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Systematic review of deep learning techniques in plant disease detection

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Abstract

Automatic identification of diseases through hyperspectral images is a very critical and primary challenge for sustainable farming and gained the attention of researchers during the past few years. The technologies proposed, and techniques adopted so far are slighted in their scope and utterly contingent on deep learning models. The performance of convolutional neural networks is emerging as the most powerful tool to diagnose and predict the infections from the crop images. The present article has reviewed some of the existing neural network's techniques that are used to process image data with prominence on detecting crop diseases. First, a review of data acquisition sources, deep learning models/architectures, and different image processing techniques used to process the imaging data provided. Second, the study highlighted the results acquired from the evaluation of various existing deep learning models and finally mentioned the future scope for hyperspectral data analysis. The preparation of this survey is to allow future research to learn larger capabilities of deep learning while detecting plant diseases by improving system performance and accuracy.

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Abbreviations

ANN:

Artificial neural networks

CPU:

Central processing unit

CNN:

Convolutional neural network

DCNN:

Deep convolutional neural network

DL:

Deep learning

FaaS:

Farm-as-a-service

FCM:

Fuzzy C-means

GPU:

Graphical processing unit

HD:

High definition

IoT:

Internet of things

ML:

Machine learning

MSVM:

Multivariant support vector machine

MYSV:

Melon yellow spot virus

NLB:

Northern leaf blight

NLP:

Natural language processing

NN:

Neural networks

OpenCV:

Open source computer vision

PReLU:

Parametric rectifier linear unit

ReLU:

Rectifier linear unit

RM:

Research methodology

SGD:

Stochastic gradient descent

SVM:

Support vector machine

MSVM:

Multiclass support vector machine

VGG:

Visual geometry group

WDD:

Wheat disease database

ZYMV:

Zucchini yellow mosaic virus

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  1. Department of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India

    M. Nagaraju & Priyanka Chawla

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  1. M. Nagaraju
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  2. Priyanka Chawla
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Nagaraju, M., Chawla, P. Systematic review of deep learning techniques in plant disease detection. Int J Syst Assur Eng Manag 11, 547–560 (2020). https://doi.org/10.1007/s13198-020-00972-1

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