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Machine Learning and Deep Learning Based Computational Techniques in Automatic Agricultural Diseases Detection: Methodologies, Applications, and Challenges

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Abstract

Plant disease detection is a critical issue that needs to be focused on for productive agriculture and economy. Detecting plant disease using traditional methods is a tedious job as it requires a tremendous amount of work, time, and expertise. Automatic plant disease detection is an important research area that has recently gained a lot of attention among the academicians, researchers, and practitioners. Machine Learning and Deep Learning can help identify the plant disease at the initial stage as soon as it appears on plant leaves. In this state-of-an-art review, a thorough investigation has been performed to evaluate the possibility of using Machine Learning models to identify plant diseases. In this study, diseases and infections of four types of crops, i.e., Tomato, Rice, Potato, and Apple, are considered. Initially, numerous possible infections and diseases on these four kinds of crops are studied along with their reason for the occurrence and possible symptoms for their detections. An in-depth study of the different steps involved in plant disease detection and classification using Machine Learning and Deep Learning is provided. Various datasets available online for plant disease detection have also been presented. Along with this, a detailed study on various existing Machine Learning and Deep Learning-based classification models proposed by different researchers across the world for four considered crops in terms of their performance evaluations, the dataset used, and the feature extraction method is discussed. At last, various challenges in the use of machine learning and deep learning for plant disease detection and future research directions are enumerated and presented.

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Abbreviations

AI:

Artificial intelligence

ANN:

Artificial neural network

BNN:

Binarized neural network

BPNN:

Back propagation neural network

CNN:

Convolutional neural network

DCNN:

Deep convolutional neural network

DL:

Deep learning

FT:

Fourier transform

GA:

Genetic algorithms

GDP:

Gross DOMESTIC product

GLCM:

Grey level co-occurrence matrix

HOG:

Histogram of oriented gradients

HIS:

Hue, saturation and intensity

HUE:

Hue, saturation value

IoT:

Internet of things

LDA:

Linear discriminant analysis

ML:

Machine learning

MSOFM:

Modified self organizing feature maps

MSVM:

Multiclass support vector machine

PCA:

Principal component analysis

RBF:

Radial based Function

ReLU:

Rectified linear unit

RF:

Random forest

RMS:

Root mean square

SVM:

Support vector machine

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Acknowledgements

We would like to thanks World Bank and National Project Implementation Unit (NPIU), MHRD, Government of India for assisting and supporting in the completion of this study.

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Wani, J.A., Sharma, S., Muzamil, M. et al. Machine Learning and Deep Learning Based Computational Techniques in Automatic Agricultural Diseases Detection: Methodologies, Applications, and Challenges. Arch Computat Methods Eng 29, 641–677 (2022). https://doi.org/10.1007/s11831-021-09588-5

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