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Powdery mildew significantly impacts the yield of natural rubber by being one of the predominant diseases that affect rubber trees. Accurate, non-destructive recognition of powdery mildew in the early stage is essential for the cultivation management of rubber trees. The objective of this study is to establish a technique for the early detection of powdery mildew in rubber trees by combining spectral and physicochemical parameter features. At three field experiment sites and in the laboratory, a spectroradiometer and a hand-held optical leaf-clip meter were utilized, respectively, to measure the hyperspectral reflectance data (350–2500 nm) and physicochemical parameter data of both healthy and early-stage powdery-mildew-infected leaves. Initially, vegetation indices were extracted from hyperspectral reflectance data, and wavelet energy coefficients were obtained through continuous wavelet transform (CWT). Subsequently, significant vegetation indices (VIs) were selected using the ReliefF algorithm, and the optimal wavelengths (OWs) were chosen via competitive adaptive reweighted sampling. Principal component analysis was used for the dimensionality reduction of significant wavelet energy coefficients, resulting in wavelet features (WFs). To evaluate the detection capability of the aforementioned features, the three spectral features extracted above, along with their combinations with physicochemical parameter features (PFs) (VIs + PFs, OWs + PFs, WFs + PFs), were used to construct six classes of features. In turn, these features were input into support vector machine (SVM), random forest (RF), and logistic regression (LR), respectively, to build early detection models for powdery mildew in rubber trees. The results revealed that models based on WFs perform well, markedly outperforming those constructed using VIs and OWs as inputs. Moreover, models incorporating combined features surpass those relying on single features, with an overall accuracy (OA) improvement of over 1.9% and an increase in F1-Score of over 0.012. The model that combines WFs and PFs shows superior performance over all the other models, achieving OAs of 94.3%, 90.6%, and 93.4%, and F1-Scores of 0.952, 0.917, and 0.941 on SVM, RF, and LR, respectively. Compared to using WFs alone, the OAs improved by 1.9%, 2.8%, and 1.9%, and the F1-Scores increased by 0.017, 0.017, and 0.016, respectively. This study showcases the viability of early detection of powdery mildew in rubber trees. [ABSTRACT FROM AUTHOR]

Powdery mildew is one of the most significant rubber tree diseases, with a substantial impact on the yield of natural rubber. This study aims to establish a detection approach that coupled continuous wavelet transform (CWT) and machine learning for the accurate assessment of powdery mildew severity in rubber trees. In this study, hyperspectral reflectance data (350–2500 nm) of healthy and powdery mildew-infected leaves were measured with a spectroradiometer in a laboratory. Subsequently, three types of wavelet features (WFs) were extracted using CWT. They were as follows: WFs dimensionally reduced by the principal component analysis (PCA) of significant wavelet energy coefficients (PCA-WFs); WFs extracted from the top 1% of the determination coefficient between wavelet energy coefficients and the powdery mildew disease class (1%R2-WFs); and all WFs at a single decomposition scale (SS-WFs). To assess the detection capability of the WFs, the three types of WFs were input into the random forest (RF), support vector machine (SVM), and back propagation neural network (BPNN), respectively. As a control, 13 optimal traditional spectral features (SFs) were extracted and combined with the same classification methods. The results revealed that the WF-based models all performed well and outperformed those based on SFs. The models constructed based on PCA-WFs had a higher accuracy and more stable performance than other models. The model combined PCA-WFs with RF exhibited the optimal performance among all models, with an overall accuracy (OA) of 92.0% and a kappa coefficient of 0.90. This study demonstrates the feasibility of combining CWT with machine learning in rubber tree powdery mildew detection. [ABSTRACT FROM AUTHOR]

Through research and analysis of the existing remote sensing image sharing and distribution systems, remote sensing image recommendation mode can be divided into subscription recommendation and active recommendation. The first mode provides data query retrieval and subscription distribution services. However, retrieval and subscription services are based on query and subscription keywords, which are problematic or insufficiently active for users. Moreover, these processes cannot discover the latent requirements of a user. Therefore, how to recommend remote sensing images to users accurately and actively is a challenging problem. Research on the active remote sensing image recommendation is rare. The typical method is a space–time periodic task model (STPT), which realizes personalized remote sensing image recommendation based on simulation user log records. However, STPT is not accurate enough because it uses the minimum bounding rectangle as the filter condition of spatial feature and considers that the user's acquisition of images is periodic, so the data that match the periodic rules is more likely to be returned, resulting in a low recall rate. Additionally, it is less efficient for large-scale image recommendation tasks because it takes a long time to calculate the time-period using Fourier transform method. In this study, we propose a spatial–temporal embedding topic (STET) model to solve the recommendation problem of remote sensing images. This model processes the spatial, temporal, and content information of remote sensing images and constructs a topic model, thereby fully applying the continuity characteristics of space and time and improving the training efficient of the recommendation model. Compared with state-of-the-art models, the results based on large scale real-world datasets show that our model not only significantly improves the recall by more than 10%, the normalized discounted cumulative gain by more than 10% when K is 100 with the precision remaining above 97%, but it also greatly reduces the training time. • Remote sensing image recommendation based on spatial–temporal embedding topic model. • STET model integrates the spatial, temporal and content information into LDA. • Using differential segmentation method to process spatial information. • Large-scale real-world datasets including 1,648 users and 1,259,303 orders. [ABSTRACT FROM AUTHOR]