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Danilov R, Kremneva O, Sereda I, Gasiyan K, Zimin M, Istomin D, Pachkin A. Study of the Spectral Characteristics of Crops of Winter Wheat Varieties Infected with Pathogens of Leaf Diseases. PLANTS (BASEL, SWITZERLAND) 2024; 13:1892. [PMID: 39065419 PMCID: PMC11280424 DOI: 10.3390/plants13141892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2024] [Revised: 07/06/2024] [Accepted: 07/07/2024] [Indexed: 07/28/2024]
Abstract
Studying the influence of the host plant genotype on the spectral reflectance of crops infected by a pathogen is one of the key directions in the development of precision methods for monitoring the phytosanitary state of wheat agrocenoses. The purpose of this research was to study the influence of varietal factors and disease development on the spectral characteristics of winter wheat varieties of different susceptibility to diseases during the growing seasons of 2021, 2022 and 2023. The studied winter wheat crops were represented by three varieties differing in susceptibility to phytopathogens: Grom, Svarog and Bezostaya 100. Over three years of research, a clear and pronounced influence of the varietal factor on the spectral characteristics of winter wheat crops was observed, which in most cases manifested itself as an immunological reaction of specific varieties to the influence of pathogen development. The nature of the influence of the pathogenic background and the spectral characteristics of winter wheat crops were determined by the complex interaction of the development of individual diseases under the conditions of a particular year of research. A uniform and clear division of the spectral characteristics of winter wheat according to the intensity of the disease was recorded only at a level of pathogen development of more than 5%. Moreover, this gradation was most clearly manifested in the spectral channels of the near-infrared range and at a wavelength of 720 nm.
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Affiliation(s)
- Roman Danilov
- Federal State Budgetary Scientific Institution «Federal Research Center of Biological Plant Protection» (FSBSI FRCBPP), Krasnodar 350039, Russia; (K.G.); (D.I.); (A.P.)
| | - Oksana Kremneva
- Federal State Budgetary Scientific Institution «Federal Research Center of Biological Plant Protection» (FSBSI FRCBPP), Krasnodar 350039, Russia; (K.G.); (D.I.); (A.P.)
| | - Igor Sereda
- Department of Cartography and Geoinformatics, Faculty of Geography, M. V. Lomonosov Moscow State University, Moscow 119991, Russia; (I.S.); (M.Z.)
| | - Ksenia Gasiyan
- Federal State Budgetary Scientific Institution «Federal Research Center of Biological Plant Protection» (FSBSI FRCBPP), Krasnodar 350039, Russia; (K.G.); (D.I.); (A.P.)
| | - Mikhail Zimin
- Department of Cartography and Geoinformatics, Faculty of Geography, M. V. Lomonosov Moscow State University, Moscow 119991, Russia; (I.S.); (M.Z.)
| | - Dmitry Istomin
- Federal State Budgetary Scientific Institution «Federal Research Center of Biological Plant Protection» (FSBSI FRCBPP), Krasnodar 350039, Russia; (K.G.); (D.I.); (A.P.)
| | - Alexey Pachkin
- Federal State Budgetary Scientific Institution «Federal Research Center of Biological Plant Protection» (FSBSI FRCBPP), Krasnodar 350039, Russia; (K.G.); (D.I.); (A.P.)
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Matese A, Prince Czarnecki JM, Samiappan S, Moorhead R. Are unmanned aerial vehicle-based hyperspectral imaging and machine learning advancing crop science? TRENDS IN PLANT SCIENCE 2024; 29:196-209. [PMID: 37802693 DOI: 10.1016/j.tplants.2023.09.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 08/07/2023] [Accepted: 09/05/2023] [Indexed: 10/08/2023]
Abstract
The past few years have seen increased interest in unmanned aerial vehicle (UAV)-based hyperspectral imaging (HSI) and machine learning (ML) in agricultural research, concomitant with an increase in published research on these topics. We provide an updated review, written for agriculturalists, highlighting the benefits in the retrieval of biophysical parameters of crops via UAVs relative to less sophisticated options. We reviewed >70 recent papers and found few consistent results between similar studies. Owing to their high complexity and cost, especially when applied to crops of low value, the benefits of most of the research reviewed are difficult to explain. Future effort will be necessary to distill research findings into lower-cost options for end-users.
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Affiliation(s)
- Alessandro Matese
- Geosystems Research Institute, Mississippi State University, Box 9627, Starkville, MS, USA; Institute of BioEconomy, National Research Council (CNR-IBE), Via Caproni 8, 50145 Florence, Italy.
| | | | - Sathishkumar Samiappan
- Geosystems Research Institute, Mississippi State University, Box 9627, Starkville, MS, USA
| | - Robert Moorhead
- Geosystems Research Institute, Mississippi State University, Box 9627, Starkville, MS, USA
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3
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Grishina A, Sherstneva O, Zhavoronkova A, Ageyeva M, Zdobnova T, Lysov M, Brilkina A, Vodeneev V. Comparison of the Efficiency of Hyperspectral and Pulse Amplitude Modulation Imaging Methods in Pre-Symptomatic Virus Detection in Tobacco Plants. PLANTS (BASEL, SWITZERLAND) 2023; 12:3831. [PMID: 38005728 PMCID: PMC10674761 DOI: 10.3390/plants12223831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 11/07/2023] [Accepted: 11/10/2023] [Indexed: 11/26/2023]
Abstract
Early detection of pathogens can significantly reduce yield losses and improve the quality of agricultural products. This study compares the efficiency of hyperspectral (HS) imaging and pulse amplitude modulation (PAM) fluorometry to detect pathogens in plants. Reflectance spectra, normalized indices, and fluorescence parameters were studied in healthy and infected areas of leaves. Potato virus X with GFP fluorescent protein was used to assess the spread of infection throughout the plant. The study found that infection increased the reflectance of leaves in certain wavelength ranges. Analysis of the normalized reflectance indices (NRIs) revealed indices that were sensitive and insensitive to infection. NRI700/850 was optimal for virus detection; significant differences were detected on the 4th day after the virus arrived in the leaf. Maximum (Fv/Fm) and effective quantum yields of photosystem II (ΦPSII) and non-photochemical fluorescence quenching (NPQ) were almost unchanged at the early stage of infection. ΦPSII and NPQ in the transition state (a short time after actinic light was switched on) showed high sensitivity to infection. The higher sensitivity of PAM compared to HS imaging may be due to the possibility of assessing the physiological changes earlier than changes in leaf structure.
