Smart agriculture is increasingly recognized as a vital approach to addressing the challenges of modern farming, including the need for improved yield prediction and efficient resource management. A cornerstone of this innovation is the precise classification of crops, which directly impacts decision-making and resource allocation. In this study, we investigate the effectiveness of several state-of-the-art deep learning models—VGGNet, Sequential, Artificial Neural Network (ANN), and ResNet50—for multiclass crop classification. A diverse and extensive crop image dataset was employed to ensure a comprehensive evaluation. The methodology incorporates rigorous preprocessing steps to enhance data quality, followed by meticulous model training and validation to achieve reliable outcomes. Comparative analysis of these models reveals their relative strengths and limitations, providing insights into their applicability in different agricultural scenarios. The findings emphasize the transformative potential of deep learning in agriculture, enabling precise crop identification and early detection of health issues, thus supporting smarter, more sustainable farming practices. This work lays a foundation for integrating advanced AI solutions into agriculture, paving the way for increased efficiency and resilience in crop management systems.
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