Cotton Verticillium wilt is a common fungal disease during the growth of cotton, leading to the yellowing of leaves, stem dryness, and root rot, severely affecting the yield and quality of cotton. Current monitoring methods for Verticillium wilt mainly rely on manual inspection and field investigation, which are inefficient and costly, and the methods of applying pesticides in cotton fields are singular, with issues of low pesticide efficiency and uneven application. This study aims to combine UAV remote sensing monitoring of cotton Verticillium wilt with the precision spraying characteristics of agricultural drones, to provide a methodological reference for monitoring and precision application of pesticides for cotton diseases. Taking the cotton fields of Shihezi City, Xinjiang as the research subject, high-resolution multispectral images were collected using drones. Simultaneously, 150 sets of field samples with varying degrees of Verticillium wilt were collected through ground data collection, utilizing data analysis methods such as partial least squares regression (PLSR) and neural network models; additionally, a cotton Verticillium wilt monitoring model based on drone remote sensing images was constructed. The results showed that the estimation accuracy $R^2$ of the PLSR and BP neural network models based on EVI, RENDVI, SAVI, MSAVI, and RDVI vegetation indices were 0.778 and 0.817, respectively, with $RMSE$ of 0.126 and 0.117, respectively. Based on this, an analysis of the condition of the areas to be treated was performed, combining the operational parameters of agricultural drones, resulting in a prescription map for spraying against cotton Verticillium wilt.

Cotton aphids are the primary pests that adversely affect cotton growth, and they also transmit a variety of viral diseases, seriously threatening cotton yield and quality. Although the traditional remote sensing method with a single data source improves the monitoring efficiency to a certain extent, it has limitations with regard to reflecting the complex distribution characteristics of aphid pests and accurate identification. Accordingly, there is a pressing need for efficient and high-precision UAV remote sensing technology for effective identification and localization. To address the above problems, this study began by presenting a fusion of two kinds of images, namely panchromatic and multispectral images, using Gram–Schmidt image fusion technique to extract multiple vegetation indices and analyze their correlation with aphid damage indices. After fusing the panchromatic and multispectral images, the correlation between vegetation indices and aphid infestation degree was significantly improved, which could more accurately reflect the spatial distribution characteristics of aphid infestation. Subsequently, these machine learning techniques were applied for modeling and evaluation of the performance of multispectral and fused image data. The results of the validation revealed that the GBDT (Gradient-Boosting Decision Tree) model for GLI, RVI, DVI, and SAVI vegetation indices based on the fused data performed the best, with an estimation accuracy of R² of 0.88 and an RMSE of 0.0918, which was obviously better than that of the other five models, and that the monitoring method of combining fusion of panchromatic and multispectral imagery with the accuracy and efficiency of the GBDT model were noticeably higher than those of single multispectral imaging. The fused panchromatic and multispectral images combined with the GBDT model significantly outperformed the single multispectral image in terms of precision and efficiency. In conclusion, this study demonstrated the effectiveness of image fusion combined with GBDT modeling in cotton aphid pest monitoring.

Under complex field conditions, robust and efficient boll detection at maturity is an important tool for pre-harvest strategy and yield prediction. To achieve automatic detection and counting of long-staple cotton in a natural environment, this paper proposes an improved algorithm incorporating deformable convolution and attention mechanism, called YOLO-C, based on YOLOv7: (1) To capture more detailed and localized features in the image, part of the 3 × 3 convolution in the ELAN layer of the backbone is replaced by deformable convolution to improve the expressiveness and accuracy of the model. (2) To suppress irrelevant information, three SENet modules are introduced after the backbone to improve the ability of feature maps to express information, and CBAM and CA are introduced for comparison experiments. (3) A WIoU loss function based on a dynamic non-monotonic focusing mechanism is established to reduce the harmful gradients generated by low-quality examples on the original loss function and improve the model performance. During the model evaluation, the model is compared with other YOLO series and mainstream detection algorithms, and the model mAP@0.5 achieves 97.19%, which is 1.6% better than the YOLOv7 algorithm. In the model testing session, the root mean square error and coefficient of determination (${\mathrm{R}}^2$) of YOLO-C are 1.88 and 0.96, respectively, indicating that YOLO-C has higher robustness and reliability for boll target detection in complex environments and can provide an effective method for yield prediction of long-staple cotton at maturity.