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Affiliation(s)
- Alyona Grishina
- Department of Biophysics, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (A.G.); (A.Z.); (T.Z.); (M.L.); (V.V.)
| | - Oksana Sherstneva
- Department of Biophysics, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (A.G.); (A.Z.); (T.Z.); (M.L.); (V.V.)
| | - Anna Zhavoronkova
- Department of Biophysics, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (A.G.); (A.Z.); (T.Z.); (M.L.); (V.V.)
| | - Maria Ageyeva
- Department of Biochemistry and Biotechnology, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (M.A.); (A.B.)
| | - Tatiana Zdobnova
- Department of Biophysics, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (A.G.); (A.Z.); (T.Z.); (M.L.); (V.V.)
| | - Maxim Lysov
- Department of Biophysics, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (A.G.); (A.Z.); (T.Z.); (M.L.); (V.V.)
| | - Anna Brilkina
- Department of Biochemistry and Biotechnology, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (M.A.); (A.B.)
| | - Vladimir Vodeneev
- Department of Biophysics, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (A.G.); (A.Z.); (T.Z.); (M.L.); (V.V.)
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Herr AW, Carter AH. Remote sensing continuity: a comparison of HTP platforms and potential challenges with field applications. FRONTIERS IN PLANT SCIENCE 2023; 14:1233892. [PMID: 37790786 PMCID: PMC10544974 DOI: 10.3389/fpls.2023.1233892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 08/29/2023] [Indexed: 10/05/2023]
Abstract
In an era of climate change and increased environmental variability, breeders are looking for tools to maintain and increase genetic gain and overall efficiency. In recent years the field of high throughput phenotyping (HTP) has received increased attention as an option to meet this need. There are many platform options in HTP, but ground-based handheld and remote aerial systems are two popular options. While many HTP setups have similar specifications, it is not always clear if data from different systems can be treated interchangeably. In this research, we evaluated two handheld radiometer platforms, Cropscan MSR16R and Spectra Vista Corp (SVC) HR-1024i, as well as a UAS-based system with a Sentera Quad Multispectral Sensor. Each handheld radiometer was used for two years simultaneously with the unoccupied aircraft systems (UAS) in collecting winter wheat breeding trials between 2018-2021. Spectral reflectance indices (SRI) were calculated for each system. SRI heritability and correlation were analyzed in evaluating the platform and SRI usability for breeding applications. Correlations of SRIs were low against UAS SRI and grain yield while using the Cropscan system in 2018 and 2019. Dissimilarly, the SVC system in 2020 and 2021 produced moderate correlations across UAS SRI and grain yield. UAS SRI were consistently more heritable, with broad-sense heritability ranging from 0.58 to 0.80. Data standardization and collection windows are important to consider in ensuring reliable data. Furthermore, practical aspects and best practices for these HTP platforms, relative to applied breeding applications, are highlighted and discussed. The findings of this study can be a framework to build upon when considering the implementation of HTP technology in an applied breeding program.
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Affiliation(s)
| | - Arron H. Carter
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
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Sereda I, Danilov R, Kremneva O, Zimin M, Podushin Y. Development of Methods for Remote Monitoring of Leaf Diseases in Wheat Agrocenoses. PLANTS (BASEL, SWITZERLAND) 2023; 12:3223. [PMID: 37765387 PMCID: PMC10537214 DOI: 10.3390/plants12183223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 08/23/2023] [Accepted: 08/29/2023] [Indexed: 09/29/2023]
Abstract
The development of remote methods for diagnosing the state of crops using spectral equipment for remote sensing of the Earth and original monitoring tools is the most promising solution to the problem of monitoring diseases of wheat agrocenoses. A research site was created on the experimental field of the Federal Research Center of Biological Plant Protection. Within the experimental field with a total area of 1 ha, test plots were allocated to create an artificial infectious background, and the corresponding control plots were treated with fungicides. The research methodology is based on the time synchronization of high-precision ground-based spectrometric measurements with satellite and unmanned remote surveys and the comparison of the obtained data with phytopathological field surveys. Our results show that the least-affected plants predominantly had lower reflectance values in the green, red, and red-edge spectral ranges and high values in the near-infrared range throughout the growing season. The most informative spectral ranges when using satellite images and multispectral cameras placed on UAVs are the red and IR ranges. At the same time, the high frequency of measurements is of key importance for determining the level of pathogenic background. We conclude that information acquisition density does not play as significant of a role as the repetition of measurements when carrying out ground-based spectrometry. The use of vegetation indices in assessing the dynamics of the spectral images of various survey systems allows us to bring them to similar values.
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Affiliation(s)
- Igor Sereda
- Faculty of Geography, Lomonosov Moscow State University, 119991 Moscow, Russia; (I.S.); (M.Z.)
| | - Roman Danilov
- Federal State Budgetary Scientific Institution “Federal Research Center of Biological Plant Protection” (FSBSI FRCBPP), 350039 Krasnodar, Russia
| | - Oksana Kremneva
- Federal State Budgetary Scientific Institution “Federal Research Center of Biological Plant Protection” (FSBSI FRCBPP), 350039 Krasnodar, Russia
| | - Mikhail Zimin
- Faculty of Geography, Lomonosov Moscow State University, 119991 Moscow, Russia; (I.S.); (M.Z.)
