1
|
Hu X, Li L, Huang J, Zeng Y, Zhang S, Su Y, Hong Y, Hong Z. Radar vegetation indices for monitoring surface vegetation: Developments, challenges, and trends. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 945:173974. [PMID: 38897467 DOI: 10.1016/j.scitotenv.2024.173974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 06/10/2024] [Accepted: 06/11/2024] [Indexed: 06/21/2024]
Abstract
Monitoring surface vegetation is essential for environmental protection, disaster prevention, and carbon sequestration in forests. However, optical remote-sensing methods and their derivative technologies typically fail to fully meet this requirement due to constraints such as lighting and weather. Radar vegetation indices (RVIs), developed based on microwave remote-sensing data, describe the dielectric properties and morphological structure of vegetation and have been applied for vegetation monitoring at various scales. This technical review is the first to systematically summarize RVIs; it analyzes and discusses their principles, developments, categories and applications, and provides a comprehensive guide for their use. Additionally, the challenges faced by RVIs, as well as their applicability, were analyzed, and future improvements and development trends were carefully projected. The selection of RVIs must consider the type of data used, the terrain and location of the study area, and the major vegetation types. The effectiveness of RVIs applied to vegetation monitoring can be affected by various factors, including index performance, sensor type, study area, and data type and quality. These factors reduce the reliability and robustness of results, as well as guide the improvement direction of RVIs. The development of technologies, such as artificial intelligence, in remote sensing offers new possibilities for RVIs, enabling the removal of background scattering, improvement in interpretation accuracy, and reduction in application thresholds. Additionally, the development trends in high resolution, multi-polarization, multi-base, multi-dimensional, and networked synthetic aperture radar (SAR) and their satellite platforms offer data support for the next generation of RVIs. The rapid development of RVIs strongly supports the use of surface vegetation monitoring and terrestrial ecosystem research.
Collapse
Affiliation(s)
- Xueqian Hu
- College of Land Science and Technology, China Agricultural University, Beijing 100083, China; Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
| | - Li Li
- College of Land Science and Technology, China Agricultural University, Beijing 100083, China; Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China.
| | - Jianxi Huang
- College of Land Science and Technology, China Agricultural University, Beijing 100083, China; Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
| | - Yelu Zeng
- College of Land Science and Technology, China Agricultural University, Beijing 100083, China; Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
| | - Shuo Zhang
- College of Land Science and Technology, China Agricultural University, Beijing 100083, China; Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
| | - Yiran Su
- College of Land Science and Technology, China Agricultural University, Beijing 100083, China; Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
| | - Yujiao Hong
- College of Land Science and Technology, China Agricultural University, Beijing 100083, China; Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
| | - Zixiang Hong
- College of Land Science and Technology, China Agricultural University, Beijing 100083, China; Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
| |
Collapse
|
2
|
Xia L, Zhang R, Chen L, Li L, Yi T, Chen M. Monitoring the leaf damage by the rice leafroller with deep learning and ultra-light UAV. PEST MANAGEMENT SCIENCE 2024. [PMID: 39264132 DOI: 10.1002/ps.8401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 03/27/2024] [Accepted: 08/20/2024] [Indexed: 09/13/2024]
Abstract
BACKGROUND Rice leafroller is a serious threat to the production of rice. Monitoring the damage caused by rice leafroller is essential for effective pest management. Owing to limitations in collecting decent quality images and high-performing identification methods to recognize the damage, studies recommending fast and accurate identification of rice leafroller damage are rare. In this study, we employed an ultra-lightweight unmanned aerial vehicle (UAV) to eliminate the influence of the downwash flow field and obtain very high-resolution images of the damaged areas of the rice leafroller. We used deep learning technology and the segmentation model, Attention U-Net, to recognize the damaged area by the rice leafroller. Further, a method is presented to count the damaged patches from the segmented area. RESULTS The result shows that Attention U-Net achieves high performance, with an F1 score of 0.908. Further analysis indicates that the deep learning model performs better than the traditional image classification method, Random Forest (RF). The traditional method of RF causes a lot of false alarms around the edge of leaves, and is sensitive to the changes in brightness. Validation based on the ground survey indicates that the UAV and deep learning-based method achieve a reasonable accuracy in identifying damage patches, with a coefficient of determination of 0.879. The spatial distribution of the damage is uneven, and the UAV-based image collecting method provides a dense and accurate method to recognize the damaged area. CONCLUSION Overall, this study presents a vision to monitor the damage caused by the rice leafroller with ultra-light UAV efficiently. It would also contribute to effectively controlling and managing the hazardous rice leafroller. © 2024 Society of Chemical Industry.
