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In order to achieve precise discrimination of leaf diseases in the Maize/Soybean intercropping system, i.e. leaf spot disease, rust disease, mixed leaf diseases, this study utilized hyperspectral imaging and deep learning algorithms for the classification of diseased leaves of maize and soybean. In the experiments, hyperspectral imaging equipment was used to collect hyperspectral images of leaves, and the regions of interest were extracted within the spectral range of 400 to 1000 nm. These regions included one or more infected areas on the leaves to obtain hyperspectral data. This approach aimed to enhance the accurate discrimination of different types of diseases, providing more effective technical support for the detection and control of crop diseases. The preprocessing of hyperspectral data involved four methods: Savitzky-Golay (SG), Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC) and 1st Derivative (1st Der). The 1st Der was found to be the optimal preprocessing method for hyperspectral data of maize and soybean diseases. Competitive Adaptive Reweighted Sampling (CARS), Successive Projections Algorithm (SPA) and Principal Component Analysis (PCA) were employed for feature extraction on the optimal preprocessed data. The Support Vector Machines (SVM), Bidirectional Long Short-Term Memory Network (BiLSTM) and Dung Beetle Optimization-Bidirectional Long Short-Term Memory Network (DBO-BiLSTM) were established for the discrimination of maize and soybean diseases. Comparative analysis indicated that, in the classification of maize and soybean diseases, the DBO-BiLSTM model based on the CARS extraction method (1st Der-CARS-DBO-BiLSTM) demonstrated the highest classification rate, reaching 98.7% on the test set. The research findings suggest that integrating hyperspectral imaging with both traditional and deep learning methods is a viable and effective approach for classifying diseases in the intercropping model of maize and soybean. These results offer a novel method and a theoretical foundation for the non-invasive, precise, and efficient identification of diseases in the intercropping model of maize and soybean, carrying positive implications for agricultural production. [ABSTRACT FROM AUTHOR]

Defoliation and accelerating ripening are important measures for cotton mechanization, and judging the time of defoliation and accelerating the ripening and harvest of cotton relies heavily on the boll opening rate, making it a crucial factor to consider. The traditional methods of cotton opening rate determination are time-consuming, labor-intensive, destructive, and not suitable for a wide range of applications. In this study, the relationship between the change rate of the vegetation index obtained by the unmanned aerial vehicle multi-spectrum and the ground boll opening rate was established to realize rapid non-destructive testing of the boll opening rate. The normalized difference vegetation index (NDVI) and green normalized difference vegetation index (GNDVI) had good prediction ability for the boll opening rate. NDVI in the training set had an R2 of 0.912 and rRMSE of 15.387%, and the validation set performance had an R2 of 0.929 and rRMSE of 13.414%. GNDVI in the training set had an R2 of 0.901 and rRMSE of 16.318%, and the validation set performance had an R2 of 0.909 and rRMSE of 15.225%. The accuracies of the models based on GNDVI and NDVI were within the acceptable range. In terms of predictive models, random forests achieve the highest accuracy in predictions. Accurately predicting the cotton boll opening rate can support decision-making for harvest and harvest aid spray timing, as well as provide technical support for crop growth monitoring and precision agriculture. [ABSTRACT FROM AUTHOR]