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Monitoring, Early Warning, and Scientific Management of Vegetation Pests and Diseases

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 19979

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Special Issue Editors

Dr. Jinling Zhao

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National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
Interests: hyperspectral image processing; remote sensing monitoring and forecasting of agricultural pests and diseases; remote sensing analysis of land use/land cover; spatial pattern analysis of agricultural landscape
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Prof. Dr. Wenjiang Huang

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Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: vegetation remote sensing; image processing; image classification; parameter mapping; system development; agricultural application
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Prof. Dr. Jingcheng Zhang

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College of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou, China
Interests: agricultural remote sensing; algorithms and models for processing multi-source geological data
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Dr. Linsheng Huang

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National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
Interests: monitoring and forecasting of crop diseases and insect pests; signal and information processing; vegetion remote sensing

Dr. Yingying Dong

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Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: hyperspectral remote sensing; multispectral remote sensing; precision agriculture; data processing; data assimilation; pests and diseases; habitat monitoring; risk forecasting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Climate change has caused a rapid increase in frequencies and severities of vegetation (crop, forest, grass, etc.) diseases and insect pests. It is highly necessary to monitor, predict the incidence and prevalence to support scientific management and biodiversity conservation, especially at a large scale (global, regional, national, local etc.). Remote sensing technology has greatly facilitated the diagnosis and treatment of vegetation diseases and insect pests to guarantee food security and stable ecological system.

In this Special Issue, we welcome papers from the international research community actively involved in research activities on vegetation diseases and insect pests monitoring, prediction, and scientific management. The Special Issue is open to all researchers working in these fields. The choice of papers for publication will rely on quality, soundness, and rigour of the research.

Dr. Jinling Zhao
Prof. Dr. Wenjiang Huang
Prof. Dr. Jingcheng Zhang
Dr. Linsheng Huang
Dr. Yingying Dong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

analysis and diagnosis of diseases and insect pests from ground, drone, air- and space-borne platforms
early-warning method through hyperspectral and thermal remote sensing
field and laboratory hyperspectral measurements for pathogenic mechanism
retrieval of severities of diseases and insect pests from multi-source remote sensing data
identification of diseases and insect pests at various spatial scales
deep learning methods for classifying different diseases and insect pests
mapping the tempo-spatial distribution of diseases and insect pests
relationship between the incidence of diseases and insect pests and environment

Published Papers (11 papers)

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Research

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17 pages, 27780 KiB

Open AccessArticle

Assessing Rice Sheath Blight Disease Habitat Suitability at a Regional Scale through Multisource Data Analysis

by Jingcheng Zhang, Huizi Li, Yangyang Tian, Hanxiao Qiu, Xuehe Zhou, Huiqin Ma and Lin Yuan

Remote Sens. 2023, 15(23), 5530; https://doi.org/10.3390/rs15235530 - 28 Nov 2023

Viewed by 890

Abstract

Extensive occurrence of rice sheath blight has been observed in China in recent years due to agricultural practices and climatic conditions, posing a serious threat to rice production. Assessing habitat suitability for rice sheath blight at a regional scale can provide important information for disease forecasting. In this context, the present study aims to propose a regional-scale habitat suitability evaluation method for rice sheath blight in Yangzhou city using multisource data, including remote sensing data, meteorological data, and disease survey data. By combining the epidemiological characteristics of the crop disease and the Relief-F algorithm, some habitat variables from key stages were selected. The maximum entropy (Maxent) and logistic regression models were adopted and compared in constructing the disease habitat suitability assessment model. The results from the Relief-F algorithm showed that some remote sensing variables in specific temporal phases are particularly crucial for evaluating disease habitat suitability, including the MODIS products of LAI (4–20 August), FPAR (9–25 June), NDVI (12–20 August), and LST (11–27 July). Based on these remote sensing variables and meteorological features, the Maxent model yielded better accuracy than the logistic regression model, with an area under the curve (AUC) value of 0.90, overall accuracy (OA) of 0.75, and a true skill statistics (TSS) value of 0.76. Indeed, the results of the habitat suitability assessment models were consistent with the actual distribution of the disease in the study area, suggesting promising predictive capability. Therefore, it is feasible to utilize remotely sensed and meteorological variables for assessing disease habitat suitability at a regional scale. The proposed method is expected to facilitate prevention and control practices for rice sheath blight disease. Full article