Collapse
Affiliation(s)
- Lang Xia
- National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Ruirui Zhang
- National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Liping Chen
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- National Center for International Research on Agricultural Aerial Application Technology, Beijing, China
| | - Longlong Li
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- National Center for International Research on Agricultural Aerial Application Technology, Beijing, China
| | - Tongchuan Yi
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- National Center for International Research on Agricultural Aerial Application Technology, Beijing, China
| | - Meixiang Chen
- National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| |
Collapse
|
3
|
Ma H, Zhang J, Huang W, Ruan C, Chen D, Zhang H, Zhou X, Gui Z. Monitoring yellow rust progression during spring critical wheat growth periods using multi-temporal Sentinel-2 imagery. PEST MANAGEMENT SCIENCE 2024. [PMID: 39139028 DOI: 10.1002/ps.8336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 07/03/2024] [Accepted: 07/12/2024] [Indexed: 08/15/2024]
Abstract
BACKGROUND Yellow rust (Puccinia striiformis f. sp. tritici) is a devastating hazard to wheat production, which poses a serious threat to yield and food security in the main wheat-producing areas in eastern China. It is necessary to monitor yellow rust progression during spring critical wheat growth periods to support its prediction by providing timely calibrations for disease prediction models and timely green prevention and control. RESULTS Three Sentinel-2 images for the disease during the three wheat growth periods (jointing, heading, and filling) were acquired. Spectral, texture, and color features were all extracted for each growth period disease. Then three period-specific feature sets were obtained. Given the differences in field disease epidemic status in the three periods, three period-targeted monitoring models were established to map yellow rust damage progression in spring and track its spatiotemporal change. The models' performance was then validated based on the disease field truth data during the three periods (87 for the jointing period, 183 for the heading period, and 155 for the filling period). The validation results revealed that the representation of the wheat yellow rust damage progression based on our monitoring model group was realistic and credible. The overall accuracy of the healthy and diseased pixel classification monitoring model at the jointing period reached 87.4%, and the coefficient of determination (R2) of the disease index regression monitoring models at the heading and filling periods was 0.77 (heading period) and 0.76 (filling period). The model-group-result-based spatiotemporal change detection of the yellow rust progression across the entire study area revealed that the area proportions conforming to the expected disease spatiotemporal development pattern during the jointing-to-heading period and the heading-to-filling period reached 98.2% and 84.4% respectively. CONCLUSIONS Our jointing, heading, and filling period-targeted monitoring model group overcomes the limitations of most existing monitoring models only based on single-phase remote sensing information. It performs well in revealing the wheat yellow rust spatiotemporal epidemic in spring, can timely update disease trends to optimize disease management, and provide a basis for disease prediction to timely correct model. © 2024 Society of Chemical Industry.
Collapse
Affiliation(s)
- Huiqin Ma
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Jingcheng Zhang
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Wenjiang Huang
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Earth Observation of Hainan Province, Hainan Research Institute, Aerospace Information Research Institute, Chinese Academy of Sciences, Sanya, China
| | - Chao Ruan
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei, China
| | - Dongmei Chen
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Hansu Zhang
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei, China
| | - Xianfeng Zhou
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Zhiqin Gui
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| |
Collapse
|
4
|
Tyagi A, Mir ZA, Ali S. Revisiting the Role of Sensors for Shaping Plant Research: Applications and Future Perspectives. SENSORS (BASEL, SWITZERLAND) 2024; 24:3261. [PMID: 38894052 PMCID: PMC11174810 DOI: 10.3390/s24113261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 05/14/2024] [Accepted: 05/18/2024] [Indexed: 06/21/2024]
Abstract
Plant health monitoring is essential for understanding the impact of environmental stressors (biotic and abiotic) on crop production, and for tailoring plant developmental and adaptive responses accordingly. Plants are constantly exposed to different stressors like pathogens and soil pollutants (heavy metals and pesticides) which pose a serious threat to their survival and to human health. Plants have the ability to respond to environmental stressors by undergoing rapid transcriptional, translational, and metabolic reprogramming at different cellular compartments in order to balance growth and adaptive responses. However, plants' exceptional responsiveness to environmental cues is highly complex, which is driven by diverse signaling molecules such as calcium Ca2+, reactive oxygen species (ROS), hormones, small peptides and metabolites. Additionally, other factors like pH also influence these responses. The regulation and occurrence of these plant signaling molecules are often undetectable, necessitating nondestructive, live research approaches to understand their molecular complexity and functional traits during growth and stress conditions. With the advent of sensors, in vivo and in vitro understanding of some of these processes associated with plant physiology, signaling, metabolism, and development has provided a novel platform not only for decoding the biochemical complexity of signaling pathways but also for targeted engineering to improve diverse plant traits. The application of sensors in detecting pathogens and soil pollutants like heavy metal and pesticides plays a key role in protecting plant and human health. In this review, we provide an update on sensors used in plant biology for the detection of diverse signaling molecules and their functional attributes. We also discuss different types of sensors (biosensors and nanosensors) used in agriculture for detecting pesticides, pathogens and pollutants.
Collapse
Affiliation(s)
- Anshika Tyagi
- Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Republic of Korea
| | - Zahoor Ahmad Mir
- Department of Plant Science and Agriculture, University of Manitoba, Winnipeg, MB R2M0TB, Canada;
| | - Sajad Ali
- Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Republic of Korea
| |
Collapse
|
5
|
Zhang T, Wang D. Classification of crop disease-pest questions based on BERT-BiGRU-CapsNet with attention pooling. FRONTIERS IN PLANT SCIENCE 2023; 14:1300580. [PMID: 38143585 PMCID: PMC10740160 DOI: 10.3389/fpls.2023.1300580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 11/22/2023] [Indexed: 12/26/2023]
Abstract
Crop disease-pest question classification is an essential part of pest knowledge intelligent question answering system. A crop disease-pest question classification method is proposed on the basis of bidirectional encoder representations from transformers (BERT), bidirectional gated unit (BiGRU), capsule network (CapsNet), and BERT-BiGRU-CapsNet with attention pooling (BBGCAP). In BBGCAP, the unstructured text data are preprocessed vectorically using BERT, BiGRU is used to extract the deep features of the text, attention pooling is used to assign the corresponding weights to the extracted deep information, and CapsNet is used to route the right alternative. BBGCAP is a synthetic model by integrating the advantages of BERT, BiGRU, CapsNet, and attention pooling. The experimental results on the cucumber-pest question database show that the proposed method is superior to the methods based on traditional template matching, support vector machines (SVM), and convolutional neural network-long short-term memory (LSTM), and the accuracy rates of precision, recall, and F1 are all above 902.15%. This method provides technical support for intelligent question answering system of crop disease-pests.
