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In recent decades, rice disease co-epidemics have caused tremendous damage to crop production in both China and Southeast Asia. A variety of remote sensing based approaches have been developed and applied to map diseases distribution using coarse- to moderate-resolution imagery. However, the detection and discrimination of various disease species infecting rice were seldom assessed using high spatial resolution data. The aims of this study were (1) to develop a set of normalized two-stage vegetation indices (VIs) for characterizing the progressive development of different diseases with rice; (2) to explore the performance of combined normalized two-stage VIs in partial least square discriminant analysis (PLS-DA); and (3) to map and evaluate the damage caused by rice diseases at fine spatial scales, for the first time using bi-temporal, high spatial resolution imagery from PlanetScope datasets at a 3 m spatial resolution. Our findings suggest that the primary biophysical parameters caused by different disease (e.g., changes in leaf area, pigment contents, or canopy morphology) can be captured using combined normalized two-stage VIs. PLS-DA was able to classify rice diseases at a sub-field scale, with an overall accuracy of 75.62% and a Kappa value of 0.47. The approach was successfully applied during a typical co-epidemic outbreak of rice dwarf (Rice dwarf virus, RDV), rice blast ( Magnaporthe oryzae ), and glume blight ( Phyllosticta glumarum ) in Guangxi Province, China. Furthermore, our approach highlighted the feasibility of the method in capturing heterogeneous disease patterns at fine spatial scales over the large spatial extents.

Crop disease recognition plays a crucial role in agricultural production. However, disease images are large in scale and have a lot of redundant information, which reduces the effectiveness of deep neural networks in extracting diseases. To address the above issues and considering that not all image regions are relevant to disease recognition, this study proposes an efficient crop disease recognition method with dynamic reduction of image redundancy. The method is a two-stage process. In the first stage, we employ the lightweight CA-AnchorNet, which incorporates coordinate attention, to swiftly generate a feature map of the affected crop areas. Subsequently, class activation maps (CAMs) are utilized to identify the disease feature regions, highlighting areas that exhibit class discriminability. These regions are then mapped to a higher resolution from the original lower-resolution image, and the target patch is extracted. In the second stage, these local semantic patches, characterized by reduced spatial redundancy, are fed into the lightweight PatchNet for accurate recognition. PatchNet incorporates the Inception-C module and the ACON-C activation function. These features enhance the model's ability to express multi-scale and non-linear characteristics of crop disease features. This method does not require manually annotated region boxes and achieves an identification accuracy of 99.86 % on a 12-class crop disease dataset with complex environments. The parameter count is only 0.98 M. This method has the characteristics of accurate localization and low parameter count, and can be used for effective and high-precision recognition of crop diseases in complex environments. • Coordinate attention mechanism was used to design a lightweight CA-AnchorNet. • A lightweight CA-AnchorNet and PatchNet constitute a two-stage framework. • A patch localization algorithm was designed through a class activation map. • A multi-scale lightweight PatchNet network is proposed. [ABSTRACT FROM AUTHOR]

Monitoring and discriminating co-epidemic diseases and pests at regional scales are of practical importance in guiding differential treatment. A combination of vegetation and environmental parameters could improve the accuracy for discriminating crop diseases and pests. Different diseases and pests could cause similar stresses and symptoms during the same crop growth period, so combining growth period information can be useful for discerning different changes in crop diseases and pests. Additionally, problems associated with imbalanced data often have detrimental effects on the performance of image classification. In this study, we developed an approach for discriminating crop diseases and pests based on bi-temporal Landsat-8 satellite imagery integrating both crop growth and environmental parameters. As a case study, the approach was applied to data during a period of typical co-epidemic outbreak of winter wheat powdery mildew and aphids in the Shijiazhuang area of Hebei Province, China. Firstly, bi-temporal remotely sensed features characterizing growth indices and environmental factors were calculated based on two Landsat-8 images. The synthetic minority oversampling technique (SMOTE) algorithm was used to resample the imbalanced training data set before model construction. Then, a back propagation neural network (BPNN) based on a new training data set balanced by the SMOTE approach (SMOTE-BPNN) was developed to generate the regional wheat disease and pest distribution maps. The original training data set-based BPNN and support vector machine (SVM) methods were used for comparison and testing of the initial results. Our findings suggest that the proposed approach incorporating both growth and environmental parameters of different crop periods could distinguish wheat powdery mildew and aphids at the regional scale. The bi-temporal growth indices and environmental factors-based SMOTE-BPNN, BPNN, and SVM models all had an overall accuracy high than 80%. Meanwhile, the SMOTE-BPNN method had the highest G-means among the three methods. These results revealed that the combination of bi-temporal crop growth and environmental parameters is essential for improving the accuracy of the crop disease and pest discriminating models. The combination of SMOTE and BPNN could effectively improve the discrimination accuracy of the minor disease or pest. [ABSTRACT FROM AUTHOR]

Wheat Fusarium head blight (FHB) poses a significant threat to wheat quality and yield. However, accurately identifying the disease in wheat ears remains challenging due to limited data and weak hyperspectral signals. This study aimed to address these issues by obtaining effective wheat canopy hyperspectral data over three years of successive experiments. To reduce band redundancy and improve accuracy, nine different models were constructed, and the optimal algorithm was determined. Additionally, two types of new indices, were developed based on the spectral response mechanism of the wheat disease and published vegetation indices. Our study demonstrated that these newly constructed indices outperformed the published vegetation indices in terms of detection capability. By fusing the optimal algorithm with the new indices, a detection accuracy of 91.4% for the disease was achieved, surpassing the current level of wheat FHB detection. The high-accuracy model developed in this study not only provides methodological support for detecting wheat FHB but also serves as a reference for diagnosing diseases in other crops. • Effective wavelengths were screened from data using multiple algorithms. • Optimal algorithm for wheat FHB detection models determined. • Two new indices, WFI two and WFI three. developed. • Optimal algorithm and new indices were integrated to improve model accuracy. [ABSTRACT FROM AUTHOR]