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Review
. 2020 Oct 1;9(10):1302.
doi: 10.3390/plants9101302.

Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Reem Ibrahim Hasan  1   2 , Suhaila Mohd Yusuf  1 , Laith Alzubaidi  2   3
Affiliations
Review

Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Reem Ibrahim Hasan et al. Plants (Basel). .

Abstract

Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy.

Keywords: deep learning; feature extraction; feature visualisation; plant diseases; shallow classifier; transfer learning.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
The current challenges of plant disease detection and crop management.
Figure 2
Figure 2
Citrus fruit in a real environment with the infected region of the real image highlighted for map segmentation purposes [25].
Figure 3
Figure 3
Capsicum leaf segmented, with infected regions highlighted; 100% accuracy achieved for Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers [26].
Figure 4
Figure 4
Strawberry leaf with different cell sizes Histogram of Gradients (HOG) was applied (white arrows indicate the obtained gradient information; RGB patches directions and arrow lengths represent the size of the gradients) [75].
Figure 5
Figure 5
Basic findings regarding transfer learning [20,78,83,86,87].
Figure 6
Figure 6
Highest accuracy levels achieved using different techniques.
Figure 7
Figure 7
Image samples for lesion spot detection; maize leaves with different natural lighting angles and complex surroundings [122].
Figure 8
Figure 8
Image samples for fruit detection; cherries and plums with leaf–fruit overlaps [109].
See this image and copyright information in PMC

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