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Defoliation and accelerating ripening are important measures for cotton mechanization, and judging the time of defoliation and accelerating the ripening and harvest of cotton relies heavily on the boll opening rate, making it a crucial factor to consider. The traditional methods of cotton opening rate determination are time-consuming, labor-intensive, destructive, and not suitable for a wide range of applications. In this study, the relationship between the change rate of the vegetation index obtained by the unmanned aerial vehicle multi-spectrum and the ground boll opening rate was established to realize rapid non-destructive testing of the boll opening rate. The normalized difference vegetation index (NDVI) and green normalized difference vegetation index (GNDVI) had good prediction ability for the boll opening rate. NDVI in the training set had an R2 of 0.912 and rRMSE of 15.387%, and the validation set performance had an R2 of 0.929 and rRMSE of 13.414%. GNDVI in the training set had an R2 of 0.901 and rRMSE of 16.318%, and the validation set performance had an R2 of 0.909 and rRMSE of 15.225%. The accuracies of the models based on GNDVI and NDVI were within the acceptable range. In terms of predictive models, random forests achieve the highest accuracy in predictions. Accurately predicting the cotton boll opening rate can support decision-making for harvest and harvest aid spray timing, as well as provide technical support for crop growth monitoring and precision agriculture. [ABSTRACT FROM AUTHOR]

Grape is an economic crop of great importance and is widely cultivated in China. With the development of remote sensing, abundant data sources strongly guarantee that researchers can identify crop types and map their spatial distributions. However, to date, only a few studies have been conducted to identify vineyards using satellite image data. In this study, a vineyard is identified using satellite images, and a new approach is proposed that integrates the continuous wavelet transform (CWT) and a convolutional neural network (CNN). Specifically, the original time series of the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and green chlorophyll vegetation index (GCVI) are reconstructed by applying an iterated Savitzky-Golay (S-G) method to form a daily time series for a full year; then, the CWT is applied to three reconstructed time series to generate corresponding scalograms; and finally, CNN technology is used to identify vineyards based on the stacked scalograms. In addition to our approach, a traditional and common approach that uses a random forest (RF) to identify crop types based on multi-temporal images is selected as the control group. The experimental results demonstrated the following: (i) the proposed approach was comprehensively superior to the RF approach; it improved the overall accuracy by 9.87% (up to 89.66%); (ii) the CWT had a stable and effective influence on the reconstructed time series, and the scalograms fully represented the unique time-related frequency pattern of each of the planting conditions; and (iii) the convolution and max pooling processing of the CNN captured the unique and subtle distribution patterns of the scalograms to distinguish vineyards from other crops. Additionally, the proposed approach is considered as able to be applied to other practical scenarios, such as using time series data to identify crop types, map landcover/land use, and is recommended to be tested in future practical applications. [ABSTRACT FROM AUTHOR]