| | - Yuri Podushin
- Federal State Budgetary Educational Institution “Kuban State Agrarian University Named after I.T. Trubilin”, 350004 Krasnodar, Russia;
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Wang H, Li T, Nishida E, Kato Y, Fukano Y, Guo W. Drone-Based Harvest Data Prediction Can Reduce On-Farm Food Loss and Improve Farmer Income. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0086. [PMID: 37692103 PMCID: PMC10484300 DOI: 10.34133/plantphenomics.0086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 08/14/2023] [Indexed: 09/12/2023]
Abstract
On-farm food loss (i.e., grade-out vegetables) is a difficult challenge in sustainable agricultural systems. The simplest method to reduce the number of grade-out vegetables is to monitor and predict the size of all individuals in the vegetable field and determine the optimal harvest date with the smallest grade-out number and highest profit, which is not cost-effective by conventional methods. Here, we developed a full pipeline to accurately estimate and predict every broccoli head size (n > 3,000) automatically and nondestructively using drone remote sensing and image analysis. The individual sizes were fed to the temperature-based growth model and predicted the optimal harvesting date. Two years of field experiments revealed that our pipeline successfully estimated and predicted the head size of all broccolis with high accuracy. We also found that a deviation of only 1 to 2 days from the optimal date can considerably increase grade-out and reduce farmer's profits. This is an unequivocal demonstration of the utility of these approaches to economic crop optimization and minimization of food losses.
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Affiliation(s)
- Haozhou Wang
- Graduate School of Agricultural and Life Sciences,
The University of Tokyo, Tokyo, Japan
| | - Tang Li
- Graduate School of Agricultural and Life Sciences,
The University of Tokyo, Tokyo, Japan
| | - Erika Nishida
- Graduate School of Agricultural and Life Sciences,
The University of Tokyo, Tokyo, Japan
| | - Yoichiro Kato
- Graduate School of Agricultural and Life Sciences,
The University of Tokyo, Tokyo, Japan
| | - Yuya Fukano
- Graduate School of Horticulture,
Chiba University, Chiba, Japan
| | - Wei Guo
- Graduate School of Agricultural and Life Sciences,
The University of Tokyo, Tokyo, Japan
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7
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Pansy DL, Murali M. UAV hyperspectral remote sensor images for mango plant disease and pest identification using MD-FCM and XCS-RBFNN. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:1120. [PMID: 37650944 DOI: 10.1007/s10661-023-11678-9] [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: 08/13/2022] [Accepted: 08/04/2023] [Indexed: 09/01/2023]
Abstract
To diminish disease transmission together with promoting effective management techniques, it is crucial to monitor plant health and detect pathogens earlier. The initial part in reducing losses sourced from plant diseases is to make an accurate and earlier identification. Thus, the usage of unmanned aerial vehicle (UAV) hyperspectral imaging (HSI) sensors for surveying and assessing crops, orchards, and forests has rapidly elevated over the last decade, particularly for the stress management like water, diseases, nutrition deficits, and pests. Using Minkowski Distance-based Fuzzy C Means (MD-FCM) clustering and Xavier initialization-adapted Cosine Similarity-induced Radial Bias Function Neural Network (XCS-RBFNN) techniques, a UAV HS imaging remote sensor for Spatial and Temporal Resolution (STR) of mango plant disease and pest identification is proposed in this scheme. Collecting the input UAV source (image or video) is eventuated initially along with the Region of Interest (ROI) calculated which is followed by preprocessing. Leaf segmentation is eventuated using Logistic U-net after preprocessing. Next, MD-FCM performs clustering to cluster the diseased leaves and pests individually. The disease and pest characteristics are then retrieved separately and classified further. The requisite features are then chosen from the retrieved features utilizing the Levy Flight Distribution-produced Butterfly Optimization Algorithm (LFD-BOA). Finally, the XCS-RBFNN classifier is utilized to categorize the various diseases together with pests found in the UAV input source using the chosen features. The proposed framework's experimental findings are then compared to some prevailing schemes, with the results revealing that the proposed work outperforms other benchmark techniques.
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Affiliation(s)
- D Lita Pansy
- Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, 603 203, India.
| | - M Murali
- Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, 603 203, India
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8
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Osuna-Caballero S, Olivoto T, Jiménez-Vaquero MA, Rubiales D, Rispail N. RGB image-based method for phenotyping rust disease progress in pea leaves using R. PLANT METHODS 2023; 19:86. [PMID: 37605206 PMCID: PMC10440949 DOI: 10.1186/s13007-023-01069-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 08/04/2023] [Indexed: 08/23/2023]
Abstract
BACKGROUND Rust is a damaging disease affecting vital crops, including pea, and identifying highly resistant genotypes remains a challenge. Accurate measurement of infection levels in large germplasm collections is crucial for finding new resistance sources. Current evaluation methods rely on visual estimation of disease severity and infection type under field or controlled conditions. While they identify some resistance sources, they are error-prone and time-consuming. An image analysis system proves useful, providing an easy-to-use and affordable way to quickly count and measure rust-induced pustules on pea samples. This study aimed to develop an automated image analysis pipeline for accurately calculating rust disease progression parameters under controlled conditions, ensuring reliable data collection. RESULTS A highly efficient and automatic image-based method for assessing rust disease in pea leaves was developed using R. The method's optimization and validation involved testing different segmentation indices and image resolutions on 600 pea leaflets with rust symptoms. The approach allows automatic estimation of parameters like pustule number, pustule size, leaf area, and percentage of pustule coverage. It reconstructs time series data for each leaf and integrates daily estimates into disease progression parameters, including latency period and area under the disease progression curve. Significant variation in disease responses was observed between genotypes using both visual ratings and image-based analysis. Among assessed segmentation indices, the Normalized Green Red Difference Index (NGRDI) proved fastest, analysing 600 leaflets at 60% resolution in 62 s with parallel processing. Lin's concordance correlation coefficient between image-based and visual pustule counting showed over 0.98 accuracy at full resolution. While lower resolution slightly reduced accuracy, differences were statistically insignificant for most disease progression parameters, significantly reducing processing time and storage space. NGRDI was optimal at all time points, providing highly accurate estimations with minimal accumulated error. CONCLUSIONS A new image-based method for monitoring pea rust disease in detached leaves, using RGB spectral indices segmentation and pixel value thresholding, improves resolution and precision. It rapidly analyses hundreds of images with accuracy comparable to visual methods and higher than other image-based approaches. This method evaluates rust progression in pea, eliminating rater-induced errors from traditional methods. Implementing this approach to evaluate large germplasm collections will improve our understanding of plant-pathogen interactions and aid future breeding for novel pea cultivars with increased rust resistance.