Collapse
Affiliation(s)
- Ting Zhang
- College of Computing, Xijing University, Xi’an, China
| | | |
Collapse
|
6
|
Prechsl UE, Mejia-Aguilar A, Cullinan CB. In vivo spectroscopy and machine learning for the early detection and classification of different stresses in apple trees. Sci Rep 2023; 13:15857. [PMID: 37739998 PMCID: PMC10517117 DOI: 10.1038/s41598-023-42428-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 09/10/2023] [Indexed: 09/24/2023] Open
Abstract
The use of in vivo spectroscopy to detect plant stress in its early stages has the potential to enhance food safety and reduce the need for plant protection products. However, differentiating between various stress types before symptoms appear remains poorly studied. In this study, we investigated the potential of Vis-NIR spectroscopy to differentiate between stress types in apple trees (Malus x domestica Borkh.) exposed to apple scab, waterlogging, and herbicides in a greenhouse. Using a spectroradiometer, we collected spectral signatures of leaves still attached to the tree and utilized machine learning techniques to develop predictive models for detecting stress presence and classifying stress type as early as 1-5 days after exposure. Our findings suggest that changes in spectral reflectance at multiple regions accurately differentiate various types of plant stress on apple trees. Our models were highly accurate (accuracies between 0.94 and 1) when detecting the general presence of stress at an early stage. The wavelengths important for classification relate to photosynthesis via pigment functioning (684 nm) and leaf water (~ 1800-1900 nm), which may be associated with altered gas exchange as a short-term stress response. Overall, our study demonstrates the potential of spectral technology and machine learning for early diagnosis of plant stress, which could lead to reduced environmental burden through optimizing resource utilization in agriculture.
Collapse
Affiliation(s)
- Ulrich E Prechsl
- Laimburg Research Centre, Laimburg 6, 39040, Auer, South Tyrol, Italy.
| | | | - Cameron B Cullinan
- Laimburg Research Centre, Laimburg 6, 39040, Auer, South Tyrol, Italy
- Faculty of Agricultural, Environmental and Food Sciences, Free University of Bolzano, Piazza Università 1, 39100, Bolzano, South Tyrol, Italy
| |
Collapse
|
7
|
Cheseto X, Rering CC, Broadhead GT, Torto B, Beck JJ. Early infestation volatile biomarkers of fruit fly Bactrocera dorsalis (Hendel) ovipositional activity in mango (Mangifera indica L.). PHYTOCHEMISTRY 2023; 206:113519. [PMID: 36462541 DOI: 10.1016/j.phytochem.2022.113519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/18/2022] [Accepted: 11/19/2022] [Indexed: 06/17/2023]
Abstract
Infestation of agricultural commodities by insect pests results in significant economic, import and export, food safety, and invasive insect introduction issues for growers, consumers, and inspectors. The Oriental fruit fly (Bactrocera dorsalis) is considered a highly invasive insect pest with populations reported in more than 60 countries, with prevalent distributions in Asia and Africa. B. dorsalis is phytophagous with a host range encompassing hundreds of fruits and vegetables. Damage to the fruit or vegetable is inflicted through oviposition and subsequent larval feeding resulting in spoilage. Early detection of insect pest infestations is a critical component for ensuring food safety as well as controlling introduction and spread of invasive insects. However, detection of ovipositional activity and early larval development is visually difficult, thus rapid and non-destructive detection often relies on odors associated with infestation. We investigated the odors of mangoes (Mangifera indica L.) infested with B. dorsalis and compared the volatile profiles of infested mangoes to non-infested and mechanically damaged mangoes 24 h post-infestation. GC-MS and multivariate analyses provided the identification of eleven compounds unique to infested mangoes compared to mechanically damaged or non-infested fruit. Results indicated compositional and quantitative differentiation of volatile profiles among treatments for detection of infested fruit at quality checks or points of commerce.
Collapse
Affiliation(s)
- Xavier Cheseto
- International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772, 00100, Nairobi, Kenya
| | - Caitlin C Rering
- Chemistry Research Unit, Center for Medical, Agricultural and Veterinary Entomology, Agricultural Research Service, U.S. Department of Agriculture, 1700 SW 23rd Drive, Gainesville, FL, 32608, United States
| | - Geoffrey T Broadhead
- Chemistry Research Unit, Center for Medical, Agricultural and Veterinary Entomology, Agricultural Research Service, U.S. Department of Agriculture, 1700 SW 23rd Drive, Gainesville, FL, 32608, United States
| | - Baldwyn Torto
- International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772, 00100, Nairobi, Kenya
| | - John J Beck
- Chemistry Research Unit, Center for Medical, Agricultural and Veterinary Entomology, Agricultural Research Service, U.S. Department of Agriculture, 1700 SW 23rd Drive, Gainesville, FL, 32608, United States.
