Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review
Abstract
:1. Introduction
2. Types of UAVs, Their Platforms and Peripherals Used in Disease Monitoring and Identification
3. Cameras and Sensors
3.1. RGB Camera
3.2. Multispectral Cameras
3.3. Hyperspectral Cameras
3.4. Thermal Cameras
3.5. Depth Sensors
4. Image Pre-Processing
5. Data Processing
5.1. Image Data Processing
5.1.1. K-Means Clustering
5.1.2. Regression Analysis
5.1.3. Vegetation Indices
6. Deep Learning Models
6.1. Artificial Neural Networks (ANNs)
6.2. Convolutional Neural Networks (CNNs)
7. Challenges of Automatic Plant Disease Identification Using UAVs
8. Future Considerations
9. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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References | Crop | Disease 1 | Accuracy 1,2 | Architectures |
---|---|---|---|---|
[150,151] | Apple, Blueberry, Cherry, Corn, Grape, Orange, Peach, Bell Pepper, Potato, Raspberry, Soybean, Squash, Strawberry, and Tomato | Cedar-Apple apple rust, scab, apple black rot, apple powdery mildew, gray leaf spot, corn common rust, northern corn leaf blight, grape black rot, esca complex, Pseudocercospora leaf spot, HLB, bacterial spot of peach and bell pepper, early and late blight of potato, squash powdery mildew, strawberry leaf scorch, early and late blight of tomato, target leaf spot, leaf mold, bacterial leaf spot, TMV, and yellow leaf curl virus of tomato | AlexNet (85.53%), GooLeNet (99.34%) | AlexNet and GoogLeNet |
[50] | Citrus | HLB | 93.3% | SVM |
[33] | Citrus | HLB | SVM with kernel (85%) | SVM with kernel, SVM, LDA and QDA |
[152] | Corn | Corn leaf blight, common rust and grey leaf spot | 92.85% | CNN, Neuroph studio |
[44] | Cotton | Ramularia blight | 79% | MLR, MLRb, SVM, RFT |
[153] | Cotton | Cotton root rot | KMSEG (88.5%), KMSVM (87.77%) | KMSEG, and KMSVM |
[36] | Grape | Leaf stripe disease | - | ANOVA |
[154] | Grape | Vine diseases | SegNet corrected (88.26%) | SegNet (fusion) and SegNet (corrected) |
[68] | Grape | Esca complex | Varied according to sizes of patches | CNN |
[99] | Paperbark Tea | Myrtle rust | 97.35% | XGBoost |
[155] | Potato | Blackleg disease | 91% | Thresholding |
[53] | Potato | Late blight | - | Thresholding |
[156] | Potato | Potato virus Y | 84% | User defined CNN |
[157] | Radish | Fusarium wilt | Over 90% | GoogLeNet |
[125] | Soybean | Bacterial blight, bacterial pustule, SDS, Septoria brown spot, and frogeye leaf spot | 94.13% | AlexNet |
[123] | Soybean | Septorial leaf blightbrown spot, frogeye leaf spot, and downy mildew | 99.35% | AlexNet and LeNet |
[27] | Soybean | Asian rust, mildew, powdery mildew | Inception (99.04%) | InceptionV3, VGG-19, ResNet-50, and Xception |
[145] | Tomato | Early blight, yellow leaf curl, Corynespora leaf spot, leaf mold, TMV, late blight, septoria leaf spot | ResNet (97.28%) | AlexNet, GoogLeNet, and ResNet |
[45] | Tomato | Target spot and bacterial spot | MLP (99%) | MLP and STDA |
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Neupane, K.; Baysal-Gurel, F. Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review. Remote Sens. 2021, 13, 3841. https://doi.org/10.3390/rs13193841
Neupane K, Baysal-Gurel F. Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review. Remote Sensing. 2021; 13(19):3841. https://doi.org/10.3390/rs13193841
Chicago/Turabian StyleNeupane, Krishna, and Fulya Baysal-Gurel. 2021. "Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review" Remote Sensing 13, no. 19: 3841. https://doi.org/10.3390/rs13193841