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As an important factor determining the competitiveness of raw cotton, cotton fiber quality has received more and more attention. The results of traditional detection methods are accurate, but the sampling cost is high and has a hysteresis, which makes it difficult to measure cotton fiber quality parameters in real time and at a large scale. The purpose of this study is to use time-series UAV (Unmanned Aerial Vehicle) multispectral and RGB remote sensing images combined with machine learning to model four main quality indicators of cotton fibers. A deep learning algorithm is used to identify and extract cotton boll pixels in remote sensing images and improve the accuracy of quantitative extraction of spectral features. In order to simplify the input parameters of the model, the stepwise sensitivity analysis method is used to eliminate redundant variables and obtain the optimal input feature set. The results of this study show that the R2 of the prediction model established by a neural network is improved by 29.67% compared with the model established by linear regression. When the spectral index is calculated after removing the soil pixels used for prediction, R2 is improved by 4.01% compared with the ordinary method. The prediction model can well predict the average length, uniformity index, and micronaire value of the upper half. R2 is 0.8250, 0.8014, and 0.7722, respectively. This study provides a method to predict the cotton fiber quality in a large area without manual sampling, which provides a new idea for variety breeding and commercial decision-making in the cotton industry. [ABSTRACT FROM AUTHOR]

The high-temporal-resolution monitoring of key management nodes in cotton management via agricultural remote sensing is vital for field cotton macro-statistics, particularly for predicting cotton production and obtaining comprehensive data. This study examines Shihezi, Xinjiang as a case study, utilizing Sentinel-1 and Sentinel-2 data from 2019 to 2021. Three machine learning models(RF, SVM, and CART) were employed to extract annual crop classification area rasters, monitor weekly cultivation progress, and monitor abandoned cropland during the cultivation period. The results demonstrate that the random forest model has produced satisfactory results in gridded extraction for cotton classification areas, achieving the producer's accuracy of the cotton category reached 98.5%, and the kappa coefficient is 0.947. Cotton cultivated in 2021 began is a week later than in 2020, yet exhibited a faster cultivate speed. The proportion of abandoned cotton fields in the study area rose in 2020 compared to 2019. The methodology presented in this study has a certain reference value for exploring the monitoring of continuous changes in crops over the years and macro-monitoring of important activities in the entire growth cycle. [ABSTRACT FROM AUTHOR]

Spider mites are important pests that cause severe economic damage to cotton. They feed on underside of leaves, piercing the chloroplast-containing cells, resulting in foliar damage and yield reduction. This paper proposed a two-stage classification approach for mite-infestation detection based on machine learning methods. Two cotton fields were selected for study, and the UAV imagery collection and concurrent ground investigation were conducted on July 20-21th, 2017. Mosaicking and geo-registration were performed on the collected multispectral imagery. Support Vector Machine (SVM) was used for scene classification, and a transferred Convolutional Neural Network (CNN) was applied for mite-infestation identification. Experimental results showed that our method outperformed others in terms of accuracy, which demonstrated that our approach has potential in mite-infestation detection using UAV multispectral imagery. [ABSTRACT FROM AUTHOR]

• 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]

• A field experiment of full-size buried pipelines under traffic loads is carried out. • A three-dimensional finite element model of traffic load considering pipe-soil nonlinearity is established. • The effects of diameter, wall thickness, internal pressure, buried depth and vehicle mass on the mechanical response of buried pipelines are investigated. • A PSO-SVR hybrid machine learning model is established to accurately predict the axial stress of buried pipelines. The acceleration of global urbanization and an increasing intersection of roads and buried pipelines pose a certain threat to the operation safety of pipelines. In this paper, the mechanical response of the X65 pipeline under empty-load, half-load, and full-load traffic loads is investigated via field experiments. The numerical model considering the nonlinear interaction between the pipe and the soil is established. Moreover, an accurate simulation of dynamic vehicle load is achieved by Dload user-defined subroutine. The error between the numerical calculation results and the field experiment results is less than 5 %. On this basis, the influence of five important factors, such as vehicle mass and buried depth, on the axial stress of pipelines is further explored. The results show that the traffic load leads to vertical bending deformation of the pipeline and local deformation of the wheel rolling position. The axial tensile stress generated at the bottom of the pipe is superimposed on the axial tensile stress generated by the internal pressure Poisson effect. The vehicle mass most significantly affects the axial stress of the pipeline, which is generally less than 100 MPa. Furthermore, the operating conditions of the pipeline with large coverage in service are calculated based on Python and ABAQUS co-simulation calculation of 1280 working conditions. The particle swarm optimization-support vector regression (PSO-SVR) hybrid machine learning model is used to accurately predict the axial stress of the buried pipeline under traffic load, and the corresponding correlation coefficient reaches 99.77 %. The research results reveal the mechanical response law of buried pipeline under traffic load, which can provide a basis for the applicability evaluation of buried pipeline in service under traffic load, and accurately evaluate the safety state of buried pipeline. [ABSTRACT FROM AUTHOR]