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Affiliation(s)
| | - Tiago Olivoto
- Department of Plant Science, Federal University of Santa Catarina, Florianópolis, 88034-000, SC, Brazil
| | | | - Diego Rubiales
- Institute for Sustainable Agriculture, CSIC, Av. Menéndez Pidal s/n 14004, Córdoba, Spain
| | - Nicolas Rispail
- Institute for Sustainable Agriculture, CSIC, Av. Menéndez Pidal s/n 14004, Córdoba, Spain
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Sun X, Yang Z, Su P, Wei K, Wang Z, Yang C, Wang C, Qin M, Xiao L, Yang W, Zhang M, Song X, Feng M. Non-destructive monitoring of maize LAI by fusing UAV spectral and textural features. FRONTIERS IN PLANT SCIENCE 2023; 14:1158837. [PMID: 37063231 PMCID: PMC10102429 DOI: 10.3389/fpls.2023.1158837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 03/20/2023] [Indexed: 06/19/2023]
Abstract
Leaf area index (LAI) is an essential indicator for crop growth monitoring and yield prediction. Real-time, non-destructive, and accurate monitoring of crop LAI is of great significance for intelligent decision-making on crop fertilization, irrigation, as well as for predicting and warning grain productivity. This study aims to investigate the feasibility of using spectral and texture features from unmanned aerial vehicle (UAV) multispectral imagery combined with machine learning modeling methods to achieve maize LAI estimation. In this study, remote sensing monitoring of maize LAI was carried out based on a UAV high-throughput phenotyping platform using different varieties of maize as the research target. Firstly, the spectral parameters and texture features were extracted from the UAV multispectral images, and the Normalized Difference Texture Index (NDTI), Difference Texture Index (DTI) and Ratio Texture Index (RTI) were constructed by linear calculation of texture features. Then, the correlation between LAI and spectral parameters, texture features and texture indices were analyzed, and the image features with strong correlation were screened out. Finally, combined with machine learning method, LAI estimation models of different types of input variables were constructed, and the effect of image features combination on LAI estimation was evaluated. The results revealed that the vegetation indices based on the red (650 nm), red-edge (705 nm) and NIR (842 nm) bands had high correlation coefficients with LAI. The correlation between the linearly transformed texture features and LAI was significantly improved. Besides, machine learning models combining spectral and texture features have the best performance. Support Vector Machine (SVM) models of vegetation and texture indices are the best in terms of fit, stability and estimation accuracy (R2 = 0.813, RMSE = 0.297, RPD = 2.084). The results of this study were conducive to improving the efficiency of maize variety selection and provide some reference for UAV high-throughput phenotyping technology for fine crop management at the field plot scale. The results give evidence of the breeding efficiency of maize varieties and provide a certain reference for UAV high-throughput phenotypic technology in crop management at the field scale.
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Affiliation(s)
- Xinkai Sun
- College of Agriculture, Shanxi Agricultural University, Jinzhong, Taigu, Shanxi, China
| | - Zhongyu Yang
- College of Agriculture, Shanxi Agricultural University, Jinzhong, Taigu, Shanxi, China
| | - Pengyan Su
- College of Agriculture, Shanxi Agricultural University, Jinzhong, Taigu, Shanxi, China
| | - Kunxi Wei
- College of Agriculture, Shanxi Agricultural University, Jinzhong, Taigu, Shanxi, China
| | - Zhigang Wang
- College of Agriculture, Shanxi Agricultural University, Jinzhong, Taigu, Shanxi, China
| | - Chenbo Yang
- College of Agriculture, Shanxi Agricultural University, Jinzhong, Taigu, Shanxi, China
| | - Chao Wang
- College of Agriculture, Shanxi Agricultural University, Jinzhong, Taigu, Shanxi, China
| | - Mingxing Qin
- College of Resources and Environment, Shanxi Agricultural University, Jinzhong, Taigu, Shanxi, China
| | - Lujie Xiao
- College of Agriculture, Shanxi Agricultural University, Jinzhong, Taigu, Shanxi, China
| | - Wude Yang
- College of Agriculture, Shanxi Agricultural University, Jinzhong, Taigu, Shanxi, China
| | - Meijun Zhang
- College of Agriculture, Shanxi Agricultural University, Jinzhong, Taigu, Shanxi, China
| | - Xiaoyan Song
- College of Agriculture, Shanxi Agricultural University, Jinzhong, Taigu, Shanxi, China
| | - Meichen Feng
- College of Agriculture, Shanxi Agricultural University, Jinzhong, Taigu, Shanxi, China
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10
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Wu Y, Li X, Zhang Q, Zhou X, Qiu H, Wang P. Recognition of spider mite infestations in jujube trees based on spectral-spatial clustering of hyperspectral images from UAVs. FRONTIERS IN PLANT SCIENCE 2023; 14:1078676. [PMID: 36818847 PMCID: PMC9932681 DOI: 10.3389/fpls.2023.1078676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 01/06/2023] [Indexed: 06/18/2023]
Abstract
Spider mite infestations are a serious hazard for jujube trees in China. The use of remote sensing technology to evaluate the health of jujube trees in large-scale intensive agricultural production is an effective means of agricultural control. Hyperspectral remote sensing has a higher spectral resolution and richer spectral information than conventional multispectral remote sensing, which improves the detection of crop pests and diseases. We used hyperspectral remote sensing data from jujube fields infested with spider mite in Hotan Prefecture, Xinjiang to evaluate their use in monitoring this important pest. We fused spectral and spatial information from the hyperspectral images and propose a method of recognizing spider mite infestations of jujube trees. Our method is based on the construction of spectral features, the fusion of spatial information and clustering of these spectral-spatial features. We evaluated the effect of different spectral-spatial features and different clustering methods on the recognition of spider mite in jujube trees. The experimental results showed that the overall accuracy of the method for the recognition of spider mites was >93% and the overall accuracy of the band clustering-density peak clustering model for the recognition of spider mite reached 96.13%. This method can be applied to the control of jujube spider mites in agricultural production.
