Xiang Gao Xuli Zan Shuai Yang Runda Zhang Shuaiming Chen Xiaodong Zhang Zhe Liu Yuntao Ma Yuanyuan Zhao Shaoming Li
Abstract
Context
The emergence rate and growth of maize seedlings are crucial for variety selection and farm managers; however, the complex planting environment and seedling morphological differences pose great challenges for seedling detection.
Objective
This study aims to rapidly quickly and accurately extract maize seedling information in the field environment based on UAV images with reduced labor cost.
Methods
In this paper, we proposed an automatic identification method for maize seedlings adapted to complex scenarios (different varieties and different seedling development stages) by fine-tuning the Mask R-CNN model. Aiming at the difficulty of obtaining the training data required by the deep learning algorithm, this paper proposes a semi-automatic labeling method for the deep learning sample data of maize seedlings. At last, we proposed a method to identify the locations of disrupted monopoly and extract seedling information such as coverage and seedling area uniformity, the mapping covers the whole experimental field.
Results and conclusions
This paper includes a discussion on the effect of real flight data and resampling data on model detection results. Indeed, the results show that the identification precision of the real flight data under the same resolution is lower than that of the resampled data. The detection precision of the model decreased as the spatial resolution decreased. To ensure AP@ 0.5IOU above 0.8, the minimum image spatial resolution is 2.1 cm. We finally selected a model which had the training data with a spatial resolution of 0.8 cm in 2019 and the average precision AP@0.5IOU was 0.887, the average accuracy of emergence rate monitoring in 2019 was 98.87 % and migration to 2020 is 95.70 %, 2021 is 98.77 %.
Significance
This work can quickly and effectively extract maize seedlings, and provide accurate seedling information, which can provide support for timely supplementation and subsequent seed selection.
Keywords
UAV; Precision agriculture; Emergence rate; Sample generation; Deep learning
Maize seedling information extraction from UAV images based on semi-automatic sample generation and Mask R-CNN model.pdf