Enhancing Cotton Crop Health: A Data-Driven Approach for Disease Detection and Yield Optimization Through Tuned VGG-16 Model
Keywords:
Cash Crop, Dropout Rate, Feature Extraction, Learning Rate, Optimizer, Tuning Parameters, Variability, VGG-16Abstract
Cotton is a valuable cash crop. Timely disease detection and management can help increase crop yields and overall agricultural productivity. Healthy crops produce higher-quality cotton fibers, which are essential for the textile industry. Maintaining a healthy cotton crop contributes to food security and economic stability, especially in areas where cotton is a primary source of income. Traditional image processing techniques extract relevant features from the segmented leaf images. These features can include color histograms, texture descriptors, shape characteristics, and more. Feature extraction helps capture the distinctive patterns associated with healthy and diseased leaves. Cotton diseases can manifest in various ways, and their visual symptoms can vary based on factors such as disease stage, environmental conditions, and cotton variety. This variability can make it challenging to develop a one-size-fits-all image processing solution. The proposed model tunes the VGG-16 to perform the feature extraction and solves the problem of Variability in Disease Symptoms. Total 8 parameters are available for tuning the VGG-16 but the proposed model focuses on the learning rate, dropout rate and optimizer. These hyperparameters significantly impact the model's performance, convergence speed, and generalization ability. Without tuning the model has got 82.18% accuracy but after tuning the model has got 92.01%, which means that nearly 10% improvement in the designed process.
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