(This article belongs to the Special Issue Monitoring, Early Warning, and Scientific Management of Vegetation Pests and Diseases)

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13 pages, 4226 KiB

Open AccessArticle

Estimating Leymus chinensis Loss Caused by Oedaleus decorus asiaticus Using an Unmanned Aerial Vehicle (UAV)

by Bobo Du, Xiaolong Ding, Chao Ji, Kejian Lin, Jing Guo, Longhui Lu, Yingying Dong, Wenjiang Huang and Ning Wang

Remote Sens. 2023, 15(17), 4352; https://doi.org/10.3390/rs15174352 - 04 Sep 2023

Viewed by 975

Abstract

Oedaleus decorus asiaticus is one of the dominant harmful pests in central Inner Mongolia, China. Large-scale outbreaks of this pest create many serious problems in animal husbandry and agriculture. Therefore, understanding the underlying mechanisms between plant losses and Odecorus at different density levels and growth stages can guide the development of monitoring and prediction measures to reduce damage. In this study, an unmanned aerial vehicle (UAV) carrying a camera was employed to collect multi-spectral data. Further, nine vegetation indices (VIs) were analyzed to explore the most suitable indices for estimating plant loss caused by O. decorus in different growth stages. The following results were obtained: (1) The second instar nymphs of O. decorus could promote vegetation growth. As the density level in each cage increased, the biomass of each cage increased (nymph density < 30 nymphs/m²) and then decreased (nymph density ≥ 30 nymphs/m²). When nymph density was greater than 60 nymphs/m², the biomass in those cages decreased significantly. (2) With respect to the control group, large damage began to emerge during the third instar nymphal stage. In particular, the largest vegetation loss was caused by fourth nymphal larvae. (3) The ratio vegetation index (RVI) appeared as the most excellent index for reflecting Leymus chinensis loss caused by O. decorus at different growth stages. Nevertheless, the difference vegetation index (DVI) was better than the RVI in the fifth instar nymphal stage. Full article

(This article belongs to the Special Issue Monitoring, Early Warning, and Scientific Management of Vegetation Pests and Diseases)

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27 pages, 19190 KiB

Open AccessArticle

Land-Use Mapping with Multi-Temporal Sentinel Images Based on Google Earth Engine in Southern Xinjiang Uygur Autonomous Region, China

by Riqiang Chen, Hao Yang, Guijun Yang, Yang Liu, Chengjian Zhang, Huiling Long, Haifeng Xu, Yang Meng and Haikuan Feng

Remote Sens. 2023, 15(16), 3958; https://doi.org/10.3390/rs15163958 - 10 Aug 2023

Cited by 2 | Viewed by 1428

Abstract

Land-use maps are thematic materials reflecting the current situation, geographical diversity, and classification of land use and are an important scientific foundation that can assist decision-makers in adjusting land-use structures, agricultural zoning, regional planning, and territorial improvement according to local conditions. Spectral reflectance and radar signatures of time series are important in distinguishing land-use types. However, their impact on the accuracy of land-use mapping and decision making remains unclear. Also, the many spatial and temporal heterogeneous landscapes in southern Xinjiang limit the accuracy of existing land-use classification products. Therefore, our objective herein is to develop reliable land-use products for the highly heterogeneous environment of the southern Xinjiang Uygur Autonomous Region using the freely available public Sentinel image datasets. Specifically, to determine the effect of temporal features on classification, several classification scenarios with different temporal features were developed using multi-temporal Sentinel-1, Sentinel-2, and terrain data in order to assess the importance, contribution, and impact of different temporal features (spectral and radar) on land-use classification models and determine the optimal time for land-use classification. Furthermore, to determine the optimal method and parameters suitable for local land-use classification research, we evaluated and compared the performance of three decision-tree-related classifiers (classification and regression tree, random forest, and gradient tree boost) with respect to classifying land use. Yielding the highest average overall accuracy (95%), kappa (95%), and F₁ score (98%), we determined that the gradient tree boost model was the most suitable for land-use classification. Of the four individual periods, the image features in autumn (25 September to 5 November) were the most accurate for all three classifiers in relation to identifying land-use classes. The results also show that the inclusion of multi-temporal image features consistently improves the classification of land-use products, with pre-summer (28 May–20 June) images providing the most significant improvement (the average OA, kappa, and F₁ score of all the classifiers were improved by 6%, 7%, and 3%, respectively) and fall images the least (the average OA, kappa, and F₁ score of all the classifiers were improved by 2%, 3%, and 2%, respectively). Overall, these analyses of how classifiers and image features affect land-use maps provide a reference for similar land-use classifications in highly heterogeneous areas. Moreover, these products are designed to describe the highly heterogeneous environments in the study area, for example, identifying pear trees that affect local economic development, and allow for the accurate mapping of alpine wetlands in the northwest. Full article