| |
Collapse
|
8
|
MA X, LIAO J, ZHAO J, XI L. Knowledge mapping of research on spectral technology in the fruit field using CiteSpace (1981-2021). FOOD SCIENCE AND TECHNOLOGY 2023. [DOI: 10.1590/fst.116622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
|
9
|
Ribeiro AV, Cira TM, MacRae IV, Koch RL. Effects of feeding injury from Popillia japonica (Coleoptera: Scarabaeidae) on soybean spectral reflectance and yield. FRONTIERS IN INSECT SCIENCE 2022; 2:1006092. [PMID: 38468790 PMCID: PMC10926407 DOI: 10.3389/finsc.2022.1006092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 10/05/2022] [Indexed: 03/13/2024]
Abstract
Remote sensing has been shown to be a promising technology for the detection and monitoring of plant stresses including insect feeding. Popillia japonica Newman, is an invasive insect species in the United States, and a pest of concern to soybean, Glycine max (L.) Merr., in the upper Midwest. To investigate the effects of P. japonica feeding injury (i.e., defoliation) on soybean canopy spectral reflectance and yield, field trials with plots of caged soybean plants were established during the summers of 2020 and 2021. In each year, field-collected P. japonica adults were released into some of the caged plots, creating a gradient of infestation levels and resulting injury. Estimates of injury caused by P. japonica, ground-based hyperspectral readings, total yield, and yield components were obtained from the caged plots. Injury was greatest in the upper canopy of soybean in plots infested with P. japonica. Overall mean canopy injury (i.e., across lower, middle, and upper canopy) ranged from 0.23 to 6.26%, which is representative of injury levels observed in soybean fields in the Midwest United States. Feeding injury from P. japonica tended to reduce measures of soybean canopy reflectance in near infra-red wavelengths (~700 to 1000 nm). These results indicate that remote sensing has potential for detection of injury from P. japonica and could facilitate scouting for this pest. Effects of P. japonica injury on total yield were not observed, but a reduction in seed size was detected in one of the two years. The threat to soybean yield posed by P. japonica alone appears minimal, but this pest adds to the guild of other defoliating insects in soybean whose combined effects could threaten yield. The results of this research will guide refinement of management recommendations for this pest in soybean and hold relevance for other cropping systems.
Collapse
Affiliation(s)
- Arthur V. Ribeiro
- Department of Entomology, University of Minnesota, Saint Paul, MN, United States
| | - Theresa M. Cira
- Department of Entomology, University of Minnesota, Saint Paul, MN, United States
| | - Ian V. MacRae
- Department of Entomology, University of Minnesota, Northwest Research and Outreach Center, Crookston, MN, United States
| | - Robert L. Koch
- Department of Entomology, University of Minnesota, Saint Paul, MN, United States
| |
Collapse
|
10
|
Shen Y, Wang X, Song X, Xu H. Regional intensity of biological disasters in farmland: quantitative assessment and spatiotemporal analysis. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:67402-67417. [PMID: 35522412 DOI: 10.1007/s11356-022-20497-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 04/24/2022] [Indexed: 06/14/2023]
Abstract
Biological disasters in farmland have become a serious threat to global sustainability. Quantitative studies on spatiotemporal change in regional intensity of biological disasters in farmland (RIBDF) are crucial for promoting sustainable intensification of farmland use and global food security. Many studies have revealed the impacts of natural environment on biological disasters in farmland. However, research on the impacts of farmers' activities on biological disasters remains very limited from the perspective of induced substitution in agricultural production. Based on the principle of induced substitution in agricultural production, a theoretical framework for the impacts of farmland use intensity (FUI), the occurrence intensity of biological disasters (OIBD), the natural loss intensity resulting from biological disasters (NLIBD), and the actual loss intensity controlled by human activities (ALIBD) on change in RIBDF was presented, and we therefore established an assessment model integrating these four key indices in this study. Taking Guangdong Province in China as the study area, this study analyzed the spatiotemporal changes in RIBDF from 1996 to 2017 and found that RIBDF increased overall with three change stages of slow growth, rapid growth, and decline at the provincial and regional levels. Moreover, the gravity center of RIBDF appeared to shift towards the coastal region in southwestern Guangdong at a speed of 18.20 km per year. The results reveal that the spatiotemporal change in RIBDF is determined by the key influencing factors including the substitution of chemical fertilizers for farmland and crop substitution. These findings suggest that it is necessary to expand the scale of farmland management and to encourage farmers to implement diversified cropping as well as chemical fertilizers reduction. The foremost contribution of this study is its exploration of an understanding of the linkage between farmland use activities characterized as induced substitution in agricultural production and biological disasters. Moreover, policy implications for sustainable intensification of farmland use were discussed based on these findings.
Collapse
Affiliation(s)
- Yajing Shen
- Research Center for Spatial Planning and Human-Environment System Simulation, School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, People's Republic of China
| | - Xiong Wang
- Research Center for Spatial Planning and Human-Environment System Simulation, School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, People's Republic of China
| | - Xiaoqing Song
- Research Center for Spatial Planning and Human-Environment System Simulation, School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, People's Republic of China.
