Plant Disease Prognosis Using Spatial-Exploitation-Based Deep-Learning Models †
Abstract
:1. Introduction
2. Literature Review
3. Methodology
4. Proposed Approach
4.1. Infrastructure and Tools
4.2. Predictive Analytics Process
- Defining a Project: Identification and definition of research objectives, scope, and datasets used for experimentation.
- Data Gathering: Preparation and formulation of data through data-mining techniques from multiple sources.
- Data Analysis: Preprocessing stages, such as resizing, normalizing, and modeling data, to extract usable information and draw conclusions.
- Statistics: Validation of hypotheses and assumptions through statistical analysis using appropriate models.
- Modelling: Creation of precise predictive models automatically, allowing for multiple evaluations to select the optimal solution.
- Deployment: Automating decisions based on the models to integrate analytical results into routine decision-making processes, generating results, reports, and output.
4.3. Knowledge-Based Expert Systems for Crop Disease Diagnosis
4.4. Plant Disease Diagnosis
- Accurate Plant Identification: Identifying the infected plants, including scientific and generic names.
- Distinguishing Characteristics: Recognizing the distinctive traits of healthy and diseased parts, accounting for variations in patterns, coloration, and growth rates.
- Symptom and Sign Analysis: Identifying specific symptoms, such as stunted growth, tissue overgrowth, tissue death, and variations in appearance. Differentiating between symptoms and analyzing ecological causative agents.
- Affected-Plant-Part Detection: Noting which plant parts are affected, such as roots, leaves, or stems.
- Symptom Distribution: Observing the spread of affected plants in the area, noting patterns and distributions.
- Host Specificity: Determining if the issue affects specific plant species or multiple species, aiding in understanding potential causes.
4.5. Plant Disease Management
- Exclusion: Preventing disease spread through geographical barriers and local prevention methods.
- Eradication: Eliminating the disease after introduction but before widespread dissemination.
- Protection: Implementing barriers, either mechanical, temporal, or economic, to prevent infection.
- Resistance: Using disease-resistant plants as a primary prevention method.
- Integrated Disease Management (IDM): Employing a combination of tactics, methods, disease diagnosis, and environmental monitoring to manage diseases effectively.
4.6. Methodology: Deep CNN and Otsu-Based Image Segmentation
- Data Acquisition: Utilizing real-time field images and the “PlantVillage” dataset, dividing the data into training, validation, and testing sets.
- Model Construction: Creating a multiclass multilayer CNN architecture suited for processing various images independent of size or orientation.
- Training and Validation: Scaling, normalizing, and training the model iteratively on the dataset to adapt to different images.
- Classification: Employing the trained deep CNN to categorize images into predefined classes, assessing its real-time performance on unseen images.
4.7. Algorithm
5. Results and Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Reference | Methodology | Finding | Limitation | Advantage |
---|---|---|---|---|
[4] | Machine learning for disease detection | Enhanced accuracy in diagnosis | Dependent on data quality and quantity | Rapid, noninvasive diagnosis |
[5] | Automated diagnosis challenges and opportunities | Integration of technology in agriculture | Limited access to advanced technology | Potential for early intervention |
[6] | Convolutional neural networks | Improved early detection | Model complexity and training time | High accuracy and speed |
