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Review
. 2019 Oct 31;8(11):468.
doi: 10.3390/plants8110468.

Plant Disease Detection and Classification by Deep Learning

Muhammad Hammad Saleem  1 , Johan Potgieter  2 , Khalid Mahmood Arif  3
Affiliations
Review

Plant Disease Detection and Classification by Deep Learning

Muhammad Hammad Saleem et al. Plants (Basel). .

Abstract

Plant diseases affect the growth of their respective species, therefore their early identification is very important. Many Machine Learning (ML) models have been employed for the detection and classification of plant diseases but, after the advancements in a subset of ML, that is, Deep Learning (DL), this area of research appears to have great potential in terms of increased accuracy. Many developed/modified DL architectures are implemented along with several visualization techniques to detect and classify the symptoms of plant diseases. Moreover, several performance metrics are used for the evaluation of these architectures/techniques. This review provides a comprehensive explanation of DL models used to visualize various plant diseases. In addition, some research gaps are identified from which to obtain greater transparency for detecting diseases in plants, even before their symptoms appear clearly.

Keywords: convolutional neural networks (CNN); deep learning; plant disease.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Summary of the evolution of deep learning from 1943–2006.
Figure 2
Figure 2
Flow diagram of DL implementation: First, the dataset is collected [25] then split into two parts, normally into 80% of training and 20% of validation set. After that, DL models are trained from scratch or by using transfer learning technique, and their training/validation plots are obtained to indicate the significance of the models. Then, performance metrics are used for the classification of images (type of particular plant disease), and finally, visualization techniques/mappings [55] are used to detect/localize/classify the images.
Figure 3
Figure 3
Summary of the evolution of various deep learning models from 2012 until now.
Figure 4
Figure 4
Feature maps after the application of convolution to an image: (a) real image, (b) first convolutional layer filter, (c) rectified output from first layer, (d) second convolutional layer filter, (e) output from second layer, (f) output of third layer, (g) output of fourth layer, (h) output of fifth layer [27].
Figure 5
Figure 5
Tomato plant disease detection by heat map: on left hand side (a) tomato early blight, (b) tomato septoria leaf spot, (c) tomato late blight and (d) tomato leaf mold) and saliency map; on right hand side (a) tomato healthy, (b) tomato late blight, (c) tomato early blight, (d) tomato septoria leaf spot, (e) tomato early blight, (f) tomato leaf mold) [55].
Figure 6
Figure 6
Detection of maize disease (indicated by red circles) by heat map [70].
Figure 7
Figure 7
Bounding box indicates the type of diseases along with the probability of their occurrence [68]. A bounding box technique was used in Figure 7 in which (a) represents the one type of disease along with its rate of occurrence, (b) indicates three types of plant disease (miner, temperature, and gray mold) in a single image, (c,d) shows one class of disease but contains different patterns on the front and back side of the image, (e,f) displays different patterns of gray mold in the starting and end stages [68].
Figure 8
Figure 8
(a) Teacher/student architecture approach; (b) segmentation using a binary threshold algorithm [67].
Figure 9
Figure 9
Comparison of Teacher/student approach visualization map with the previous approaches [67].
Figure 10
Figure 10
Activation visualization for detection of apple plant disease to show the significance of a VGG-Inception model (the plant disease is indicated by the red circle) [85].
Figure 11
Figure 11
Segmentation and edge map for olive leaf disease detection [65].
Figure 12
Figure 12
Deep learning models used in the particular number of research papers.
Figure 13
Figure 13
Sample images of OR-AC-GAN (a hyperspectral imaging model) [112].
Figure 14
Figure 14
Hyperspectral images by UAV: (a) RGB color plots, (b) Random-Forest classifier, and (c) proposed multiple Inception-ResNet model [114].
See this image and copyright information in PMC

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