- Hunan Key Laboratory of Land Resources Evaluation and Utilization, Hunan Planning Institute of Land and Resources, Changsha, 410007, People's Republic of China.
| | - Huixiao Xu
- Research Center for Spatial Planning and Human-Environment System Simulation, School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, People's Republic of China
| |
Collapse
|
11
|
Li S, Feng Z, Yang B, Li H, Liao F, Gao Y, Liu S, Tang J, Yao Q. An intelligent monitoring system of diseases and pests on rice canopy. FRONTIERS IN PLANT SCIENCE 2022; 13:972286. [PMID: 36035691 PMCID: PMC9403268 DOI: 10.3389/fpls.2022.972286] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Accepted: 07/25/2022] [Indexed: 05/24/2023]
Abstract
Accurate and timely surveys of rice diseases and pests are important to control them and prevent the reduction of rice yields. The current manual survey method of rice diseases and pests is time-consuming, laborious, highly subjective and difficult to trace historical data. To address these issues, we developed an intelligent monitoring system for detecting and identifying the disease and pest lesions on the rice canopy. The system mainly includes a network camera, an intelligent detection model of diseases and pests on rice canopy, a web client and a server. Each camera of the system can collect rice images in about 310 m2 of paddy fields. An improved model YOLO-Diseases and Pests Detection (YOLO-DPD) was proposed to detect three lesions of Cnaphalocrocis medinalis, Chilo suppressalis, and Ustilaginoidea virens on rice canopy. The residual feature augmentation method was used to narrow the semantic gap between different scale features of rice disease and pest images. The convolution block attention module was added into the backbone network to enhance the regional disease and pest features for suppressing the background noises. Our experiments demonstrated that the improved model YOLO-DPD could detect three species of disease and pest lesions on rice canopy at different image scales with an average precision of 92.24, 87.35 and 90.74%, respectively, and a mean average precision of 90.11%. Compared to RetinaNet, Faster R-CNN and Yolov4 models, the mean average precision of YOLO-DPD increased by 18.20, 6.98, 6.10%, respectively. The average detection time of each image is 47 ms. Our system has the advantages of unattended operation, high detection precision, objective results, and data traceability.
Collapse
Affiliation(s)
- Suxuan Li
- School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China
| | - Zelin Feng
- School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China
| | - Baojun Yang
- State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou, China
| | - Hang Li
- School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China
| | - Fubing Liao
- School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China
| | - Yufan Gao
- School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China
| | - Shuhua Liu
- State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou, China
| | - Jian Tang
- State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou, China
| | - Qing Yao
- School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China
| |
Collapse
|
12
|
Improved CNN Method for Crop Pest Identification Based on Transfer Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9709648. [PMID: 35341164 PMCID: PMC8942633 DOI: 10.1155/2022/9709648] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 02/17/2022] [Accepted: 02/21/2022] [Indexed: 11/17/2022]
Abstract
Timely treatment and elimination of diseases and pests can effectively improve the yield and quality of crops, but the current identification methods are difficult to achieve efficient and accurate research and analysis of diseases and pests. To solve this problem, this study proposes a crop pest identification method based on a multilayer network model. First, the method provides a reliable sample dataset for the recognition model through image data enhancement and other operations; the corresponding pest image recognition and analysis model is constructed based on VGG16 and Inception-ResNet-v2 transfer learning network to ensure the completeness of the recognition and analysis model; then, using the idea of an integrated algorithm, the two improved CNN series pest image recognition and analysis models are effectively fused to improve the accuracy of the model for crop pest recognition and classification. The simulation analysis is realized based on the IDADP dataset. The experimental results show that the accuracy of the proposed method for pest identification is 97.71%, which improves the poor identification effect of the current method.
Collapse
|
13
|
Al Ktash M, Stefanakis M, Englert T, Drechsel MSL, Stiedl J, Green S, Jacob T, Boldrini B, Ostertag E, Rebner K, Brecht M. UV Hyperspectral Imaging as Process Analytical Tool for the Characterization of Oxide Layers and Copper States on Direct Bonded Copper. SENSORS 2021; 21:s21217332. [PMID: 34770640 PMCID: PMC8588143 DOI: 10.3390/s21217332] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 11/01/2021] [Accepted: 11/03/2021] [Indexed: 11/25/2022]
Abstract
Hyperspectral imaging and reflectance spectroscopy in the range from 200–380 nm were used to rapidly detect and characterize copper oxidation states and their layer thicknesses on direct bonded copper in a non-destructive way. Single-point UV reflectance spectroscopy, as a well-established method, was utilized to compare the quality of the hyperspectral imaging results. For the laterally resolved measurements of the copper surfaces an UV hyperspectral imaging setup based on a pushbroom imager was used. Six different types of direct bonded copper were studied. Each type had a different oxide layer thickness and was analyzed by depth profiling using X-ray photoelectron spectroscopy. In total, 28 samples were measured to develop multivariate models to characterize and predict the oxide layer thicknesses. The principal component analysis models (PCA) enabled a general differentiation between the sample types on the first two PCs with 100.0% and 96% explained variance for UV spectroscopy and hyperspectral imaging, respectively. Partial least squares regression (PLS-R) models showed reliable performance with R2c = 0.94 and 0.94 and RMSEC = 1.64 nm and 1.76 nm, respectively. The developed in-line prototype system combined with multivariate data modeling shows high potential for further development of this technique towards real large-scale processes.
Collapse
Affiliation(s)
- Mohammad Al Ktash
- Process Analysis and Technology PA & T, Reutlingen University, Alteburgstraße 150, 72762 Reutlingen, Germany; (M.A.K.); (M.S.); (M.S.L.D.); (B.B.); (E.O.); (K.R.)
