Search

SignificanceThe scientific dataset of agricultural pests and diseases is the foundation for monitoring and warning of agricultural pests and diseases. It is of great significance for the development of agricultural pest control, and is an important component of developing smart agriculture. The quality of the dataset affecting the effectiveness of image recognition algorithms, with the discovery of the importance of deep learning technology in intelligent monitoring of agricultural pests and diseases. The construction of high-quality agricultural pest and disease datasets is gradually attracting attention from scholars in this field. In the task of image recognition, on one hand, the recognition effect depends on the improvement strategy of the algorithm, and on the other hand, it depends on the quality of the dataset. The same recognition algorithm learns different features in different quality datasets, so its recognition performance also varies. In order to propose a dataset evaluation index to measure the quality of agricultural pest and disease datasets, this article analyzes the existing datasets and takes the challenges faced in constructing agricultural pest and disease image datasets as the starting point to review the construction of agricultural pest and disease datasets.ProgressFirstly, disease and pest datasets are divided into two categories: private datasets and public datasets. Private datasets have the characteristics of high annotation quality, high image quality, and a large number of inter class samples that are not publicly available. Public datasets have the characteristics of multiple types, low image quality, and poor annotation quality. Secondly, the problems faced in the construction process of datasets are summarized, including imbalanced categories at the dataset level, difficulty in feature extraction at the dataset sample level, and difficulty in measuring the dataset size at the usage level. These include imbalanced inter class and intra class samples, selection bias, multi-scale targets, dense targets, uneven data distribution, uneven image quality, insufficient dataset size, and dataset availability. The main reasons for the problem are analyzed by two key aspects of image acquisition and annotation methods in dataset construction, and the improvement strategies and suggestions for the algorithm to address the above issues are summarized. The collection devices of the dataset can be divided into handheld devices, drone platforms, and fixed collection devices. The collection method of handheld devices is flexible and convenient, but it is inefficient and requires high photography skills. The drone platform acquisition method is suitable for data collection in contiguous areas, but the detailed features captured are not clear enough. The fixed device acquisition method has higher efficiency, but the shooting scene is often relatively fixed. The annotation of image data is divided into rectangular annotation and polygonal annotation. In image recognition and detection, rectangular annotation is generally used more frequently. It is difficult to label images that are difficult to separate the target and background. Improper annotation can lead to the introduction of more noise or incomplete algorithm feature extraction. In response to the problems in the above three aspects, the evaluation methods are summarized for data distribution consistency, dataset size, and image annotation quality at the end of the article.Conclusions and ProspectsThe future research and development suggestions for constructing high-quality agricultural pest and disease image datasets based are proposed on the actual needs of agricultural pest and disease image recognition:(1) Construct agricultural pest and disease datasets combined with practical usage scenarios. In order to enable the algorithm to extract richer target features, image data can be collected from multiple perspectives and environments to construct a dataset. According to actual needs, data categories can be scientifically and reasonably divided from the perspective of algorithm feature extraction, avoiding unreasonable inter class and intra class distances, and thus constructing a dataset that meets task requirements for classification and balanced feature distribution. (2) Balancing the relationship between datasets and algorithms. When improving algorithms, consider the more sufficient distribution of categories and features in the dataset, as well as the size of the dataset that matches the model, to improve algorithm accuracy, robustness, and practicality. It ensures that comparative experiments are conducted on algorithm improvement under the same evaluation standard dataset, and improved the pest and disease image recognition algorithm. Research the correlation between the scale of agricultural pest and disease image data and algorithm performance, study the relationship between data annotation methods and algorithms that are difficult to annotate pest and disease images, integrate recognition algorithms for fuzzy, dense, occluded targets, and propose evaluation indicators for agricultural pest and disease datasets. (3) Enhancing the use value of datasets. Datasets can not only be used for research on image recognition, but also for research on other business needs. The identification, collection, and annotation of target images is a challenging task in the construction process of pest and disease datasets. In the process of collecting image data, in addition to collecting images, attention can be paid to the collection of surrounding environmental information and host information. This method is used to construct a multimodal agricultural pest and disease dataset, fully leveraging the value of the dataset. In order to focus researchers on business innovation research, it is necessary to innovate the organizational form of data collection, develop a big data platform for agricultural diseases and pests, explore the correlation between multimodal data, improve the accessibility and convenience of data, and provide efficient services for application implementation and business innovation.

Achieving high luminescent quantum yield and thermal stability of phosphors simultaneously remains challenging, yet it is critical for facilitating high-power white light emitting diodes (WLEDs). Herein, we report the design and preparation of the layered structure BaZnB4O8:xEu3+ (0.10 ≤ x ≤ 0.60) red phosphors with high quantum yield (QY = 76.5%) and thermal stability (82.8%@150 °C) by the traditional solid-state reaction method. The results of XRD and Rietveld refinement show that the presence of Eu3+ ions at Ba2+ sites causes the formation of cation (Zn2+/Ba2+) vacancies in the lattice. The PL and PL decay results reveal that the quenching concentration of BZBO:xEu3+ phosphors is as high as 50%, and the lifetime remains unchanged with Eu3+ concentration due to the unique structure of the host and the cation vacancies generated by the heterovalent substitution. Furthermore, on a 395 nm near-UV chip, a pc-WLED device with exceptional optical performance (CCT = 4415 K, CRI = 92.1) was realized using the prepared BZBO:0.50Eu3+ as a red phosphor. Simple synthesis and excellent performance parameters suggest that the reported BaZnB4O8:xEu3+ phosphors have promising applications in high-power pc-WLEDs. At the same time, it also indicates that cationic vacancy engineering based on heterovalent ion substitution is a potential strategy for improving luminescence quantum yield and thermal quenching performance. [ABSTRACT FROM AUTHOR]

