Prediction of cotton FPAR and construction of defoliation spraying prescription map based on multi-source UAV images

Efficient and accurate spraying of cotton defoliant is a vital part of cotton production; the traditional way of spraying cotton defoliant will cause waste of pesticides and environmental pollution. The fraction of absorbed photosynthetically active radiation(FPAR) in cotton predicted by multi-source remote sensing information from unmanned aerial vehicles(UAVs) offers the possibility of precise control of cotton defoliant dosage. In this study, RGB and multispectral(MS) images of cotton at multiple fertility stages were collected by UAV. Six machine learning models (random forest regression(RFR), support vector regression(SVR), gradient-boosting decision tree(GBDT), extreme gradient boosting(XGBoost), K-nearest neighbor(k-NN), partial least squares regression(PLSR)) were constructed using spectral indices and texture features extracted from UAV remote sensing imagery and cotton FPAR measurements collected, and the performance of each model was evaluated. A spatial distribution map of cotton FPAR based on UAV imagery was constructed from cotton FPAR predicted by the machine learning models. The study results show that (i) image feature changes over multiple periods based on UAV multi-source remote sensing data are consistent with the evolution of cotton FPAR and can be used to predict cotton FPAR. (ii) Overall, texture features are more sensitive to FPAR than spectral indices, and the fusion of texture features with spectral indices increases the prediction accuracy of the model. (iii) The GBDT model based on UAV multi-source remote sensing data not only performs better on cotton FPAR prediction for a large sample dataset (whole fertility period), with R2 = 0.88 and rRMSE = 7.43 %, but also for a small sample dataset (single fertility period), with a range of R2 of 0.65–0.85 and a range of rRMSE of 7.41 %-19.09 %. In addition, a cotton defoliation spray prescription map was constructed based on the spatial distribution of cotton FPAR predicted by GBDT.