(This article belongs to the Special Issue Monitoring, Early Warning, and Scientific Management of Vegetation Pests and Diseases)

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9 pages, 15477 KiB

Open AccessCommunication

MODIS Time Series Reveal New Maximum Records of Defoliated Area by Ormiscodes amphimone in Deciduous Nothofagus Forests, Southern Chile

by Sergio A. Estay, Roberto O. Chávez, José A. Lastra, Ronald Rocco, Álvaro G. Gutiérrez and Mathieu Decuyper

Remote Sens. 2023, 15(14), 3538; https://doi.org/10.3390/rs15143538 - 14 Jul 2023

Viewed by 1110

Abstract

Outbreaks of the Ormiscodes amphimone moth are among the largest biotic disturbances in South America, defoliating vast areas of native Nothofagus pumilio forests in the Chilean and Argentinian Patagonia in the last decade. Using MODIS 16-day composites of the enhanced vegetation index and the new functions of the latest release of the “npphen” R-package, we identified new maximum records of continuously defoliated area in the Aysén region (Chilean Patagonia). This approach allowed us to detect 55,193 ha and 62,344 ha of extremely defoliated N. pumilio forest in 2019 and 2022, respectively, in an area locally known as “Mallín Grande”. Extreme defoliation was accounted for by means of negative EVI anomalies with values falling among 5% of the lowest EVI records of the reference period (2000–2010). These new 2019 and 2022 outbreaks in Mallín Grande were the largest reported insect outbreaks in South American Patagonia in this century. Full article

(This article belongs to the Special Issue Monitoring, Early Warning, and Scientific Management of Vegetation Pests and Diseases)

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17 pages, 4756 KiB

Open AccessArticle

Detecting Individual Plants Infected with Pine Wilt Disease Using Drones and Satellite Imagery: A Case Study in Xianning, China

by Peihua Cai, Guanzhou Chen, Haobo Yang, Xianwei Li, Kun Zhu, Tong Wang, Puyun Liao, Mengdi Han, Yuanfu Gong, Qing Wang and Xiaodong Zhang

Remote Sens. 2023, 15(10), 2671; https://doi.org/10.3390/rs15102671 - 20 May 2023

Cited by 4 | Viewed by 1615

Abstract

In recent years, remote sensing techniques such as satellite and drone-based imaging have been used to monitor Pine Wilt Disease (PWD), a widespread forest disease that causes the death of pine species. Researchers have explored the use of remote sensing imagery and deep learning algorithms to improve the accuracy of PWD detection at the single-tree level. This study introduces a novel framework for PWD detection that combines high-resolution RGB drone imagery with free-access Sentinel-2 satellite multi-spectral imagery. The proposed approach includes an PWD-infected tree detection model named YOLOv5-PWD and an effective data augmentation method. To evaluate the proposed framework, we collected data and created a dataset in Xianning City, China, consisting of object detection samples of infected trees at middle and late stages of PWD. Experimental results indicate that the YOLOv5-PWD detection model achieved 1.2% higher mAP compared to the original YOLOv5 model and a further improvement of 1.9% mAP was observed after applying our dataset augmentation method, which demonstrates the effectiveness and potential of the proposed framework for PWD detection. Full article

