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The purpose of this paper is to study the comparative analysis between three hybrid nanofluids flow past a permeable stretching surface in a porous medium with thermal radiation. Uniform magnetic field is applied together with heat source and sink. Three set of different hybrid nanofluids with water as a base fluid having suspension of Copper-Aluminum Oxide (Cu−Al2O3), Silver-Aluminum Oxide (Ag−Al2O3) and Copper-Silver (Cu−Ag) nanoparticles are considered. The Marangoni boundary condition is applied.

The governing model of the flow is solved by Runga–Kutta fourth-order method with shooting technique, using appropriate similarity transformations. Temperature and velocity field are explained by the figures for many flow pertinent parameters.

Almost same behavior is observed for all the parameters presented in this analysis for the three set of hybrid nanofluids. For increased mass transfer wall parameter ( fw) and Prandtl Number (Pr), heat transfer rate cuts down for all three sets of hybrid nanofluids, and reverse effect is seen for radiation parameter (R), and heat source/sink parameter ( δ).

The thermal conductivity of hybrid nanofluids is much larger than the conventional fluids; thus, heat transfer efficiency can be improved with these fluids and its implications can be seen in the fields of biomedical, microelectronics, thin-film stretching, lubrication, refrigeration, etc.

The current analysis is to optimize heat transfer of three different radiative hybrid nanofluids ( Cu−Al2O3/H2O, Ag−Al2O3/H2O and Cu−Ag/H2O) over stretching surface after applying heat source/sink with Marangoni convection. To the best of the authors’ knowledge, this work is new and never published before.

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.

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.

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.

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.