TY - GEN
T1 - Weed Localization
T2 - 3rd International Conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023
AU - Shekhawat, Neha
AU - Verma, Seema
AU - Juwono, F. H.
AU - Wei, Wong Kitt
AU - Apriono, Catur
AU - Gde Dharma Nugraha, I.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - Unwanted plants called weeds challenges the crops for nutrients, water, sunshine, and space in agricultural areas, which lowers crop quality and yield. Farmers have used a variety of techniques and tools to get rid of weeds for millennia. Herbicides are currently being used by farmers to manage weeds, but they are negatively affecting agricultural productivity. Farmers seek to use fewer herbicides to boost crop productivity. To overcome this, precision agriculture is used. To effectively identify weeds in rice fields, unmanned aerial vehicle (UAV) system-based imaging has been used. The usage of UAV and deep learning models has provided effective results in weed detection in the fields. Therefore, in this work, the performance of the UNet model and modified UNet using transfer learning models are analysed to detect weeds using UAV images collected from a rice crop field. U-Net, UNet-VGG16, UNet-VGG19, and UNet-ResNet50 have been compared with the different batch sizes i.e. 16, 32, and 64. Among this UNet-VGG19 95.51% with a batch size of 16 outperformed. Precision, Recall, and F1-score were also considered to analyze the results.
AB - Unwanted plants called weeds challenges the crops for nutrients, water, sunshine, and space in agricultural areas, which lowers crop quality and yield. Farmers have used a variety of techniques and tools to get rid of weeds for millennia. Herbicides are currently being used by farmers to manage weeds, but they are negatively affecting agricultural productivity. Farmers seek to use fewer herbicides to boost crop productivity. To overcome this, precision agriculture is used. To effectively identify weeds in rice fields, unmanned aerial vehicle (UAV) system-based imaging has been used. The usage of UAV and deep learning models has provided effective results in weed detection in the fields. Therefore, in this work, the performance of the UNet model and modified UNet using transfer learning models are analysed to detect weeds using UAV images collected from a rice crop field. U-Net, UNet-VGG16, UNet-VGG19, and UNet-ResNet50 have been compared with the different batch sizes i.e. 16, 32, and 64. Among this UNet-VGG19 95.51% with a batch size of 16 outperformed. Precision, Recall, and F1-score were also considered to analyze the results.
KW - U-Net, UNet-VGG16, UNet-VGG19 and UNet-ResNet50
KW - Unmanned aerial vehicle (UAV)
KW - Weed detection
UR - http://www.scopus.com/inward/record.url?scp=85205343273&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-2508-3_1
DO - 10.1007/978-981-97-2508-3_1
M3 - Conference contribution
AN - SCOPUS:85205343273
SN - 9789819725076
T3 - Lecture Notes in Electrical Engineering
SP - 1
EP - 14
BT - Advances in Artificial-Business Analytics and Quantum Machine Learning - Select Proceedings of the 3rd International Conference, Com-IT-Con 2023
A2 - Santosh, K.C.
A2 - Sood, Sandeep Kumar
A2 - Pandey, Hari Mohan
A2 - Virmani, Charu
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 14 July 2023 through 15 July 2023
ER -
