Performance Analysis of Crop Disease Detection and Monitoring
Implementation plan:
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Step 1: Initially, We collect and load the data from “New Plant Diseases Dataset”
Step 2: Then, we pre-process the collected data using Gaussian Filter technique for image normalization.
Step 3: Next we augment the data using Conditional Generative Adversarial Network (cGAN) to recognize the disease.
Step 4: Next, We select the features using Mutual Information with Salp Swarm Optimization Algorithm (MI-SSOA).
Step 5: Next, We extract and optimize the selected features using Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP).
Step 6: Next, we implement MobileNetV3 and Swim Transformer (MNV3-ST) hybrid feature fusion techniques to efficiently extract low-level spatial features.
Step 7: NeXT, we implement InceptionV3, Orthogonal Learning Particle Swarm Optimization, and You Only Look Once version 3 (IV3-OLPSO-YOLOv3) for disease detection and monitoring its stage.
Step 8: Finally, we plot graph for the following metrics:
8.1: Number of epochs vs Accuracy (%)
8.2: Number of epochs vs Precision (%)
8.3: Number of epochs vs Recall (%)
8.4: Number of epochs vs F1-score (%)
8.5: Number of Epochs vs. Loss (%)
8.6: Number of Epochs vs. True positive Rate (%)
8.7: Number of Epochs vs. False positive Rate (%)
8.8: Confusion Matrix
Software Requirements:
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1. Development Tool: Python 3.12.9
2. Operating System: Windows-11 (64-bit)
Dataset:
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Link: https://www.kaggle.com/datasets/jeyaprathapp/new-plant-disease-dataset
Note :-
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1) If the plan does not meet your requirements, provide detailed steps, parameters, models, or expected results in advance. Once implemented, changes won’t be possible without prior input; otherwise, we’ll proceed as per our implementation plan.
2) If the plan satisfies your requirement, Please confirm with us.
3) Project based on Simulation only, not a real time project.
4) Please understand that any modifications made to the confirmed implementation plan will not be made after the project development.
We perform with an Existing Reference 3: Title:- Artificial Driving based Efficient Net for Automatic Plant Leaf Disease Classification