(This article belongs to the Special Issue Monitoring, Early Warning, and Scientific Management of Vegetation Pests and Diseases)

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19 pages, 5044 KiB

Open AccessArticle

Dynamic Analysis of Regional Wheat Stripe Rust Environmental Suitability in China

by Linsheng Huang, Xinyu Chen, Yingying Dong, Wenjiang Huang, Huiqin Ma, Hansu Zhang, Yunlei Xu and Jing Wang

Remote Sens. 2023, 15(8), 2021; https://doi.org/10.3390/rs15082021 - 11 Apr 2023

Cited by 1 | Viewed by 1729

Abstract

Stripe rust is one of the most destructive wheat diseases in China, negatively affecting the production safety and causing yield losses of wheat. Thus, it is important to analyze the environmental suitability and dynamic changes of wheat stripe rust in China. The occurrence of stripe rust is affected by multiple factors. Therefore, this study combined data from various disciplinary fields such as remote sensing, meteorology, biology, and plant protection to evaluate the environmental suitability of stripe rust in China using species distribution models. The study also discusses the importance and effect of various variables. Results revealed that meteorological factors had the greatest impact on the occurrence of stripe rust, especially temperature and precipitation. Wheat growth factors have a greater impact from April to August. Elevation has a greater impact in summer. The ensemble model results were better than the single model, with TSS and AUC greater than 0.851 and 0.971, respectively. Overlapping analysis showed that the winter stripe rust suitable areas were mainly in the Sichuan Basin, Northwestern Hubei, Southern Shaanxi, and Southern Henan wheat areas. In spring, the suitable areas of stripe rust increased in Huang-Huai-Hai and the middle and lower reaches of the Yangtze River and Guanzhong Plain, and the development of northwestern wheat areas such as Xinjiang and Gansu slightly lagged behind. In summer, wheat threatened by stripe rust is mainly in late-ripening spring wheat areas in Gansu, Ningxia, Qinghai, and Xinjiang. This study can provide a scientific basis for optimizing and improving the comprehensive management strategy of stripe rust. Full article

(This article belongs to the Special Issue Monitoring, Early Warning, and Scientific Management of Vegetation Pests and Diseases)

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17 pages, 6452 KiB

Open AccessArticle

Spatiotemporal Distribution and Main Influencing Factors of Grasshopper Potential Habitats in Two Steppe Types of Inner Mongolia, China

by Jing Guo, Longhui Lu, Yingying Dong, Wenjiang Huang, Bing Zhang, Bobo Du, Chao Ding, Huichun Ye, Kun Wang, Yanru Huang, Zhuoqing Hao, Mingxian Zhao and Ning Wang

Remote Sens. 2023, 15(3), 866; https://doi.org/10.3390/rs15030866 - 03 Feb 2023

Cited by 6 | Viewed by 1913

Abstract

Grasshoppers can greatly interfere with agriculture and husbandry, and they will breed and grow rapidly in suitable habitats. Therefore, it is necessary to extract the distribution of the grasshopper potential habitat (GPH), analyze the spatial-temporal characteristics of the GPH, and detect the different effects of key environmental factors in the meadow and typical steppe. To achieve the goal, this study took the two steppe types of Xilingol (the Inner Mongolia Autonomous Region of China) as the research object and coupled them with the MaxEnt and multisource remote sensing data to establish a model. First, the environmental factors, including meteorological, vegetation, topographic, and soil factors, that affect the developmental stages of grasshoppers were obtained. Secondly, the GPH associated with meadow and typical steppes from 2018 to 2022 were extracted based on the MaxEnt model. Then, the spatial-temporal characteristics of the GPHs were analyzed. Finally, the effects of the habitat factors in two steppe types were explored. The results demonstrated that the most suitable and moderately suitable areas were distributed mainly in the southern part of the meadow steppe and the eastern and southern parts of the typical steppe. Additionally, most areas in the town of Gaorihan, Honggeergaole, Jirengaole, as well as the border of Wulanhalage and Haoretugaole became more suitable for grasshoppers from 2018 to 2022. This paper also found that the soil temperature in the egg stage, the vegetation type, the soil type, and the precipitation amount in the nymph stage were significant factors both in the meadow and typical steppes. The slope and precipitation in the egg stage played more important roles in the typical steppe, whereas the aspect had a greater contribution to the meadow steppe. These findings can provide a methodical guide for grasshopper control and management and for further ensuring the security of agriculture and husbandry. Full article

