Empirical Regression Model Using Ndvi, Meteorological Factors For Estimation Of Wheat Yield In Yunnan, China

Crop yield estimation is of great importance to food security. NDVI, as an effective crop monitoring tool, is extensively used in crop yield estimation. However there are few studies conducted in the regions where mixed crops are grown. In this study, a statistical approach for crop area identification is proposed and applied to wheat in Jianshui County in the Nanpan River Basin, Yunnan Province of China. Based on the correlation analysis between MODIS NDVI data and crop yield, the planting areas are identified, as well as the best periods for a reliable estimation. Regression models are presented to predict the crop yield with the retrieved NDVI from the corresponding crop planting-areas. Besides, the crop yield is also strongly influenced by meteorological factors, such as precipitation, temperature and potential evapotranspiration data. Therefore, new regression model by adding those factors is presented and compared with the former one. This study has proposed a simple and convenient method on crop yield estimation using meteorological factors and NDVI data in small regions where crop type is unknown exactly

FlowFormer: A Transformer Architecture and Its Masked Cost Volume Autoencoding for Optical Flow

This paper introduces a novel transformer-based network architecture, FlowFormer, along with the Masked Cost Volume AutoEncoding (MCVA) for pretraining it to tackle the problem of optical flow estimation. FlowFormer tokenizes the 4D cost-volume built from the source-target image pair and iteratively refines flow estimation with a cost-volume encoder-decoder architecture. The cost-volume encoder derives a cost memory with alternate-group transformer~(AGT) layers in a latent space and the decoder recurrently decodes flow from the cost memory with dynamic positional cost queries. On the Sintel benchmark, FlowFormer architecture achieves 1.16 and 2.09 average end-point-error~(AEPE) on the clean and final pass, a 16.5\% and 15.5\% error reduction from the GMA~(1.388 and 2.47). MCVA enhances FlowFormer by pretraining the cost-volume encoder with a masked autoencoding scheme, which further unleashes the capability of FlowFormer with unlabeled data. This is especially critical in optical flow estimation because ground truth flows are more expensive to acquire than labels in other vision tasks. MCVA improves FlowFormer all-sided and FlowFormer+MCVA ranks 1st among all published methods on both Sintel and KITTI-2015 benchmarks and achieves the best generalization performance. Specifically, FlowFormer+MCVA achieves 1.07 and 1.94 AEPE on the Sintel benchmark, leading to 7.76\% and 7.18\% error reductions from FlowFormer.Comment: arXiv admin note: substantial text overlap with arXiv:2203.16194, arXiv:2303.0123

Thermal Properties and Biodegradability Studies of Poly(3-hydroxybutyrate-co-3-hydroxyvalerate)

For investigating the relationship between thermal properties and biodegradability of poly (3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV), several films of PHBV containing different polyhydroxyvalerate (HV) fractions were subjected to degradation in different conditions for up to 49 days. Differential scanning calorimetry (DSC), thermogravimetry (TG), specimen weight loss and scanning electron microscopy (SEM) were performed to characterize the thermal properties and enzymatic biodegradability of PHBV. The experimental results suggest that the degradation rates of PHBV films increase with decreasing crystallinity; the degradability of PHBV occurring from the surface is very significant under enzymatic hydrolysis; the crystallinity of PHBV decreased with the increase of HV fraction in PHBV; and no decrease in molecular weight was observed in the partially-degraded polymer.Ningbo Natural Science Foundation (Grant 2006A610043)Science and Technology Department of Zhejiang Provincial Government (Grant 2007R10020