from the accuracy concerns because of the lack of the appropriate training.…”

Section: Motivation For the Researchmentioning

confidence: 99%

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Advanced pest detection strategy using hybrid optimization tuned deep convolutional neural network

Thakare

2022

JEDT

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Purpose Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model. Design/methodology/approach The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents. Findings The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods. Originality/value The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

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