(This article belongs to the Special Issue Monitoring, Early Warning, and Scientific Management of Vegetation Pests and Diseases)

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19 pages, 5072 KiB

Open AccessArticle

Early Detection of Dendroctonus valens Infestation at Tree Level with a Hyperspectral UAV Image

by Bingtao Gao, Linfeng Yu, Lili Ren, Zhongyi Zhan and Youqing Luo

Remote Sens. 2023, 15(2), 407; https://doi.org/10.3390/rs15020407 - 09 Jan 2023

Cited by 6 | Viewed by 1815

Abstract

The invasive pest Dendroctonus valens has spread to northeast China, causing serious economic and ecological losses. Early detection and disposal of infested trees is critical to prevent its outbreaks. This study aimed to evaluate the potential of an unmanned aerial vehicle (UAV)-based hyperspectral image for early detection of D. valens infestation at the individual tree level. We compared the spectral characteristics of Pinus tabuliformis in three states (healthy, infested and dead), and established classification models using three groups of features (reflectance, derivatives and spectral vegetation indices) and two algorithms (random forest and convolutional neural network). The spectral features of dead trees were clearly distinct from those of the other two classes, and all models identified them accurately. The spectral changes of infested trees occurred mainly in the visible region, but it was difficult to distinguish infested from healthy trees using random forest classification models based on reflectance and derivatives. The random forest model using spectral vegetation indices and the convolutional neural network model performed better, with an overall accuracy greater than 80% and a recall rate of infested trees reaching 70%. Our results demonstrated the great potential of hyperspectral imaging and deep learning for the early detection of D. valens infestation. The convolutional neural network proposed in this study can provide a reference for the automatic detection of early D. valens infestation using UAV-based multispectral or hyperspectral images in the future. Full article

(This article belongs to the Special Issue Monitoring, Early Warning, and Scientific Management of Vegetation Pests and Diseases)

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19 pages, 21402 KiB

Open AccessArticle

Mapping the Spatio-Temporal Distribution of Fall Armyworm in China by Coupling Multi-Factors

by Yanru Huang, Hua Lv, Yingying Dong, Wenjiang Huang, Gao Hu, Yang Liu, Hui Chen, Yun Geng, Jie Bai, Peng Guo and Yifeng Cui

Remote Sens. 2022, 14(17), 4415; https://doi.org/10.3390/rs14174415 - 05 Sep 2022

Cited by 2 | Viewed by 2860

Abstract

The fall armyworm (FAW) (Spodoptera frugiperda) (J. E. Smith) is a migratory pest that lacks diapause and has raised widespread concern in recent years due to its global dispersal and infestation. Seasonal environmental changes lead to its large-scale seasonal activities, and quantitative simulations of its dispersal patterns and spatiotemporal distribution facilitate integrated pest management. Based on remote sensing data and meteorological assimilation products, we constructed a mechanistic model of the dynamic distribution of FAW (FAW-DDM) by integrating weather-driven flight of FAW with host plant phenology and environmental suitability. The potential distribution of FAW in China from February to August 2020 was simulated. The results showed a significant linear relationship between the dates of the first simulated invasion and the first observed invasion of FAW in 125 cities (R² = 0.623; p < 0.001). From February to April, FAW was distributed in the Southwestern and Southern Mountain maize regions mainly due to environmental influences. From May to June, FAW spread rapidly, and reached the Huanghuaihai and North China maize regions between June to August. Our results can help in developing pest prevention and control strategies with data on specific times and locations, reducing the impact of FAW on food security. Full article

