An Intelligent Classification Logic to Differentiate Crops and Weeds based on Learning assisted Image Processing Technique

Precision agriculture has become increasingly critical as the global demand for food production rises alongside the need for sustainable farming practices. This study presents an intelligent classification system that differentiates crops from weeds using a hybrid approach combining Artificial Neural Networks (ANN) with XGBoost. The proposed method leverages learning-assisted image processing to achieve robust feature extraction and precise classification. High-resolution images were acquired from multiple sources, including field cameras and drones, under diverse environmental conditions. Extensive preprocessing and augmentation techniques, such as noise reduction, normalization, rotation, scaling, and cropping, were applied to enhance image quality and dataset variability. Subsequent segmentation using region-based methods and morphological operations ensured the isolation of relevant plant features from the background. Both manual and ANN-based feature extraction techniques were employed to capture intrinsic characteristics of crops and weeds. The ANN module automatically extracted high-level features that were then passed to the XGBoost classifier, which fine-tuned the decision boundaries through gradient boosting. Experimental results indicate that the hybrid model achieved an overall classification accuracy ranging from 92% to 95%, with the best configuration reaching an accuracy of 93.5%, a precision of 94%, and a recall of 93%. These performance metrics demonstrate the system's potential for real-time field deployment in precision agriculture. The integration of deep learning with gradient boosting not only improved the model's discriminative ability but also ensured computational efficiency, making it viable for practical applications. This work contributes to sustainable agricultural practices by enabling precise weed management and targeted interventions, thereby reducing the need for extensive chemical treatments and enhancing crop yield.