Ensemble Method Used for Weed Classification in Cotton Field

In the realm of agricultural technology, the classification of weeds in cotton fields holds significant importance for enhancing crop yield and reducing manual labor. This paper proposes an ensemble method leveraging Artificial Intelligence and Machine Learning (AIML) techniques for efficient weed classification in cotton fields. The ensemble method combines multiple machine learning algorithms to create a robust classification model capable of accurately identifying various weed species amidst cotton plants. Key steps involve data collection from cotton fields, preprocessing techniques to enhance data quality, feature extraction to identify relevant characteristics, and model training using ensemble learning algorithms such as Google net, Alex net. The proposed approach aims to overcome the limitations of individual algorithms by leveraging their collective intelligence to improve classification accuracy and reliability. Experimental results demonstrate the effectiveness of the ensemble method in accurately differentiating between different weed species, thereby aiding farmers in implementing targeted weed control strategies and optimizing crop management practices. Overall, this study showcases the potential of AIML-based ensemble methods in addressing agricultural challenges and fostering sustainable farming practices