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Affiliation(s)
- Yue Wu
- College of Information Science and Engineering, Shandong Agricultural University, Taian, China
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - Xican Li
- College of Information Science and Engineering, Shandong Agricultural University, Taian, China
| | - Qing Zhang
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - Xiaozhen Zhou
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - Hongbin Qiu
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - Panpan Wang
- Institute of Agricultural Sciences, the 14th Division of Xinjiang Production and Construction Corps, Kunyu, China
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11
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Bai X, Fang H, He Y, Zhang J, Tao M, Wu Q, Yang G, Wei Y, Tang Y, Tang L, Lou B, Deng S, Yang Y, Feng X. Dynamic UAV Phenotyping for Rice Disease Resistance Analysis Based on Multisource Data. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0019. [PMID: 37040287 PMCID: PMC10076055 DOI: 10.34133/plantphenomics.0019] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 12/09/2022] [Indexed: 05/27/2023]
Abstract
Bacterial blight poses a threat to rice production and food security, which can be controlled through large-scale breeding efforts toward resistant cultivars. Unmanned aerial vehicle (UAV) remote sensing provides an alternative means for the infield phenotype evaluation of crop disease resistance to relatively time-consuming and laborious traditional methods. However, the quality of data acquired by UAV can be affected by several factors such as weather, crop growth period, and geographical location, which can limit their utility for the detection of crop disease and resistant phenotypes. Therefore, a more effective use of UAV data for crop disease phenotype analysis is required. In this paper, we used time series UAV remote sensing data together with accumulated temperature data to train the rice bacterial blight severity evaluation model. The best results obtained with the predictive model showed an R p 2 of 0.86 with an RMSEp of 0.65. Moreover, model updating strategy was used to explore the scalability of the established model in different geographical locations. Twenty percent of transferred data for model training was useful for the evaluation of disease severity over different sites. In addition, the method for phenotypic analysis of rice disease we built here was combined with quantitative trait loci (QTL) analysis to identify resistance QTL in genetic populations at different growth stages. Three new QTLs were identified, and QTLs identified at different growth stages were inconsistent. QTL analysis combined with UAV high-throughput phenotyping provides new ideas for accelerating disease resistance breeding.
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Affiliation(s)
- Xiulin Bai
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Hui Fang
- Huzhou Institute of Zhejiang University, Huzhou 313000, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Jinnuo Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Mingzhu Tao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Qingguan Wu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Guofeng Yang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Yuzhen Wei
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Yu Tang
- Academy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou 510665, China
| | - Lie Tang
- Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA 50011-3270, USA
| | - Binggan Lou
- College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China
| | - Shuiguang Deng
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China
| | - Yong Yang
- State Key Laboratory for Managing Biotic and Chemical Treats to the Quality and Safety of Agro-Products, Key Laboratory of Biotechnology for Plant Protection, Ministry of Agriculture, and Rural Affairs, Zhejiang Provincial Key Laboratory of Biotechnology for Plant Protection, Institute of Virology and Biotechnology, Zhejiang Academy of Agricultural Science, Hangzhou 31002, China
| | - Xuping Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
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12
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Shahi TB, Xu CY, Neupane A, Fresser D, O'Connor D, Wright G, Guo W. A cooperative scheme for late leaf spot estimation in peanut using UAV multispectral images. PLoS One 2023; 18:e0282486. [PMID: 36972266 PMCID: PMC10042374 DOI: 10.1371/journal.pone.0282486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 02/15/2023] [Indexed: 03/29/2023] Open
Abstract
In Australia, peanuts are mainly grown in Queensland with tropical and subtropical climates. The most common foliar disease that poses a severe threat to quality peanut production is late leaf spot (LLS). Unmanned aerial vehicles (UAVs) have been widely investigated for various plant trait estimations. The existing works on UAV-based remote sensing have achieved promising results for crop disease estimation using a mean or a threshold value to represent the plot-level image data, but these methods might be insufficient to capture the distribution of pixels within a plot. This study proposes two new methods, namely measurement index (MI) and coefficient of variation (CV), for LLS disease estimation on peanuts. We first investigated the relationship between the UAV-based multispectral vegetation indices (VIs) and the LLS disease scores at the late growth stages of peanuts. We then compared the performances of the proposed MI and CV-based methods with the threshold and mean-based methods for LLS disease estimation. The results showed that the MI-based method achieved the highest coefficient of determination and the lowest error for five of the six chosen VIs whereas the CV-based method performed the best for simple ratio (SR) index among the four methods. By considering the strengths and weaknesses of each method, we finally proposed a cooperative scheme based on the MI, the CV and the mean-based methods for automatic disease estimation, demonstrated by applying this scheme to the LLS estimation in peanuts.
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Affiliation(s)
- Tej Bahadur Shahi
- School of Engineering and Technology, CQUniversity, Rockhampton, QLD, Australia
| | - Cheng-Yuan Xu
- Institute for Future Farming Systems, School of Health, Medical and Applied Sciences, CQUniversity, Bundaberg, QLD, Australia
| | - Arjun Neupane
- School of Engineering and Technology, CQUniversity, Rockhampton, QLD, Australia
| | - Dayle Fresser
- Department of Agriculture and Fisheries, Bundaberg, QLD, Australia
| | - Dan O'Connor
- Peanut Company of Australia, Kingaroy, QLD, Australia
| | - Graeme Wright
- Peanut Company of Australia, Kingaroy, QLD, Australia
| | - William Guo
- School of Engineering and Technology, CQUniversity, Rockhampton, QLD, Australia
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13
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Hassan MH, Omar AM, Daskalakis E, Mohamed AA, Boyd LA, Blanford C, Grieve B, Bartolo PJDS. Multi-Layer Biosensor for Pre-Symptomatic Detection of Puccinia strifformis, the Causal Agent of Yellow Rust. BIOSENSORS 2022; 12:829. [PMID: 36290966 PMCID: PMC9599175 DOI: 10.3390/bios12100829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/27/2022] [Accepted: 10/01/2022] [Indexed: 06/16/2023]
Abstract
The yellow rust of wheat (caused by Puccinia striiformis f. sp. tritici) is a devastating fungal infection that is responsible for significant wheat yield losses. The main challenge with the detection of this disease is that it can only be visually detected on the leaf surface between 7 and 10 days after infection, and by this point, counter measures such as the use of fungicides are generally less effective. The hypothesis of this study is to develop and use a compact electrochemical-based biosensor for the early detection of P. striiformis, thus enabling fast countermeasures to be taken. The biosensor that was developed consists of three layers. The first layer mimics the wheat leaf surface morphology. The second layer consists of a sucrose/agar mixture that acts as a substrate and contains a wheat-derived terpene volatile organic compound that stimulates the germination and growth of the spores of the yellow rust pathogen P. s. f. sp. tritici. The third layer consists of a nonenzymatic glucose sensor that produces a signal once invertase is produced by P. striiformis, which comes into contact with the second layer, thereby converting sucrose to glucose. The results show the proof that this innovative biosensor can enable the detection of yellow rust spores in 72 h.
