Abstract:
The increasing use of convolutional neural networks (CNNs) has brought about a significant transformation in numerous fields, such as image categorization and identification. In the
development of a CNN model to classify images of sericea lespedeza [SL; Lespedeza cuneata (Dum-Cours) G. Don] from weed images, four architectures were explored: CNN model variant 1, CNN
model variant 2, the Visual Geometry Group (VGG16) model, and ResNet50. CNN model variant 1
(batch normalization with adjusted dropout method) demonstrated 100% validation accuracy, while
variant 2 (RMSprop optimization with adjusted learning rate) achieved 90.78% validation accuracy.
Pre-trained models, like VGG16 and ResNet50, were also analyzed. In contrast, ResNet50’s steady
learning pattern indicated the potential for better generalization. A detailed evaluation of these
models revealed that variant 1 achieved a perfect score in precision, recall, and F1 score, indicating
superior optimization and feature utilization. Variant 2 presented a balanced performance, with
metrics between 86% and 93%. VGG16 mirrored the behavior of variant 2, both maintaining around
90% accuracy. In contrast, ResNet50’s results revealed a conservative approach for class 0 predictions.
Overall, variant 1 stood out in performance, while both variant 2 and VGG16 showed balanced results.
The reliability of CNN model variant 1 was highlighted by the significant accuracy percentages,
suggesting potential for practical implementation in agriculture. In addition to the above, a smart-
phone application for the identification of SL in a field-based trial showed promising results with an
accuracy of 98–99%. The conclusion from the above is that a CNN model with batch normalization
has the potential to play a crucial role in the future in redefining and optimizing the management of
undesirable vegetation.