(This article belongs to the Special Issue Monitoring, Early Warning, and Scientific Management of Vegetation Pests and Diseases)

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17 pages, 4186 KiB

Open AccessArticle

Spatial and Temporal Variability of Grassland Grasshopper Habitat Suitability and Its Main Influencing Factors

by Bobo Du, Jun Wei, Kejian Lin, Longhui Lu, Xiaolong Ding, Huichun Ye, Wenjiang Huang and Ning Wang

Remote Sens. 2022, 14(16), 3910; https://doi.org/10.3390/rs14163910 - 12 Aug 2022

Cited by 12 | Viewed by 1818

Abstract

Grasshoppers are highly destructive pests, and their outbreak can directly damage livestock development. Grasshopper outbreaks can be monitored and forecasted through dynamic analysis of their potential geographic distribution and main influencing factors. By integrating vegetation, edaphic, meteorological, topography, and other geospatial data, this study simulated the grasshopper suitability index in Hulunbuir grassland using maximum entropy species distribution modeling (Maxent). The Maxent model showed high accuracy, with the training area under the curve (AUC) value ranging from 0.897 to 0.973 and the testing AUC ranging from 0.853 to 0.971 for the past 13 years. The results showed that suitable areas, including the most suitable area and moderately suitable area, accounted for a small proportion and were mainly located in the eastern and southern parts of the study area. According to model analysis based on 51 environmental factors, not all factors played a significant role in the grasshopper cycle. Moreover, differences in environmental factors drive the spatial variability of suitable areas for grasshoppers. The monitoring and prediction of potential outbreak areas can be improved by identifying major environmental factors having large variability between suitable and unsuitable areas. Future trends in grasshopper suitability indices are likely to contradict past trends in most of the study area, with only approximately 33% of the study area continuing the past trend. The results are expected to guide future monitoring and prediction of grasshoppers in Hulunbuir grassland. Full article

(This article belongs to the Special Issue Monitoring, Early Warning, and Scientific Management of Vegetation Pests and Diseases)

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Review

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25 pages, 3893 KiB

Open AccessReview

Global Trends and Future Directions in Agricultural Remote Sensing for Wheat Scab Detection: Insights from a Bibliometric Analysis

by Sarfraz Hussain, Ghulam Mustafa, Imran Haider Khan, Jiayuan Liu, Cheng Chen, Bingtao Hu, Min Chen, Iftikhar Ali and Yuhong Liu

Remote Sens. 2023, 15(13), 3431; https://doi.org/10.3390/rs15133431 - 06 Jul 2023

Cited by 2 | Viewed by 1337

Abstract

The study provides a comprehensive bibliometric analysis of imaging and non-imaging spectroscopy for wheat scab (INISWS) using CiteSpace. Therefore, we underpinned the developments of global INISWS detection at kernel, spike, and canopy scales, considering sensors, sensitive wavelengths, and algorithmic approaches. The study retrieved original articles from the Web of Science core collection (WOSCC) using a combination of advanced keyword searches related to INISWS. Afterward, visualization networks of author co-authorship, institution co-authorship, and country co-authorship were created to categorize the productive authors, countries, and institutions. Furthermore, the most significant authors and the core journals were identified by visualizing the journal co-citation, top research articles, document co-citation, and author co-citation networks. The investigation examined the major contributions of INISWS research at the micro, meso, and macro levels and highlighted the degree of collaboration between them and INISWS knowledge sources. Furthermore, it identifies the main research areas of INISWS and the current state of knowledge and provides future research directions. Moreover, an examination of grants and cooperating countries shows that the policy support from the People’s Republic of China, the United States of America, Germany, and Italy significantly benefits the progress of INISWS research. The co-occurrence analysis of keywords was carried out to highlight the new research frontiers and current hotspots. Lastly, the findings of kernel, spike, and canopy scales are presented regarding the best algorithmic, sensitive feature, and instrument techniques. Full article

(This article belongs to the Special Issue Monitoring, Early Warning, and Scientific Management of Vegetation Pests and Diseases)

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