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An Organic Weed Control Prototype using Directed Energy and Deep Learning

arXiv 2405.21056 ↗First posted 31 May 2024cs.RO cs.AI cs.CVcited by 0

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Abstract (arXiv)

Organic weed control is a vital to improve crop yield with a sustainable approach. In this work, a directed energy weed control robot prototype specifically designed for organic farms is proposed. The robot uses a novel distributed array robot (DAR) unit for weed treatment. Soybean and corn databases are built to train deep learning neural nets to perform weed recognition. The initial deep learning neural nets show a high performance in classifying crops. The robot uses a patented directed energy plant eradication recipe that is completely organic and UV-C free, with no chemical damage or physical disturbance to the soil. The deep learning can classify 8 common weed species in a soybean field under natural environment with up to 98% accuracy.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 46 references that are in the map. First posted on arXiv 31 May 2024.

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15 of 46 references.

References · 15

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Densely Connected Convolutional Networkscited by 6,691
  3. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  4. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  5. 2014Going Deeper with Convolutionscited by 4,231
  6. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  7. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,320
  8. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,509
  9. 2017ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devicescited by 1,194
  10. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,097
  11. 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,054
  12. 2016Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learningcited by 209
3 more
  1. 2018Deep learning in agriculture: A surveycited by 160
  2. 2018DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learningcited by 69
  3. 2017Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNscited by 55

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