- Institute of Physical and Theoretical Chemistry, Eberhard Karls University Tübingen, Auf der Morgenstelle 18, 72076 Tübingen, Germany
| | - Mona Stefanakis
- Process Analysis and Technology PA & T, Reutlingen University, Alteburgstraße 150, 72762 Reutlingen, Germany; (M.A.K.); (M.S.); (M.S.L.D.); (B.B.); (E.O.); (K.R.)
- Institute of Physical and Theoretical Chemistry, Eberhard Karls University Tübingen, Auf der Morgenstelle 18, 72076 Tübingen, Germany
| | - Tim Englert
- Robert Bosch GmbH, Automotive Electronics, Postfach 1342, 72703 Reutlingen, Germany; (T.E.); (J.S.); (S.G.)
- Institute of Electrochemistry, Ulm University, Albert-Einstein-Allee 47, 89081 Ulm, Germany;
| | - Maryam S. L. Drechsel
- Process Analysis and Technology PA & T, Reutlingen University, Alteburgstraße 150, 72762 Reutlingen, Germany; (M.A.K.); (M.S.); (M.S.L.D.); (B.B.); (E.O.); (K.R.)
| | - Jan Stiedl
- Robert Bosch GmbH, Automotive Electronics, Postfach 1342, 72703 Reutlingen, Germany; (T.E.); (J.S.); (S.G.)
| | - Simon Green
- Robert Bosch GmbH, Automotive Electronics, Postfach 1342, 72703 Reutlingen, Germany; (T.E.); (J.S.); (S.G.)
| | - Timo Jacob
- Institute of Electrochemistry, Ulm University, Albert-Einstein-Allee 47, 89081 Ulm, Germany;
| | - Barbara Boldrini
- Process Analysis and Technology PA & T, Reutlingen University, Alteburgstraße 150, 72762 Reutlingen, Germany; (M.A.K.); (M.S.); (M.S.L.D.); (B.B.); (E.O.); (K.R.)
| | - Edwin Ostertag
- Process Analysis and Technology PA & T, Reutlingen University, Alteburgstraße 150, 72762 Reutlingen, Germany; (M.A.K.); (M.S.); (M.S.L.D.); (B.B.); (E.O.); (K.R.)
| | - Karsten Rebner
- Process Analysis and Technology PA & T, Reutlingen University, Alteburgstraße 150, 72762 Reutlingen, Germany; (M.A.K.); (M.S.); (M.S.L.D.); (B.B.); (E.O.); (K.R.)
| | - Marc Brecht
- Process Analysis and Technology PA & T, Reutlingen University, Alteburgstraße 150, 72762 Reutlingen, Germany; (M.A.K.); (M.S.); (M.S.L.D.); (B.B.); (E.O.); (K.R.)
- Institute of Physical and Theoretical Chemistry, Eberhard Karls University Tübingen, Auf der Morgenstelle 18, 72076 Tübingen, Germany
- Correspondence:
| |
Collapse
|
14
|
Rhodes MW, Bennie JJ, Spalding A, Ffrench-Constant RH, Maclean IMD. Recent advances in the remote sensing of insects. Biol Rev Camb Philos Soc 2021; 97:343-360. [PMID: 34609062 DOI: 10.1111/brv.12802] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 09/21/2021] [Accepted: 09/23/2021] [Indexed: 12/31/2022]
Abstract
Remote sensing has revolutionised many aspects of ecological research, enabling spatiotemporal data to be collected in an efficient and highly automated manner. The last two decades have seen phenomenal growth in capabilities for high-resolution remote sensing that increasingly offers opportunities to study small, but ecologically important organisms, such as insects. Here we review current applications for using remote sensing within entomological research, highlighting the emerging opportunities that now arise through advances in spatial, temporal and spectral resolution. Remote sensing can be used to map environmental variables, such as habitat, microclimate and light pollution, capturing data on topography, vegetation structure and composition, and luminosity at spatial scales appropriate to insects. Such data can also be used to detect insects indirectly from the influences that they have on the environment, such as feeding damage or nest structures, whilst opportunities for directly detecting insects are also increasingly available. Entomological radar and light detection and ranging (LiDAR), for example, are transforming our understanding of aerial insect abundance and movement ecology, whilst ultra-high spatial resolution drone imagery presents tantalising new opportunities for direct observation. Remote sensing is rapidly developing into a powerful toolkit for entomologists, that we envisage will soon become an integral part of insect science.
Collapse
Affiliation(s)
- Marcus W Rhodes
- Environment and Sustainability Institute, University of Exeter Penryn Campus, Penryn, Cornwall, TR10 9FE, U.K
| | - Jonathan J Bennie
- Centre for Geography and Environmental Science, University of Exeter Penryn Campus, Penryn, Cornwall, TR10 9FE, U.K
| | - Adrian Spalding
- Spalding Associates (Environmental) Ltd, 10 Walsingham Place, Truro, Cornwall, TR1 2RP, U.K
| | - Richard H Ffrench-Constant
- Centre for Ecology and Conservation, University of Exeter Penryn Campus, Penryn, Cornwall, TR10 9FE, U.K
| | - Ilya M D Maclean
- Environment and Sustainability Institute, University of Exeter Penryn Campus, Penryn, Cornwall, TR10 9FE, U.K
| |
Collapse
|
15
|
Characterization of Pharmaceutical Tablets Using UV Hyperspectral Imaging as a Rapid In-Line Analysis Tool. SENSORS 2021; 21:s21134436. [PMID: 34203526 PMCID: PMC8271527 DOI: 10.3390/s21134436] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 06/23/2021] [Accepted: 06/25/2021] [Indexed: 11/26/2022]
Abstract
A laboratory prototype for hyperspectral imaging in ultra-violet (UV) region from 225 to 400 nm was developed and used to rapidly characterize active pharmaceutical ingredients (API) in tablets. The APIs are ibuprofen (IBU), acetylsalicylic acid (ASA) and paracetamol (PAR). Two sample sets were used for a comparison purpose. Sample set one comprises tablets of 100% API and sample set two consists of commercially available painkiller tablets. Reference measurements were performed on the pure APIs in liquid solutions (transmission) and in solid phase (reflection) using a commercial UV spectrometer. The spectroscopic part of the prototype is based on a pushbroom imager that contains a spectrograph and charge-coupled device (CCD) camera. The tablets were scanned on a conveyor belt that is positioned inside a tunnel made of polytetrafluoroethylene (PTFE) in order to increase the homogeneity of illumination at the sample position. Principal component analysis (PCA) was used to differentiate the hyperspectral data of the drug samples. The first two PCs are sufficient to completely separate all samples. The rugged design of the prototype opens new possibilities for further development of this technique towards real large-scale application.
