An Image Processing Approach for Weed Detection Using Deep Convolutional Neural Network
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8451-0_25
Reference22 articles.
1. Razfar N, True J, Bassiouny R, Venkatesh V, Kashef R (2022) Weed detection in soybean crops using custom lightweight deep learning models. J Agric Food Res 8:100308. https://doi.org/10.1016/j.jafr.2022.100308
2. Alrowais F, Asiri MM, Alabdan R, Marzouk R, Anwer, Hilal M, Alkhayyat A, Gupta D (2022) Hybrid leader based optimization with deep learning driven weed detection on internet of things enabled smart agriculture environment. Comput Electr Eng 104(Part A):108411, https://doi.org/10.1016/j.compeleceng.2022.108411
3. Badhan S, Desai K, Dsilva M, Sonkusare R, Weakey S (2021) Real-time weed detection using machine learning and stereo-vision. In: 2021 6th International conference for convergence in technology (I2CT). https://doi.org/10.1109/I2CT51068.2021.9417989
4. Bhaskar N, Ganashree TS (2022) Pulmonary nodule detection using Laplacian of Gaussian and deep convolutional neural network. In: Bhateja V, Satapathy SC, Travieso-Gonzalez CM, Adilakshmi T (eds) Smart intelligent computing and applications, vol 1. Smart innovation, systems and technologies, vol 282. Springer, Singapore. https://doi.org/10.1007/978-981-16-9669-5_58
5. Etienne A, Ahmad A, Aggarwal V, Saraswat D (2021) Deep learning-based object detection system for identifying weeds using UAS imagery. Remote Sens 13:5182. https://doi.org/10.3390/rs13245182,MDPIJournal
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