Integrating Deep Learning, Grey Wolf Optimization, and SVM for Precise Plant Seedling Classification

Abstract

The agricultural sector, particularly in emerging economies like Africa, faces significant challenges in weed management, directly impacting yield, production costs, and crop quality. Accurate and early weed identification is pivotal for effective weed control strategies. In response, our research extends beyond conventional deep learning methodologies by integrating Convolutional Neural Networks (CNN) with Grey Wolf Optimization (GWO) and Support Vector Machine (SVM) for enhanced plant seedling classification. Leveraging a dataset of 5539 images across 12 plant species, including essential crops such as Common Wheat, Maize, and Sugar Beet, alongside nine weed types, we embarked on a comprehensive analysis employing four advanced CNN architectures: ResNet-50, Inception-V3, VGG-16, and EfficientNet-B0. Our approach involved initial model training and validation, followed by the application of GWO for feature optimization and SVM for refined classification. Post-optimization, the EfficientNet-B0 model emerged as the frontrunner, showcasing exemplary performance with a remarkable training accuracy of 99.82% and a test accuracy of 98.83%. These results highlight the efficacy of combining CNNs with evolutionary algorithms and machine-learning techniques in agricultural applications. This study illustrates the capabilities of CNNs in agricultural contexts and emphasizes the added value of optimization algorithms in improving model performance. The integration of GWO and SVM presents a significant advancement in plant seedling classification, offering a powerful tool for precision agriculture. Our findings hold great promise for enhancing crop management and yield in Africa and other emerging economies, contributing to the evolution of sustainable farming practices through innovative technological solutions.