Haq, S.I.U.; Tahir, M.N.; Lan, Y. Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI). Appl. Sci.2023, 13, 8840.
Haq, S.I.U.; Tahir, M.N.; Lan, Y. Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI). Appl. Sci. 2023, 13, 8840.
Haq, S.I.U.; Tahir, M.N.; Lan, Y. Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI). Appl. Sci.2023, 13, 8840.
Haq, S.I.U.; Tahir, M.N.; Lan, Y. Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI). Appl. Sci. 2023, 13, 8840.
Abstract
This study evaluated the using of machine vision in combination with deep learning to identify weeds in real-time for wheat production system. PMAS-Arid Agriculture university research farm were selected for collection of images (6000 total images) of weeds and wheat crops under different weather condition. During growing season, the database was constructed to identify the weeds. For this study two framework were used TensorFlow and PyTorch under CNNs and Deep learning. Deep learning perfromed better with in PyTourch value as compared to another model in Tensorflow. comparing with other networks such as YOLOv4, we concluded that our network reaches a better result between speed and accuracy. Specifically, the maximum precision of weed and wheat plants were 0.89 and 0.91 respectively with 9.43 ms and 12.38 ms inference time per image (640 × 640) NVIDIA RTX2070 GPU.
Keywords
deep learning; Machine Learning; Artificial Intelligence
Subject
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.
