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Feature analysis and classification of maize crop diseases employing AlexNet-inception network

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Abstract

Classification of plant diseases is an important aspect of agriculture and this proposed methodology aims at identification, prediction and classification of corn leaf disease using AlexNet architecture with transfer learning methodology and AlexNet-Inception model. Different optimizers such as Stochastic Gradient Descent with Momentum, RMSprop and ADAM were employed in training the network. A 25-layer AlexNet model with transfer learning approach was modelled to sort the dataset into 4 classes, healthy, blight, common rust, and grey leaf spot. Both the networks were trained with multiple hyper parameter configurations with various learning rates, mini batch sizes, and training-to-test ratios. The modified AlexNet-Inception network performs multiple parallel convolution operations with different sizes of filters 1 × 1, 3 × 3 and 5 × 5 and average pooling on the output of 2D max pooling layer of AlexNet layer and these outputs are concatenated to produce one output. Thus the network gets progressively wider, not deeper and the computational cost is reduced and thereby avoiding the vanishing gradient problem. The detailed analysis of the features that were prioritized by AlexNet and AlexNet-Inception network for the classification of test images were validated with LIME and Grad-CAM technique and it was proved that AlexNet-Inception network outperforms the AlexNet transfer learning approach and the accuracy achieved were 98.91% for the ideal learning rate setting of 0.0001. The trials’ findings indicate that the algorithm is more precise and quicker than conventional AlexNet model, providing a novel method for detecting abnormalities in maize plants. In a number of agricultural industries, this suggested effort can be utilized to implement in real time applications.

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Data availability

Data that support the findings is available from the corresponding author upon reasonable request.

Abbreviations

ADAM:

Adaptive Moment Estimation

CNN:

Convolutional Neural Network

DNN:

Deep Neural Network

DFCANet:

Double Fusion block with Coordinate Attention Network

F-CNN:

Faster-CNN

FCN:

Fully Convolutional Network

FP:

False Positive

FN:

False Negative

Grad-CAM:

Gradient-weighted Class Activation Mapping

LIME:

local interpretable model-agnostic

LDSNet:

Lightweight dense-scale network

LSTM:

Long Short Term Memory

RAM:

Random Access Memory

RECALL:

Repairing Errors in Computer Aided Language Learning

R-FCN:

Region-based FCN

R-CNN:

Region-based Convolutional Neural Networks

ROI:

Region of Interest

RMSProp:

Root Mean Squared propagation

ResNet:

Residual Network

SGDM:

Stochastic Gradient Descent with Momentum

TP:

True Positive

VGG:

Visual Geometry Group

YOLOv3:

You Only Look Once, Version 3

y :

True Output

y^ :

predicted output

v t :

(Momentum)

η :

momentum factor

α:

Learning Rate.

W old :

Previous Weight.

\(\frac{\partial loss}{\partial {W}_{old}}\) :

Gradient – Derivative of loss with respect to weight

γ:

Previous Gradient

W t :

Weight at any instant of time t

W t − 1 :

Weight at any instant of time t-1

ϵ :

Constant – Stopping Criteria

θ:

Objective Function

m t :

Moment at time t

β 1 and  β 2 :

(Constant)

\(\hat{\ {m}_t}\) :

Updated value of first moment

\(\hat{v_t}\) :

Updated value of Second moment

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Authors and Affiliations

  1. Dr.N.G.P Institute of Technology, Tamilnadu, Coimbatore, India

    Gayathri Devi K

  2. Dr. Mahalingam College of Engineering and Technology, Pollachi, India

    Kishore Balasubramanian

  3. Sri Krishna College of Technology, Coimbatore, Tamilnadu, India

    Senthilkumar C

Authors
  1. Gayathri Devi K
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  3. Senthilkumar C
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Correspondence to Kishore Balasubramanian.

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K, G.D., Balasubramanian, K. & C, S. Feature analysis and classification of maize crop diseases employing AlexNet-inception network. Multimed Tools Appl 83, 26971–26999 (2024). https://doi.org/10.1007/s11042-023-16467-7

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