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Crop Guidance Photography Algorithm for Mobile Terminals

Author

Listed:
  • Yunsong Jia

    (College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China)

  • Qingxin Zhao

    (College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China)

  • Yi Xiong

    (College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China)

  • Xin Chen

    (College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China)

  • Xiang Li

    (College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China)

Abstract

The issues of inadequate digital proficiency among agricultural practitioners and the suboptimal image quality captured using mobile smart devices have been addressed by providing appropriate guidance to photographers to properly position their mobile devices during image capture. An application for crop guidance photography was developed, which involved classifying and identifying crops from various orientations and providing guidance prompts. Three steps were executed, including increasing sample randomness, model pruning, and knowledge distillation, to improve the MobileNet model for constructing a smartphone-based orientation detection model with high accuracy and low computational requirements. Subsequently, the application was realized by utilizing the classification results for guidance prompts. The test demonstrated that this method effectively and seamlessly guided agricultural practitioners in capturing high-quality crop images, providing effective photographic guidance for farmers.

Suggested Citation

  • Yunsong Jia & Qingxin Zhao & Yi Xiong & Xin Chen & Xiang Li, 2024. "Crop Guidance Photography Algorithm for Mobile Terminals," Agriculture, MDPI, vol. 14(2), pages 1-21, February.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:2:p:271-:d:1335222
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    References listed on IDEAS

    as
    1. Jinzhu Lu & Lijuan Tan & Huanyu Jiang, 2021. "Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification," Agriculture, MDPI, vol. 11(8), pages 1-18, July.
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