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The heading and flowering stages are crucial for wheat growth and should be used for fusarium head blight (FHB) and other plant prevention operations. Rapid and accurate monitoring of wheat growth in hilly areas is critical for determining plant protection operations and strategies. Currently, the operation time for FHB prevention and plant protection is primarily determined by manual tour inspection of plant growth, which has the disadvantages of low information gathering and subjectivity. In this study, an unmanned aerial vehicle (UAV) equipped with a multispectral camera was used to collect wheat canopy multispectral images and heading rate information during the heading and flowering stages in order to develop a method for detecting the appropriate time for preventive control of FHB. A 1D convolutional neural network + decision tree model (1D CNN + DT) was designed. All the multispectral information was input into the model for feature extraction and result regression. The regression revealed that the coefficient of determination (R2) between multispectral information in the wheat canopy and the heading rate was 0.95, and the root mean square error of prediction (RMSE) was 0.24. This result was superior to that obtained by directly inputting multispectral data into neural networks (NN) or by inputting multispectral data into NN via traditional VI calculation, support vector machines regression (SVR), or decision tree (DT). On the basis of FHB prevention and control production guidelines and field research results, a discrimination model for FHB prevention and plant protection operation time was developed. After the output values of the regression model were input into the discrimination model, a 97.50% precision was obtained. The method proposed in this study can efficiently monitor the growth status of wheat during the heading and flowering stages and provide crop growth information for determining the timing and strategy of FHB prevention and plant protection operations. [ABSTRACT FROM AUTHOR]

Topping is an important part in cotton field management, the spraying time has a great impact on cotton quality. In agricultural production, the strategy of timing the cotton topping mainly relies on manual inspections and experience, which is lack of efficiency and science. To solve the problem, this paper uses a drone equipped with a multispectral camera to collect the multispectral information of the cotton canopy of 12 days which includes before and after the topping operation in Shihezi. At the same time, the information of cotton plant height, the number of fruiting branches, and flower buds are collected. Compare multiple band combinations and vegetation index; the combined data of 550 + 730 + 790 nm band is selected as the model input. AdaBoost + decision tree method is proposed as a fitting model, the fitting results show that the coefficient of determination (R2) between multispectrum and cotton plant height is 0.96, and the average prediction error (RMSEP) is 0.40 cm, the coefficient of determination (R2) between multispectrum information and the fruiting branches is 0.97, the prediction mean error (RMSEP) is 0.54, and the correlation determination (R2) with the flower buds is 0.84, and the prediction mean error is (RMSEP) 0.49. The output data of the fitting model is used as the input of the topping time discriminant model, and the discriminant model can obtain an accuracy of 94.03%. The method in this paper can effectively monitor the growth status of cotton in the topping time and provide a technical path to scientifically determine the cotton topping time. [ABSTRACT FROM AUTHOR]