Automating Crop Identification: A Deep Learning Approach Using ResNet50V2 in Agriculture

Aiming at automating the identification of different crop kinds from images, this research study offers an agricultural crop classification model utilizing the ResNet50V2 deep learning architecture. Precision agriculture's developments depend much on accurate crop classification to monitor and control agricultural activities. This work trains the model using a dataset of images of thirty various cropping. Model generalization is improved by means of rescaling, rotation, and horizontal flipping among data augmentation methods. The efficacy of the model is assessed using performance measures and achieved an accuracy of 0.96, a confusion matrix is utilized to show classification results. Particularly for crops like "Lemon," "Cotton," and "Soyabean," the model shows great classification accuracy. But because of their apparent resemblance, some crops like "Cherry" and "Cardamom" create classification problems. With further improvements suggested to improve performance for demanding classes, the results show the model's capacity to support precision agriculture by allowing automated crop identification. This work emphasizes how well deep learning models might transform agricultural resource management and crop monitoring.