Collapse
|
16
|
Assessment of Poplar Looper (Apocheima cinerarius Erschoff) Infestation on Euphrates (Populus euphratica) Using Time-Series MODIS NDVI Data Based on the Wavelet Transform and Discriminant Analysis. REMOTE SENSING 2021. [DOI: 10.3390/rs13122345] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Poplar looper (Apocheima cinerarius Erschoff) is a destructive insect infesting Euphrates or desert poplars (Populus euphratica) in Xinjiang, China. Since the late 1950s, it has been plaguing desert poplars in the Tarim Basin in Xinjiang and caused widespread damages. This paper presents an approach to the detection of poplar looper infestations on desert poplars and the assessment of the severity of the infestations using time-series MODIS NDVI data via the wavelet transform and discriminant analysis, using the middle and lower reaches of the Yerqiang River as a case study. We first applied the wavelet transform to the NDVI time series data in the period of 2009–2014 for the study area, which decomposed the data into a representation that shows detailed NDVI changes and trends as a function of time. This representation captures both intra- and inter-annual changes in the data, some of which characterise transient events. The decomposed components were then used to filter out details of the changes to create a smoothed NDVI time series that represent the phenology of healthy desert poplars. Next the subset of the original NDVI time series spanning the time period when the pest was active was extracted and added to the smoothed time series to generate a blended time series. The wavelet transform was applied again to decompose the blended time series to enhance and identify the changes in the data that may represent the signals of the pest infestations. Based on the amplitude of the enhanced pest infestation signals, a predictive model was developed via discriminant analysis to detect the pest infestation and assess its severity. The predictive model achieved a severity classification accuracy of 91.7% and 94.37% accuracy in detecting the time of the outbreak. The methodology presented in this paper provides a fast, precise, and practical method for monitoring pest outbreak in dense desert poplar forests, which can be used to support the surveillance and control of poplar looper infestations on desert poplars. It is of great significance to the conservation of the desert ecological environment.
Collapse
|
17
|
Levine M. Fluorescence-Based Sensing of Pesticides Using Supramolecular Chemistry. Front Chem 2021; 9:616815. [PMID: 33937184 PMCID: PMC8085505 DOI: 10.3389/fchem.2021.616815] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 01/11/2021] [Indexed: 01/02/2023] Open
Abstract
The detection of pesticides in real-world environments is a high priority for a broad range of applications, including in areas of public health, environmental remediation, and agricultural sustainability. While many methods for pesticide detection currently exist, the use of supramolecular fluorescence-based methods has significant practical advantages. Herein, we will review the use of fluorescence-based pesticide detection methods, with a particular focus on supramolecular chemistry-based methods. Illustrative examples that show how such methods have achieved success in real-world environments are also included, as are areas highlighted for future research and development.
