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Improved lightweight identification of agricultural diseases based on MobileNetV3

arXiv 2207.11238 ↗First posted 19 Jul 2022cs.CV cs.AI cs.LGcited by 0

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Abstract (arXiv)

At present, the identification of agricultural pests and diseases has the problem that the model is not lightweight enough and difficult to apply. Based on MobileNetV3, this paper introduces the Coordinate Attention block. The parameters of MobileNetV3-large are reduced by 22%, the model size is reduced by 19.7%, and the accuracy is improved by 0.92%. The parameters of MobileNetV3-small are reduced by 23.4%, the model size is reduced by 18.3%, and the accuracy is increased by 0.40%. In addition, the improved MobileNetV3-small was migrated to Jetson Nano for testing. The accuracy increased by 2.48% to 98.31%, and the inference speed increased by 7.5%. It provides a reference for deploying the agricultural pest identification model to embedded devices.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 14 references that are in the map. First posted on arXiv 19 Jul 2022.

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References · 8

  1. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  2. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  3. 2017Squeeze-and-Excitation Networkscited by 4,789
  4. 2014Going Deeper with Convolutionscited by 4,241
  5. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  6. 2019Searching for MobileNetV3cited by 1,994
  7. 2021EfficientNetV2: Smaller Models and Faster Trainingcited by 1,135
  8. 2021Coordinate Attention for Efficient Mobile Network Designcited by 241

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