Leaf area index (LAI) is one of the indicators measuring the growth of rice in the field. LAI monitoring plays an important role in ensuring the stable increase of grain yield. In this study, the canopy reflectance spectrum of rice was obtained by ASD at the elongation, booting, heading and post-flowering stages of rice, and the correlations between the original reflectance (OR), first-derivative transformation (FD), reciprocal transformation (1/R), and logarithmic transformation (LOG) with LAI were analyzed. Characteristic bands of spectral data were then selected based on the successive projections algorithm (SPA) and Pearson correlation. Moreover, ridge regression (RR), partial least squares (PLS), and multivariate stepwise regression (MSR) were conducted to establish estimation models based on characteristic bands and vegetation indices. The research results showed that the correlation between canopy spectrum and LAI was significantly improved after FD transformation. Modeling using SPA to select FD characteristic bands performed better than using Pearson correlation. The optimal modeling combination was FD-SPA-VI-RR, with the coefficient of determination (R²) of 0.807 and the root-mean-square error (RMSE) of 0.794 for the training set, R² of 0.878 and RMSE of 0.773 for the validation set 1, and R² of 0.705 and RMSE of 1.026 for the validation set 2. The results indicated that the present model may predict the rice LAI accurately, meeting the requirements of large-scale statistical monitoring of rice growth indicators in the field.

This paper presents a fault-tolerant flight control method for a multi-rotor UAV under actuator failure and external wind disturbances. The control method is based on an active disturbance rejection controller (ADRC) and spatio-temporal radial basis function neural networks, which can be used to achieve the stable control of the system when the parameters of the UAV mathematical model change. Firstly, an active disturbance rejection controller with an optimized parameter design is designed for rthe obust control of a multi-rotor vehicle. Secondly, a spatio-temporal radial basis function neural network with a new adaptive kernel is designed. In addition, the output of the novel radial basis function neural network is used to estimate fusion parameters containing actuator faults and model uncertainties and, consequently, to design an active fault-tolerant controller for a multi-rotor vehicle. Finally, fault injection experiments are carried out with the Qball-X4 quadrotor UAV as a specific research object, and the experimental results show the effectiveness of the proposed self-tolerant, fault-tolerant control method.

The quadrotor drone is small in size and light in weight, the mechanical structure is simple, and the requirements for the working environment are low. The development of a quadrotor UAV technology is also the focus of the current technical personnel. The author proposes a four-rotor UAV sensor fault diagnosis and fault-tolerant control based on genetic algorithm and introduces common sensor failures, and based on the improved BP neural network of the GA algorithm, the genetic algorithm is improved. This paper uses a classical BP algorithm, a classical GA-BP algorithm, and an improved GA-BP algorithm for training. Using a total of 150 sets of training data and training function using LevenbregMarquardt (trainlm), MeanSquaredError (performance function using mse), in the same noise background, the improved GA-BP algorithm has the highest detection rate, the classical GA-BP algorithm followed, and classical BP algorithm is the worst. Therefore, using the improved GA-BP algorithm, various errors of the sensor can be detected quickly and accurately.

Yield is an important indicator in evaluating rice planting, and it is the collective result of various factors over multiple growth stages. To achieve a large-scale accurate prediction of rice yield, based on yield estimation models using a single growth stage and conventional spectral transformation methods, this study introduced the continuous wavelet transform algorithm and constructed models under the premise of combined multiple growth stages. In this study, canopy reflectance spectra at four important stages of rice elongation, heading, flowering and milky were selected, and then, a rice yield estimation model was constructed by combining vegetation index, first derivative and wavelet transform based on random forest algorithm or multiple stepwise regression. This study found that the combination of multiple growth stages significantly improved the model accuracy. In addition, after two validations, the optimal model combination for rice yield estimation is first derivative-wavelet transform-vegetation index-random forest model based on four growth stages, with the coefficient of determination (R²) of 0.86, the root mean square error (RMSE) of 35.50 g·m⁻² and the mean absolute percentage error (MAPE) of 4.6% for the training set, R² of 0.85, RMSE of 33.40 g.m⁻² and MAPE 4.30% for the validation set 1, and R² of 0.80, RMSE of 37.40 g·m⁻² and MAPE of 4.60% for the validation set 2. The research results demonstrated that the established model could accurately predict rice yield, providing technical support and a foundation for large-scale statistical estimating of rice yield.

In order to solve the problems of blocking the drainage ditch and reducing the soil flatness caused by soil accumulation when using compound planter with plowshare to ditch, a spiral soil separation mechanism (SSSM) is designed. The SSSM is analyzed. In order to obtain the optimal parameters of the SSSM, based on the discrete element method, the multifactor test is carried out with the embedded depth, pitch, and rotation speed of the spiral blade as the test factors and the soil separation distance and uniformity as the evaluation index. The optimal parameters are the embedded depth 49 mm, pitch 331 mm, and rotation speed of the spiral blade 318 r min⁻¹. The field experiment is carried out with these parameters, with soil separation distance 900 mm and standard deviation of soil height 7.8 mm, which is consistent with the simulation results. No blockage of drainage ditch was found, which shows that this device can effectively solve the problem. This study can provide a reference for the design of soil separation equipment using spiral soil separation device.