Collapse
Affiliation(s)
- Mindy Levine
- Ariel University, Department of Chemical Sciences, Ariel, Israel
| |
Collapse
|
18
|
Application of Remote Sensing Data for Locust Research and Management-A Review. INSECTS 2021; 12:insects12030233. [PMID: 33803360 PMCID: PMC8002081 DOI: 10.3390/insects12030233] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 02/10/2021] [Accepted: 02/25/2021] [Indexed: 11/17/2022]
Abstract
Simple Summary Locust outbreaks around the world regularly affect vast areas and millions of people. Mapping and monitoring locust habitats, as well as prediction of locust outbreaks is essential to minimize the damage on crops and pasture. In this context, remote sensing has become one of the most important data sources for effective locust management. This review paper summarizes remote sensing-based studies for locust management and research over the past four decades and reveals progress made and gaps for further research. We quantify which locust species, regions of interest, sensor data and variables were mainly used and which thematic foci were of interest. Our review shows that most studies were conducted for the desert locust, the migratory locust and Australian plague locust and corresponding areas of interest. Remote sensing studies for other destructive locust species are rather rare. Most studies utilized data from optical sensors to derive NDVI and land cover for mapping and monitoring the locust habitats. Furthermore, temperature, precipitation and soil moisture are derived from thermal infrared, passive and active radar sensors. Applications of the European Sentinel fleet, entire Landsat archive or very-high-spatial-resolution data are rare. Implementing new methods (e.g., data fusion) and additional data sources could provide new insights for locust research and management. Abstract Recently, locust outbreaks around the world have destroyed agricultural and natural vegetation and caused massive damage endangering food security. Unusual heavy rainfalls in habitats of the desert locust (Schistocerca gregaria) and lack of monitoring due to political conflicts or inaccessibility of those habitats lead to massive desert locust outbreaks and swarms migrating over the Arabian Peninsula, East Africa, India and Pakistan. At the same time, swarms of the Moroccan locust (Dociostaurus maroccanus) in some Central Asian countries and swarms of the Italian locust (Calliptamus italicus) in Russia and China destroyed crops despite developed and ongoing monitoring and control measurements. These recent events underline that the risk and damage caused by locust pests is as present as ever and affects 100 million of human lives despite technical progress in locust monitoring, prediction and control approaches. Remote sensing has become one of the most important data sources in locust management. Since the 1980s, remote sensing data and applications have accompanied many locust management activities and contributed to an improved and more effective control of locust outbreaks and plagues. Recently, open-access remote sensing data archives as well as progress in cloud computing provide unprecedented opportunity for remote sensing-based locust management and research. Additionally, unmanned aerial vehicle (UAV) systems bring up new prospects for a more effective and faster locust control. Nevertheless, the full capacity of available remote sensing applications and possibilities have not been exploited yet. This review paper provides a comprehensive and quantitative overview of international research articles focusing on remote sensing application for locust management and research. We reviewed 110 articles published over the last four decades, and categorized them into different aspects and main research topics to summarize achievements and gaps for further research and application development. The results reveal a strong focus on three species—the desert locust, the migratory locust (Locusta migratoria), and the Australian plague locust (Chortoicetes terminifera)—and corresponding regions of interest. There is still a lack of international studies for other pest species such as the Italian locust, the Moroccan locust, the Central American locust (Schistocerca piceifrons), the South American locust (Schistocerca cancellata), the brown locust (Locustana pardalina) and the red locust (Nomadacris septemfasciata). In terms of applied sensors, most studies utilized Advanced Very-High-Resolution Radiometer (AVHRR), Satellite Pour l’Observation de la Terre VEGETATION (SPOT-VGT), Moderate-Resolution Imaging Spectroradiometer (MODIS) as well as Landsat data focusing mainly on vegetation monitoring or land cover mapping. Application of geomorphological metrics as well as radar-based soil moisture data is comparably rare despite previous acknowledgement of their importance for locust outbreaks. Despite great advance and usage of available remote sensing resources, we identify several gaps and potential for future research to further improve the understanding and capacities of the use of remote sensing in supporting locust outbreak- research and management.
Collapse
|
19
|
A Review of Insect Monitoring Approaches with Special Reference to Radar Techniques. SENSORS 2021; 21:s21041474. [PMID: 33672508 PMCID: PMC7923785 DOI: 10.3390/s21041474] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 02/09/2021] [Accepted: 02/14/2021] [Indexed: 12/02/2022]
Abstract
Drastic declines in insect populations are a vital concern worldwide. Despite widespread insect monitoring, the significant gaps in the literature must be addressed. Future monitoring techniques must be systematic and global. Advanced technologies and computer solutions are needed. We provide here a review of relevant works to show the high potential for solving the aforementioned problems. Major historical and modern methods of insect monitoring are considered. All major radar solutions are carefully reviewed. Insect monitoring with radar is a well established technique, but it is still a fast-growing topic. The paper provides an updated classification of insect radar sets. Three main groups of insect radar solutions are distinguished: scanning, vertical-looking, and harmonic. Pulsed radar sets are utilized for all three groups, while frequency-modulated continuous-wave (FMCW) systems are applied only for vertical-looking and harmonic insect radar solutions. This work proves the high potential of radar entomology based on the growing research interest, along with the emerging novel setups, compact devices, and data processing approaches. The review exposes promising insect monitoring solutions using compact radar instruments. The proposed compact and resource-effective setups can be very beneficial for systematic insect monitoring.
Collapse
|
20
|
Detection of Defoliation Injury in Peanut with Hyperspectral Proximal Remote Sensing. REMOTE SENSING 2020. [DOI: 10.3390/rs12223828] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Remote sensing can be applied to optimize efficiency in pest detection, as an insect sampling tool. This efficiency can result in more precise recommendations for decision making in pest management. Pest detection with remote sensing is often feasible because plant biotic stress caused by herbivory triggers a defensive physiological response in plants, which generally results in changes to leaf reflectance. Therefore, the key objective of this study was to use hyperspectral proximal remote sensing and gas exchange parameters to characterize peanut leaf responses to herbivory by Stegasta bosqueella (Lepidoptera: Gelechiidae) and Spodoptera cosmioides (Lepidoptera: Noctuidae), two major pests in South American peanut (Arachis hypogaea) production. The experiment was conducted in a randomized complete block design with a 2 × 3 factorial scheme (two lepidopterous species and 3 categories of injury). The injury treatments were: (1) natural infestation by third instars of S. bosqueella, (2) natural infestation by third instars of S. cosmioides, and (3) simulation of injury with scissors to mimic larval injury. We verified that peanut leaf reflectance is different between herbivory by the two larval species, but similar among real and simulated defoliation. Similarly, we observed differences in photosynthetic rate, stomatal conductance, transpiration, and photosynthetic water use efficiency only between species but not between real and simulated larval defoliation. Our results provide information that is essential for the development of sampling and economic thresholds of S. bosqueella and S. cosmioides on the peanut.
Collapse
|