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Optimized convolutional neural network for identification of maize leaf diseases with adaptive ageist spider monkey optimization model

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Abstract

In recent years, the number of maize disease species has increased, which obviously increases the level of damages in leaves. The reason for maize leaf disease is due to variations in agriculture systems, the variants of pathogen, and it also occurs due to the scarcity of plant conservation measures. The disease in maize leaves can be exhibited by varied symptoms; however, it might be complex for farmers to identify the disease in naked eye. Therefore, this paper intends to present a new automatic system for identifying and diagnosing maize leaf diseases. The proposed model includes two major phases: Proposed Feature Extraction and Classification. The first phase is feature extraction, where the proposed 4D-Local Binary Pattern (4D-LBP) based texture features will be extracted. More particularly, Dimension 1 insists pixel intensity, dimension 2 insists angle, dimension 3 insists local frequency from intensity patch and dimension 4 insists global frequency as well. Once the features get extracted, they are subjected for classification process, where the optimized Convolutional Neural Network (CNN) is used, where the count of convolutional layers is optimally tuned. For this optimal selection, a new Adaptive Opposition based Spider Monkey optimization (AOSMO), which is the enhanced version of SMO algorithm. At last, the performance of proposed work is evaluated over other traditional models with respect to accuracy.

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Abbreviations

LBP:

Local binary pattern

CNN:

Convolutional neural network

AOSMO:

Adaptive opposition based spider monkey optimization

ANN:

Artificial NN

SVM:

Support vector machine

DAS-ELISA:

Double antibody sandwich-ELISA

RTPCR:

Reverse transcriptase polymerase chain reaction

RF:

Random forest

MLP:

Multi-layer perceptron

FFSS:

Fission fusion social structure

LLP:

Local leader phase

GLP:

Global leader phase

GLL:

Global leader learning phase

LLL:

Local leader learning phase

LLD:

Local leader decision phase

k-NN:

K-nearest neighbour

GLD:

Global leader decision phase

MCC:

Matthews correlation coefficient

NPV:

Net present value

FOR:

False omission rate

TS:

Threat score

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Authors and Affiliations

  1. APPA Institute of Engineering and Technology, Kalburgi, Karnataka, India

    Shravankumar Arjunagi

  2. Govt College of Engineering, Gangavati, Karnataka, India

    Nagaraj B. Patil

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  1. Shravankumar Arjunagi
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  2. Nagaraj B. Patil
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Correspondence to Shravankumar Arjunagi.

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Arjunagi, S., Patil, N.B. Optimized convolutional neural network for identification of maize leaf diseases with adaptive ageist spider monkey optimization model. Int. j. inf. tecnol. 15, 877–891 (2023). https://doi.org/10.1007/s41870-021-00657-3

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  • DOI: https://doi.org/10.1007/s41870-021-00657-3

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