Abstract
Plant diseases impact extensively on agricultural production growth. It results in a price hike on food grains and vegetables. To reduce economic loss and to predict yield loss, early detection of plant disease is highly essential. Current plant disease detection involves the physical presence of domain experts to ascertain the disease; this approach has significant limitations, namely: domain experts need to move from one place to another place which involves transportation cost as well as travel time; heavy transportation charge makes the domain expert not travel a long distance, and domain experts may not be available all the time, and though the domain experts are available, the domain expert(s) may charge high consultation charge which may not be feasible for many farmers. Thus, there is a need for a cost-effective, robust automated plant disease detection or classification approach. In this line, various plant disease detection approaches are proposed in the literature. This systematic study provides various Deep Learning-based and Machine Learning-based plant disease detection or classification approaches; 160 diverse research works are considered in this study, which comprises single network models, hybrid models, and also real-time detection approaches. Around 57 studies considered multiple plants, and 103 works considered a single plant. 50 different plant leaf disease datasets are discussed, which include publicly available and publicly unavailable datasets. This study also discusses the various challenges and research gaps in plant disease detection. This study also highlighted the importance of hyperparameters in deep learning.
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Abbreviations
- ABC:
-
Artificial Bee Colony
- AE:
-
Auto Encoders
- ALHS:
-
Apple Leaf Hyper-Spectral
- ANN:
-
Artificial Neural Network
- ASA:
-
Adaptive Snake Algorithm
- AT:
-
Augmentation Technique
- BM:
-
Black Measles
- BS:
-
Bacterial Spot
- CBA:
-
Convolutional Block Attention
- CNN:
-
Convolutional Neural Network
- DBN:
-
Deep Belief Network
- DCNN:
-
Deep Convolutional Neural Networks
- DLM:
-
Deep Learning Model
- DM:
-
Downy Mildew
- DRT:
-
Dimensionality Reduction Technique
- DSC:
-
Depthwise Separable Convolution
- DTL:
-
Dual Transfer Learning
- EB:
-
Early Blight
- FBFN:
-
Fuzzy-Based Function Network
- FM:
-
Feature Maps
- FC:
-
Fully Connected
- FCV:
-
Fold Cross Validation
- FRCNN:
-
Faster R-CNN
- GAN:
-
Generative Adversarial Networks
- GConv:
-
Group-Convolution
- GPDCNN:
-
Global Pooling Dilated CNN
- GR-CNN:
-
Gated Recurrent CNN
- HS:
-
Hyper-Spectral
- HSV:
-
Hue Saturation Value
- INAR:
-
Inception And Rainbow
- IoT:
-
Internet of Things
- KNN:
-
K-Nearest Neighbours
- LB:
-
Late Blight
- LDA:
-
Linear Discriminant Analysis
- LDSN:
-
Lightweight Dense-Scale Network
- LM:
-
Leaf Mold
- LS:
-
Leaf Spot
- MCNN:
-
Multilayer CNN
- MLP:
-
Multi-Layer Perceptron
- MV:
-
Mosaic Virus
- PCA:
-
Principal Component Analysis
- PD2SE-Net:
-
Plant Disease Diagnosis and Severity Estimation Network
- PLS-DA:
-
Partial Least Squares Discriminant Analysis
- PM:
-
Powdery Mildew
- PN:
-
Plant Name
- PRISMA:
-
Preferred Reporting Items for Systematic Reviews and Meta-Analyses
- PV:
-
PlantVillage
- R-CNN:
-
Region-based CNN
- RF:
-
Random Forest
- RI-Net:
-
Residual Inception Network
- RoI:
-
Region of Interest
- RRDN:
-
Restricted Residual Dense Network
- SGD:
-
Stochastic Gradient Descent
- SIFT:
-
Scale-Invariant Feature Transform
- SIMCA:
-
Soft Independent Modeling of Class Analogy
- SSMD:
-
Single-Shot Multi-box Detector
- SVM:
-
Support Vector Machine
- TLAlexNet:
-
Transfer learning AlexNet
- UAV:
-
Unmanned Aerial Vehicle
- VGG:
-
Visual Geometry Group
- VI:
-
Vegetation Indices
- WDD:
-
Wheat Disease Dataset
- WT:
-
Wavelet Transform
- YLCV:
-
Yellow Leaf Curl Virus
- YOLO:
-
You Only Look Once
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Sunil, C.K., Jaidhar, C.D. & Patil, N. Systematic study on deep learning-based plant disease detection or classification. Artif Intell Rev 56, 14955–15052 (2023). https://doi.org/10.1007/s10462-023-10517-0
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