Version 1
: Received: 13 April 2024 / Approved: 15 April 2024 / Online: 16 April 2024 (10:52:19 CEST)
How to cite:
Siddique, A.; Cook, K.; Holt, Y.; Panda, S.; Mahapatra, A.K.; MORGAN, E.R.; Van Wyk, J.A.; Terrill, T.H. From Plants to Pixels: The Role of Artificial Intelligence in Identifying Sericea Lespedeza. Preprints2024, 2024041002. https://doi.org/10.20944/preprints202404.1002.v1
Siddique, A.; Cook, K.; Holt, Y.; Panda, S.; Mahapatra, A.K.; MORGAN, E.R.; Van Wyk, J.A.; Terrill, T.H. From Plants to Pixels: The Role of Artificial Intelligence in Identifying Sericea Lespedeza. Preprints 2024, 2024041002. https://doi.org/10.20944/preprints202404.1002.v1
Siddique, A.; Cook, K.; Holt, Y.; Panda, S.; Mahapatra, A.K.; MORGAN, E.R.; Van Wyk, J.A.; Terrill, T.H. From Plants to Pixels: The Role of Artificial Intelligence in Identifying Sericea Lespedeza. Preprints2024, 2024041002. https://doi.org/10.20944/preprints202404.1002.v1
APA Style
Siddique, A., Cook, K., Holt, Y., Panda, S., Mahapatra, A.K., MORGAN, E.R., Van Wyk, J.A., & Terrill, T.H. (2024). From Plants to Pixels: The Role of Artificial Intelligence in Identifying Sericea Lespedeza. Preprints. https://doi.org/10.20944/preprints202404.1002.v1
Chicago/Turabian Style
Siddique, A., Jan A. Van Wyk and Thomas H Terrill. 2024 "From Plants to Pixels: The Role of Artificial Intelligence in Identifying Sericea Lespedeza" Preprints. https://doi.org/10.20944/preprints202404.1002.v1
Abstract
The increasing use of Convolutional Neural Networks (CNN) has brought about a significant transformation in numerous fields, such as image categorization and identification. In the development of a CNN model for classifying images of sericea lespedeza (SL; Lespedeza cuneata) from weed images, four architectures were explored: CNN-Model Variant 1, CNN-Model Variant 2, VGG16, and ResNet50. The CNN-Model Variant 1 demonstrated 100 % validation accuracy, while Variant 2 achieved 90.78% validation accuracy. Pre-trained models, like VGG16 and ResNet50, were also analyzed. In contrast, ResNet50's steady learning pattern indicated 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%. The VGG16 mirrored the behavior of Variant 2, both maintaining around 90% accuracy, but 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 is highlighted by the significant accuracy percentages, which demonstrate its potential for practical implementation in agriculture. Smartphone application for the identification of SL in a field-based trial has shown promising results with an accuracy of 98%-99%. Using a CNN model with batch normalization has the potential to play a crucial role in redefining and optimizing the management of undesirable vegetation in the future.
Biology and Life Sciences, Agricultural Science and Agronomy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.