The Chinese Loess Plateau (CLP) is the largest loess deposit on Earth with expansive surface-exposed source rocks of varying origins, ages, and history. Here, we present abundances of elements on representative loess and palaeosol samples from seven classic sections of the CLP. Most elements, including soluble elements (e.g. Rb and Cs), show significant correlations with La or Al2O3. These correlations indicate that these elements are hosted or absorbed in particle minerals during weathering, transport, and deposition (e.g. mica, K-feldspar, and clay minerals). These new observations allow the use of La/X ('X' being the element of interest) and the estimated La abundance of 31 ppm in the model upper continental crust (UCC) to estimate the abundances of other elements. The results show higher Cs (Cs = 6.7 ± 1.2 ppm), lower transition metals, Ba, and Ga. Given the high CaO and presence of carbonate in UCC rocks of both vast western China (the primary source for the CLP) and eastern China, we propose that these updates on the element abundances represent a refined model for the carbonate-bearing UCC. [ABSTRACT FROM AUTHOR]

The West Qinling Orogenic Belt (WQOB) is a major portion of the Qinling‐Dabie‐Sulu Orogen and holds essential information for understanding the protracted evolution of the north‐eastern branch of the Paleo‐Tethys in East Asia. In this study, we report our petrological, geochemical, and geochronological study on the five Triassic granitoid plutons of West Qinling with emphasis on the poorly studied Jiaochangba pluton with zircon U–Pb ages of 217.5 ± 1.6 Ma and 215.2 ± 1.2 Ma. The new data and the existing data on the other four plutons support the view that the West Qinling granitoids represent a magmatic response to the continental collision of the Yangtze Block (YB) with the North China Craton (NCC) in the Triassic. Like the other four plutons, the Jiaochangba pluton shows strong light rare earth element (REE) enrichment and weak heavy REE depletion ([La/Sm]N ≈ 7.14 ± 1.89; [Sm/Yb]N ≈ 4.63 ± 1.85) with varying negative Eu anomalies (Eu/Eu* ≈ 0.65 ± 0.20). In the N‐MORB normalized diagram, all the samples show relative enrichment in Rb, Pb, U, and K with negative Nb, Ta, P, and Ti anomalies, resembling those of the model continental crust. The Jiaochangba pluton has relatively lower (87Sr/86Sr)i (0.7062 to 0.7081), higher εNd(t) (−6.91 to −2.09), and εHf(t) (−5.57 to −0.14) than mature continental crust, which are consistent with their source being dominated by lower crust with significant mantle contributions. Mantle‐derived melt, which formed from partial melting of mantle wedge peridotite facilitated by dehydration of the subducted/subducting Mianlue ocean crust, provide the required heat for the crustal melting while also contributing to the compositions of these granitoids. Evolution of such parental magmas in open system crustal magma chambers with continued evolution/replenishment and crustal contamination and assimilation give rise to the observed petrological and geochemical characteristics of these granitoids. [ABSTRACT FROM AUTHOR]

Achieving high luminescent quantum yield and thermal stability of phosphors simultaneously remains challenging, yet it is critical for facilitating high-power white light emitting diodes (WLEDs). Herein, we report the design and preparation of the layered structure BaZnB 4 O 8 : x Eu 3+ (0.10 ≤ x ≤ 0.60) red phosphors with high quantum yield (QY = 76.5%) and thermal stability (82.8%@150 °C) by the traditional solid-state reaction method. The results of XRD and Rietveld refinement show that the presence of Eu 3+ ions at Ba 2+ sites causes the formation of cation (Zn 2+ /Ba 2+ ) vacancies in the lattice. The PL and PL decay results reveal that the quenching concentration of BZBO: x Eu 3+ phosphors is as high as 50%, and the lifetime remains unchanged with Eu 3+ concentration due to the unique structure of the host and the cation vacancies generated by the heterovalent substitution. Furthermore, on a 395 nm near-UV chip, a pc-WLED device with exceptional optical performance (CCT = 4415 K, CRI = 92.1) was realized using the prepared BZBO:0.50Eu 3+ as a red phosphor. Simple synthesis and excellent performance parameters suggest that the reported BaZnB 4 O 8 : x Eu 3+ phosphors have promising applications in high-power pc-WLEDs. At the same time, it also indicates that cationic vacancy engineering based on heterovalent ion substitution is a potential strategy for improving luminescence quantum yield and thermal quenching performance.

The fuzzy decision system for ecological distribution of citrus in north-cultivated-marginal area was established by using the data of mean extreme low temperature, accumulated active temperature (> or = 12.5 degrees C), mean temperature in coldest month, annual mean temperature integrated with annual precipitation, days with mean extreme low temperature below -9 degrees C, physiognomy, elevation, inland water body, cold air barrier, and soil nutrition and pH value. The complex reasoning model with imprecision was used in the decision system by introduction of L-R fuzzy number and recognition formula, which made the decision system have a wide range of application in practice. An example of applying the decision system was tested in Anhui province. The result showed that the development of the decision system would provide a useful tool for the decision of the ecological distribution of citrus in north-cultivated-marginal area.