Skip to main content

ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Plant Bioinformatics
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1502863
This article is part of the Research Topic Recent Advances in Big Data, Machine, and Deep Learning for Precision Agriculture, Volume II View all 10 articles

Monitoring of agricultural progress in rice-wheat rotation area based on UAV RGB images

Provisionally accepted
Jianliang Wang Jianliang Wang 1*Chen Chen Chen Chen 2Senpeng Huang Senpeng Huang 1Hui Wang Hui Wang 3Yuanyuan Zhao Yuanyuan Zhao 1Jiacheng Wang Jiacheng Wang 1Yao Zhaosheng Yao Zhaosheng 1Chengming Sun Chengming Sun 1Liu Tao Liu Tao 1
  • 1 College of Agriculture, Yangzhou University, Yangzhou, China
  • 2 Zhenjiang Agricultural Science Research Institute of Jiangsu Hilly Area, Zhenjiang, China
  • 3 Lixiahe Agricultural Institute of Jiangsu Province, Yangzhou, Jiangsu Province, China

The final, formatted version of the article will be published soon.

    Real-time monitoring of rice-wheat rotation areas is crucial for improving agricultural productivity and ensuring the overall yield of rice and wheat. However, the current monitoring methods mainly rely on manual recording and observation, leading to low monitoring efficiency. This study addresses the challenges of monitoring agricultural progress and the time-consuming and labor-intensive nature of the monitoring process. By integrating Unmanned aerial vehicle (UAV) image analysis technology and deep learning techniques, we proposed a method for precise monitoring of agricultural progress in rice-wheat rotation areas. The proposed method was initially used to extract color, texture, and convolutional features from RGB images for model construction. Then, redundant features were removed through feature correlation analysis. Additionally, activation layer features suitable for agricultural progress classification were proposed using the deep learning framework, enhancing classification accuracy. The results showed that the classification accuracies obtained by combining Color+Texture, Color+L08CON, Color+ResNet50, and Color+Texture+L08CON with the random forest model were 0.91, 0.99, 0.98, and 0.99, respectively. In contrast, the model using only color features had an accuracy of 85.3%, which is significantly lower than that of the multi-feature combination models. Color feature extraction took the shortest processing time (0.19 s) for a single image. The proposed Color+L08CON method achieved high accuracy with a processing time of 1.25 s, much faster than directly using deep learning models. This method effectively meets the need for real-time monitoring of agricultural progress.

    Keywords: UAV image, Agricultural progress, deep learning, Rice-wheat rotation, Classification

    Received: 27 Sep 2024; Accepted: 17 Dec 2024.

    Copyright: © 2024 Wang, Chen, Huang, Wang, Zhao, Wang, Zhaosheng, Sun and Tao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Jianliang Wang, College of Agriculture, Yangzhou University, Yangzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.