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Affiliation(s)
- Mohamed H. Hassan
- Department of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester M13 9PL, UK
| | - Abdalla M. Omar
- Department of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester M13 9PL, UK
| | - Evangelos Daskalakis
- Department of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester M13 9PL, UK
| | | | | | | | - Bruce Grieve
- Department of Electrical & Electronic Engineering, University of Manchester, Manchester M13 9PL, UK
| | - Paulo JDS. Bartolo
- Department of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester M13 9PL, UK
- Singapore 3D Printing Centre, Nanyang Technological University, Singapore 639798, Singapore
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14
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Lu J, Qiu H, Zhang Q, Lan Y, Wang P, Wu Y, Mo J, Chen W, Niu H, Wu Z. Inversion of chlorophyll content under the stress of leaf mite for jujube based on model PSO-ELM method. FRONTIERS IN PLANT SCIENCE 2022; 13:1009630. [PMID: 36247579 PMCID: PMC9562855 DOI: 10.3389/fpls.2022.1009630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 08/29/2022] [Indexed: 06/16/2023]
Abstract
During the growth season, jujube trees are susceptible to infestation by the leaf mite, which reduces the fruit quality and productivity. Traditional monitoring techniques for mites are time-consuming, difficult, subjective, and result in a time lag. In this study, the method based on a particle swarm optimization (PSO) algorithm extreme learning machine for estimation of leaf chlorophyll content (SPAD) under leaf mite infestation in jujube was proposed. Initially, image data and SPAD values for jujube orchards under four severities of leaf mite infestation were collected for analysis. Six vegetation indices and SPAD value were chosen for correlation analysis to establish the estimation model for SPAD and the vegetation indices. To address the influence of colinearity between spectral bands, the feature band with the highest correlation coefficient was retrieved first using the successive projection algorithm. In the modeling process, the PSO correlation coefficient was initialized with the convergent optimal approximation of the fitness function value; the root mean square error (RMSE) of the predicted and measured values was derived as an indicator of PSO goodness-of-fit to solve the problems of ELM model weights, threshold randomness, and uncertainty of network parameters; and finally, an iterative update method was used to determine the particle fitness value to optimize the minimum error or iteration number. The results reflected that significant differences were observed in the spectral reflectance of the jujube canopy corresponding with the severity of leaf mite infestation, and the infestation severity was negatively correlated with the SPAD value of jujube leaves. The selected vegetation indices NDVI, RVI, PhRI, and MCARI were positively correlated with SPAD, whereas TCARI and GI were negatively correlated with SPAD. The accuracy of the optimized PSO-ELM model (R 2 = 0.856, RMSE = 0.796) was superior to that of the ELM model alone (R 2 = 0.748, RMSE = 1.689). The PSO-ELM model for remote sensing estimation of relative leaf chlorophyll content of jujube shows high fault tolerance and improved data-processing efficiency. The results provide a reference for the utility of UAV remote sensing for monitoring leaf mite infestation of jujube.
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Affiliation(s)
- Jianqiang Lu
- College of Electronic Engineering, College of Artificial Intelligence, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology (NPAAC), Guangzhou, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
| | - Hongbin Qiu
- College of Electronic Engineering, College of Artificial Intelligence, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology (NPAAC), Guangzhou, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - Qing Zhang
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - Yubin Lan
- College of Electronic Engineering, College of Artificial Intelligence, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology (NPAAC), Guangzhou, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
| | - Panpan Wang
- The 14th Division of Xinjiang Production and Construction Corps, Institute of Agricultural Sciences, Kunyu, China
| | - Yue Wu
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - Jiawei Mo
- College of Electronic Engineering, College of Artificial Intelligence, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology (NPAAC), Guangzhou, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
| | - Wadi Chen
- College of Electronic Engineering, College of Artificial Intelligence, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology (NPAAC), Guangzhou, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
| | - HongYu Niu
- College of Electronic Engineering, College of Artificial Intelligence, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology (NPAAC), Guangzhou, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
| | - Zhiyun Wu
- College of Electronic Engineering, College of Artificial Intelligence, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology (NPAAC), Guangzhou, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
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15
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Bouguettaya A, Zarzour H, Kechida A, Taberkit AM. A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images. CLUSTER COMPUTING 2022; 26:1297-1317. [PMID: 35968221 PMCID: PMC9362359 DOI: 10.1007/s10586-022-03627-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 04/12/2022] [Accepted: 05/10/2022] [Indexed: 06/15/2023]
Abstract
The agricultural crop productivity can be affected and reduced due to many factors such as weeds, pests, and diseases. Traditional methods that are based on terrestrial engines, devices, and farmers' naked eyes are facing many limitations in terms of accuracy and the required time to cover large fields. Currently, precision agriculture that is based on the use of deep learning algorithms and Unmanned Aerial Vehicles (UAVs) provides an effective solution to achieve agriculture applications, including plant disease identification and treatment. In the last few years, plant disease monitoring using UAV platforms is one of the most important agriculture applications that have gained increasing interest by researchers. Accurate detection and treatment of plant diseases at early stages is crucial to improving agricultural production. To this end, in this review, we analyze the recent advances in the use of computer vision techniques that are based on deep learning algorithms and UAV technologies to identify and treat crop diseases.