[7] | Multi spectral image processing | Training and testing process | Model complexity | Proof of concept |
[8] | Few shot learning approach | Fast processing | Complex model | Improved Speed |
[9] | Regression technique | Hyperspectral images | Less accuracy | Improved speed |
[10] | Deep learning for disease detection | Accurate multiclass classification | Requires large labeled datasets | Robust and scalable detection |
[11] | Deep transfer learning | Enhanced network | Requires less data | Improved Accuracy |
[12] | Hyperspectral images and machine learning | Enhanced spectral disease detection | Hardware and cost limitations | Improved spectral resolution |
[13] | Deep-learning model for citrus diseases | High accuracy in citrus disease identification | Limited to specific diseases | Accurate and quick diagnosis |
[14] | New image processing Techniques | Improved accuracy | Time delay in processing | Accuracy improved |
[15] | Review of deep-learning techniques | Comprehensive overview of deep-learning applications | Lack of standardization | Wide applicability and effectiveness |
[16] | AI and ML applications | Diverse AI and ML applications | Lack of interpretability | Broad coverage of AI techniques |
[17] | Computer vision with ML algorithms | Integration of technique | Improved interpretability | Improved accuracy |
[18] | Machine learning in smart agriculture | Integration of AI in agriculture | Infrastructure constraints | Enhanced efficiency and productivity |
[19] | Deep learning for disease classification | High accuracy in classification | Data imbalance issues | Effective for large datasets |
Models | Transfer Learning | Training from Scratch | ||||||
---|---|---|---|---|---|---|---|---|
A | P | R | F1 | A | P | R | F1 | |
LENET | 0.97 | 0.96 | 0.97 | 0.94 | 0.92 | 0.94 | 0.90 | 0.90 |
ALEXNET | 0.98 | 0.97 | 0.98 | 0.96 | 0.94 | 0.92 | 0.92 | 0.90 |
ZFNET | 0.97 | 0.99 | 0.99 | 0.98 | 0.94 | 0.91 | 0.91 | 0.91 |
VGG-16 | 0.99 | 0.99 | 0.99 | 0.98 | 0.94 | 0.93 | 0.91 | 0.91 |
VGG-19 | 0.99 | 0.99 | 0.99 | 0.99 | 0.95 | 0.95 | 0.93 | 0.94 |
GOOGLENET | 0.99 | 0.99 | 0.99 | 0.99 | 0.97 | 0.96 | 0.94 | 0.95 |
Number of Epochs | Training Accuracy | Training Loss | Validation Accuracy | Validation Loss |
---|---|---|---|---|
25 | 0.8965 | 0.3865 | 0.9025 | 0.2145 |
40 | 0.9251 | 0.2456 | 0.9365 | 0.156 |
50 | 0.9564 | 0.0952 | 0.9657 | 0.123 |
75 | 0.9765 | 0.1365 | 0.9898 | 0.1021 |
100 | 0.8678 | 0.5862 | 0.8742 | 0.4658 |
Number of Epochs | Learning Rate | Dropout Rate | Training Accuracy | Validation Accuracy | Training Loss | Validation Loss |
---|---|---|---|---|---|---|
25 | 0.001 | 0.25 | 0.9354 | 0.8624 | 0.4362 | 0.4521 |
50 | 0.0001 | 0.25 | 0.9264 | 0.8951 | 0.3561 | 0.3125 |
75 | 0.1 | 0.15 | 0.9021 | 0.8999 | 0.2531 | 0.2001 |
75 | 0.001 | 0.25 | 0.9985 | 0.9854 | 0.1254 | 0.1564 |
75 | 0.0001 | 0.40 | 0.8694 | 0.832 | 0.3214 | 0.2154 |
75 | 0.00001 | 0.50 | 0.8216 | 0.8021 | 0.2145 | 0.5641 |
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Vankara, J.; Nandini, S.S.; Muddada, M.K.; Kuppili, N.S.C.; Naidu, K.S. Plant Disease Prognosis Using Spatial-Exploitation-Based Deep-Learning Models. Eng. Proc. 2023, 59, 137. https://doi.org/10.3390/engproc2023059137
Vankara J, Nandini SS, Muddada MK, Kuppili NSC, Naidu KS. Plant Disease Prognosis Using Spatial-Exploitation-Based Deep-Learning Models. Engineering Proceedings. 2023; 59(1):137. https://doi.org/10.3390/engproc2023059137
Chicago/Turabian StyleVankara, Jayavani, Sekharamahanti S. Nandini, Murali Krishna Muddada, N. Satya Chitra Kuppili, and K Sowjanya Naidu. 2023. "Plant Disease Prognosis Using Spatial-Exploitation-Based Deep-Learning Models" Engineering Proceedings 59, no. 1: 137. https://doi.org/10.3390/engproc2023059137