Search

• A method to create a defoliation spray map using predictive FPAR was devised. • GBDT is the best model for estimating the FPAR of cotton. • Fusion of multi-source remote sensing data improves model prediction accuracy. • Textural features outperform spectral indices in estimating cotton's FPAR. 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. [ABSTRACT FROM AUTHOR]

In recent times, Wireless Sensor Network (WSN) technology is widely applied in various agricultural applications. However, when a WSN becomes large and complex, it is difficult to maintain and adjust the whole WSN or multiple WSNs manually. Therefore, we propose an UAV-based scheme for automatic delivery and recovery of WSN nodes in the field. This scheme includes a combination of specially designed UAV, node platforms together with a GNSS-RTK (Global Navigation Satellite System, Real – Time Kinematic) system. A prototype system was built to test the UAV-based scheme. The results and analyses of indoor and outdoor experiments demonstrated the feasibility and efficacy of the scheme. [ABSTRACT FROM AUTHOR]

• Established a relationship between UASS spray volume and cotton canopy structure. • The model's result was verified by the biological efficacy of cotton defoliation. • It is feasible to determine the spray volume of UASS by remote sensing images. The Unmanned aerial spraying systems (UASS), with a precise positioning system and convenient control system, have attracted more attention from researchers and the market. However, the UASS operation parameter setting still depends on the operator, and there is still a gap between the intelligent spraying. The upgrade of UASS from automation to intelligence requires a "scientific brain" to make spraying decisions. In this study, the UASS equipped with centrifugal nozzles was used to simulate defoliant spraying, combined with RGB and multi-spectral cameras to collect remote sensing images of the target area. The droplet distribution data were obtained through two years of field trials in two places. A droplet distribution prediction model based on the spray volume of UASS and the remote sensing spectral index that characterizes the cotton canopy structure is established by the BP neural network and Bayesian regularization training algorithm. The cotton defoliant was verified using this model and combined with NY/T 3213–2018 standard. The research results show that it is feasible to use remote sensing images to determine the application volume of UASSs for cotton defoliant. Compared with the conventional application rate and overspray, the defoliant spray based on the decision model can achieve the expected cotton defoliation effect and reduce the application volume. [ABSTRACT FROM AUTHOR]