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Affiliation(s)
- Abdelmalek Bouguettaya
- Research Centre in Industrial Technologies (CRTI), P.O. Box 64, Cheraga, 16014 Algiers, Algeria
| | - Hafed Zarzour
- LIM Research, Department of Mathematics and Computer Science, Souk Ahras University, 41000 Souk Ahras, Algeria
| | - Ahmed Kechida
- Research Centre in Industrial Technologies (CRTI), P.O. Box 64, Cheraga, 16014 Algiers, Algeria
| | - Amine Mohammed Taberkit
- Research Centre in Industrial Technologies (CRTI), P.O. Box 64, Cheraga, 16014 Algiers, Algeria
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16
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Early Detection of Bacterial Wilt in Tomato with Portable Hyperspectral Spectrometer. REMOTE SENSING 2022. [DOI: 10.3390/rs14122882] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
As a kind of soil-borne epidemic disease, bacterial wilt (BW) is one of the most serious diseases in tomatoes in southern China, which may significantly reduce food quality and the total amount of yield. Hyperspectral remote sensing can detect crop diseases in the early stages and offers potential for BW detection in tomatoes. Tomatoes in southern China are commonly cultivated in greenhouses or bird nets, limiting the application of remote sensing based on natural sunlight. To resolve these issues, we collected the spectrum of tomatoes firstly using the HS-VN1000B Portable Intelligent Spectrometer, which is equipped with a simulated solar light source. We then proposed a tomato BW detection model based on some optimal spectral features. Specifically, these optimal features, including vegetation indexes and principal components (PCs), were extracted by the sequential forward selection (SFS), the simulated annealing (SA), and the genetic algorithm (GA) and were finally fed into the support vector machine (SVM) classifier to detect diseased tomatoes. The results showed that the infected and healthy tomatoes exhibit different spectral characteristics for both leave and stem spectra, especially for near-infrared bands. In addition, the BW detecting model built by the combination of GA and SVM (GA-SVM) achieved the best performance with overall accuracies (OA) of 90.7% for leaves and 92.6% for stems. Compared with the results based on leaves, spectral features of stems provided better accuracy, indicating that the symptom of early infection of BW is more significant in tomato stems than in leaves. Further, the reliability of the GA-SVM tomato stem model was verified in our 2022 experiment with an OA of 88.6% and an F1 score of 0.80. Our study provides an effective means to detect BW disease of tomatoes in the early stages, which could help farmers manage their tomato production and effectively prevent pesticide abuse.
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17
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Ma Y, Ma L, Zhang Q, Huang C, Yi X, Chen X, Hou T, Lv X, Zhang Z. Cotton Yield Estimation Based on Vegetation Indices and Texture Features Derived From RGB Image. FRONTIERS IN PLANT SCIENCE 2022; 13:925986. [PMID: 35783985 PMCID: PMC9240637 DOI: 10.3389/fpls.2022.925986] [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: 04/22/2022] [Accepted: 05/23/2022] [Indexed: 06/15/2023]
Abstract
Yield monitoring is an important parameter to evaluate cotton productivity during cotton harvest. Nondestructive and accurate yield monitoring is of great significance to cotton production. Unmanned aerial vehicle (UAV) remote sensing has fast and repetitive acquisition ability. The visible vegetation indices has the advantages of low cost, small amount of calculation and high resolution. The combination of the UAV and visible vegetation indices has been more and more applied to crop yield monitoring. However, there are some shortcomings in estimating cotton yield based on visible vegetation indices only as the similarity between cotton and mulch film makes it difficult to differentiate them and yields may be saturated based on vegetation index estimates near harvest. Texture feature is another important remote sensing information that can provide geometric information of ground objects and enlarge the spatial information identification based on original image brightness. In this study, RGB images of cotton canopy were acquired by UAV carrying RGB sensors before cotton harvest. The visible vegetation indices and texture features were extracted from RGB images for cotton yield monitoring. Feature parameters were selected in different methods after extracting the information. Linear and nonlinear methods were used to build cotton yield monitoring models based on visible vegetation indices, texture features and their combinations. The results show that (1) vegetation indices and texture features extracted from the ultra-high-resolution RGB images obtained by UAVs were significantly correlated with the cotton yield; (2) The best model was that combined with vegetation indices and texture characteristics RF_ELM model, verification set R 2 was 0.9109, and RMSE was 0.91277 t.ha-1. rRMSE was 29.34%. In conclusion, the research results prove that UAV carrying RGB sensor has a certain potential in cotton yield monitoring, which can provide theoretical basis and technical support for field cotton production evaluation.
