Application of unmanned aircraft remote sensing image processing method based on artificial intelligence algorithm in corn growth assessment

Abstract

This paper proposes a method based on UAV low-altitude photogrammetry and deep learning algorithms for corn crop growth monitoring. During the shooting process, a unified UAV photogrammetry strategy is set to ensure that the obtained images have high spatial resolution, and after pre-processing the original images, a convolutional neural network (CNN) model is utilized to extract features from the images and improve the accuracy of the CNN with the help of the idea of transfer learning. In addition, multi-scale feature fusion and attention mechanism are introduced to allow the model to focus on important location information, and weighted multi-task loss function is used to jointly optimize the multi-objective values such as plant height, leaf area index, and biomass. Experiments show that the method has good real-time performance and scalability while maintaining high prediction accuracy, providing an effective solution for crop monitoring in precision agriculture.

Plant phenotyping relevance

UAV画像と深層学習を用いてトウモロコシの草丈、葉面積指数、バイオマスを推定する手法自体が研究の中心であり、植物形質推定の方法開発・応用に該当する。

Code and data availability

The article describes UAV multispectral image acquisition and a CNN-based maize growth assessment model, but contains no data availability statement, no public dataset or code repository, no trained model release, and no author-provided access URL. The 400GB of imagery and the model remain unpublished; no qualifying or

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