Recognition of sunflower growth period based on deep learning from UAV remote sensing images

Accurate determination of crops growth period plays an important role in field management and agricultural decision-making. The current work mostly extracts the crop normalized difference vegetation index curve from multi-temporal data and identifies the crop phenology based on its trend or special nodes. However, these time-series-based identification methods are difficult to be applied to practically crop monitoring tasks. In this paper, the unmanned aerial vehicle remote sensing platform is used to collect the multi-spectral images of the experimental field and identify the sunflower growth period based on the different population features during its different growth periods. According to the actual field management needs, this study obtains the plot-level sunflower growth period result by analyzing statistically the distribution area of different sunflower periods in a field plot. This study uses the data of 2018 in the study area to build the model and test its performance on the data of 2019. Through comparative experiments, PSPNet can achieve a good balance between accuracy and efficiency in this study. Further, given to time-series relationship between the adjacent growth periods classification, this paper proposes an improved loss function to weight different types of misclassification to optimize model performance. The results show that improved PSPNet with proposed weighted loss function achieves the optimal recognition accuracy of 89.01%, which provides a solution for sunflower growth period recognition based on the single-phase data.