Abstract
A rapid and precise large-scale agricultural disaster survey is a basis for agricultural disaster relief and insurance but is labor-intensive and time-consuming. This study applies Unmanned Aerial Vehicles (UAVs) images through deep-learning image processing to estimate the rice lodging in paddies over a large area. This study establishes an image semantic segmentation model employing two neural network architectures, FCN-AlexNet, and SegNet, whose effects are explored in the interpretation of various object sizes and computation efficiency. Commercial UAVs imaging rice paddies in high-resolution visible images are used to calculate three vegetation indicators to improve the applicability of visible images. The proposed model was trained and tested on a set of UAV images in 2017 and was validated on a set of UAV images in 2019. For the identification of rice lodging on the 2017 UAV images, the F1-score reaches 0.80 and 0.79 for FCN-AlexNet and SegNet, respectively. The F1-score of FCN-AlexNet using RGB + ExGR combination also reaches 0.78 in the 2019 images for validation. The proposed model adopting semantic segmentation networks is proven to have better efficiency, approximately 10 to 15 times faster, and a lower misinterpretation rate than that of the maximum likelihood method.
Highlights
Typhoon-associated strong winds and heavy rains frequently cause a considerable amount of crop damages that negatively impacted farmers’ incomes and crop price balance on the agricultural market
For the rice lodging category, the highest validation accuracy reaches to 80.08% and 75.37% in fully convolutional network (FCN)-AlexNet using RGB+ExGR and SegNet using RGB+Excess Green index (ExG), respectively
The rice lodging assessment still heavily relies on manual objective evaluation, which is time-consuming, labor-intensive, and problematic in terms of its poor efficiency and objectivity
Summary
Typhoon-associated strong winds and heavy rains frequently cause a considerable amount of crop damages that negatively impacted farmers’ incomes and crop price balance on the agricultural market. Based on the Taiwan council of agriculture (COA) agriculture statistics [1,2,3,4,5], the average annual crop damage cost US$352,482 in the past five years (2014–2018). The Taiwan government established the Implementation Rules of Agricultural Natural Disaster Relief for more than 30 years and has been trying to implement agricultural insurances recently. Farmers’ incomes can be partially compensated by the emergency allowances based on the relief rules. Limitations such as shortage of disaster relief funds, high administrative costs, and crop damage assessment disputes with the ongoing disaster relief rule urgently demand improvements
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