Performance Analysis of Detection of Crop Diseases Using Basic Color and Shape Features
Implementation plan:
Step 1: Initially, We collect and load the data from an “tomato leaf disease detection dataset”
Step 2: Then, we pre-process the collected data using median filtering, color space conversion, and normalization technique.
Step 3: Next we implement the image segmentation process using “Simple Linear Iterative Clustering (SLIC)” technique .
Step 4: Next, We perform feature extraction by fusing the Histogram of Oriented Gradients (HOG) with the Ant Whale Optimization Technique (AWOT).
Step 5: Next, We classify the image using the Light Gradient Boosting Machine (LightGBM) technique.
Step 6: Finally, we plot graph for the following metrics:
6.1: No. of epochs vs Accuracy (%)
6.2: No. of epochs vs Precision (%)
6.3: No. of epochs vs Recall (%)
6.4: No. of epochs vs F1-score (%)
Software Requirements:
1. Development Tool: Python 3.11.4 or above
2. Operating System: Windows-10(64-bit) or above
Dataset:
Link: https://www.kaggle.com/datasets/kaustubhb999/tomatoleaf
Note :-
1) If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.
2) Please note that this implementation plan does not include any further steps after it is put into implementation.
3) If the plan satisfies your requirement, Please confirm with us.
4) Project based on Simulation only, not a real time project.
5) Please understand that any modifications made to the confirmed implementation plan will not be made before or after the project development.
We perform with an Existing Reference 2: Title: “Image-based Plant Diseases Detection using Deep Learning”