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Affiliation(s)
- Yiru Ma
- Xinjiang Production and Construction Crops Oasis Eco-Agriculture Key Laboratory, College of Agriculture, Shihezi University, Shihezi, China
| | - Lulu Ma
- Xinjiang Production and Construction Crops Oasis Eco-Agriculture Key Laboratory, College of Agriculture, Shihezi University, Shihezi, China
| | - Qiang Zhang
- Xinjiang Production and Construction Crops Oasis Eco-Agriculture Key Laboratory, College of Agriculture, Shihezi University, Shihezi, China
| | - Changping Huang
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - Xiang Yi
- Xinjiang Production and Construction Crops Oasis Eco-Agriculture Key Laboratory, College of Agriculture, Shihezi University, Shihezi, China
| | - Xiangyu Chen
- Xinjiang Production and Construction Crops Oasis Eco-Agriculture Key Laboratory, College of Agriculture, Shihezi University, Shihezi, China
| | - Tongyu Hou
- Xinjiang Production and Construction Crops Oasis Eco-Agriculture Key Laboratory, College of Agriculture, Shihezi University, Shihezi, China
| | - Xin Lv
- Xinjiang Production and Construction Crops Oasis Eco-Agriculture Key Laboratory, College of Agriculture, Shihezi University, Shihezi, China
| | - Ze Zhang
- Xinjiang Production and Construction Crops Oasis Eco-Agriculture Key Laboratory, College of Agriculture, Shihezi University, Shihezi, China
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18
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Combining Disease Mechanism and Machine Learning to Predict Wheat Fusarium Head Blight. REMOTE SENSING 2022. [DOI: 10.3390/rs14122732] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Wheat Fusarium head blight (FHB) can be effectively controlled through prediction. To address the low accuracy and poor stability of model predictions of wheat FHB, a prediction method of wheat FHB that couples a logistic regression mechanism-based model and k-nearest neighbours (KNN) model is proposed in this paper. First, we selected predictive factors, including remote sensing-based and meteorological factors. Then, we quantitatively expressed the factor weights of the disease occurrence and development mechanisms in the disease prediction model by using a logistic model. Subsequently, we integrated the obtained factor weights into the predictive factors and input the predictive factors with weights into the KNN model to predict the incidence of wheat FHB. Finally, the accuracy and generalizability of the models were evaluated. Wheat fields in Changfeng, Dingyuan, Fengyuan, and Feidong counties, Anhui Province, where wheat FHB often occurs, were used as the study area. The incidences of wheat FHB on 29 April and 10 May 2021 were predicted. Compared with a model that did not consider disease mechanism, the accuracy of our model increased by approximately 13%. The overall accuracies of the models for the two dates were 0.88 and 0.92, and the F1 index was 0.86 and 0.94, respectively. The results show that the predictions made with the logistic-KNN model had higher accuracy and better stability than those made with the KNN model, thus achieving remote sensing-based high-precision prediction of wheat FHB.
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19
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Surveying soil-borne disease development on wild rocket salad crop by proximal sensing based on high-resolution hyperspectral features. Sci Rep 2022; 12:5098. [PMID: 35332172 PMCID: PMC8948195 DOI: 10.1038/s41598-022-08969-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 03/14/2022] [Indexed: 11/08/2022] Open
Abstract
Wild rocket (Diplotaxis tenuifolia, Brassicaceae) is a baby-leaf vegetable crop of high economic interest, used in ready-to-eat minimally processed salads, with an appreciated taste and nutraceutical features. Disease management is key to achieving the sustainability of the entire production chain in intensive systems, where synthetic fungicides are limited or not permitted. In this context, soil-borne pathologies, much feared by growers, are becoming a real emergency. Digital screening of green beds can be implemented in order to optimize the use of sustainable means. The current study used a high-resolution hyperspectral array (spectroscopy at 350-2500 nm) to attempt to follow the progression of symptoms of Rhizoctonia, Sclerotinia, and Sclerotium disease across four different severity levels. A Random Forest machine learning model reduced dimensions of the training big dataset allowing to compute de novo vegetation indices specifically informative about canopy decay caused by all basal pathogenic attacks. Their transferability was also tested on the canopy dataset, which was useful for assessing the health status of wild rocket plants. Indeed, the progression of symptoms associated with soil-borne pathogens is closely related to the reduction of leaf absorbance of the canopy in certain ranges of visible and shortwave infrared spectral regions sensitive to reduction of chlorophyll and other pigments as well as to modifications of water content and turgor.
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20
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Consolidated Convolutional Neural Network for Hyperspectral Image Classification. REMOTE SENSING 2022. [DOI: 10.3390/rs14071571] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The performance of hyperspectral image (HSI) classification is highly dependent on spatial and spectral information, and is heavily affected by factors such as data redundancy and insufficient spatial resolution. To overcome these challenges, many convolutional neural networks (CNN) especially 2D-CNN-based methods have been proposed for HSI classification. However, these methods produced insufficient results compared to 3D-CNN-based methods. On the other hand, the high computational complexity of the 3D-CNN-based methods is still a major concern that needs to be addressed. Therefore, this study introduces a consolidated convolutional neural network (C-CNN) to overcome the aforementioned issues. The proposed C-CNN is comprised of a three-dimension CNN (3D-CNN) joined with a two-dimension CNN (2D-CNN). The 3D-CNN is used to represent spatial–spectral features from the spectral bands, and the 2D-CNN is used to learn abstract spatial features. Principal component analysis (PCA) was firstly applied to the original HSIs before they are fed to the network to reduce the spectral bands redundancy. Moreover, image augmentation techniques including rotation and flipping have been used to increase the number of training samples and reduce the impact of overfitting. The proposed C-CNN that was trained using the augmented images is named C-CNN-Aug. Additionally, both Dropout and L2 regularization techniques have been used to further reduce the model complexity and prevent overfitting. The experimental results proved that the proposed model can provide the optimal trade-off between accuracy and computational time compared to other related methods using the Indian Pines, Pavia University, and Salinas Scene hyperspectral benchmark datasets.
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21
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Abstract
Hyperspectral (HS) images are adjacent band images that are generally used in remote-sensing applications. They have numerous spatial and spectral information bands that are extremely useful for material detection in various fields. However, their high dimensionality is a big challenge that affects their overall performance. A new data normalization method was developed to enhance the variations and data distribution using the output of principal component analysis (PCA) and quantile transformation, called QPCA. This paper also proposes a novel HS images classification framework using the meta-learner technique to train multi-class and multi-size datasets by concatenating and training the hybrid and multi-size kernel of convolutional neural networks (CNN). The high-level model works to combine the output of the lower-level models and train them with the new input data, called meta-learner hybrid models (MLHM). The proposed MLHM framework with our external normalization (QPCA) improves the accuracy and outperforms other approaches using three well-known benchmark datasets. Moreover, the evaluation outcomes showed that the QPCA enhanced the framework accuracy by 13% for most models and datasets and others by more than 25%, and MLHM provided the best performance.
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22
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Terentev A, Dolzhenko V, Fedotov A, Eremenko D. Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review. SENSORS 2022; 22:s22030757. [PMID: 35161504 PMCID: PMC8839015 DOI: 10.3390/s22030757] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 01/13/2022] [Accepted: 01/16/2022] [Indexed: 01/10/2023]
Abstract
The development of hyperspectral remote sensing equipment, in recent years, has provided plant protection professionals with a new mechanism for assessing the phytosanitary state of crops. Semantically rich data coming from hyperspectral sensors are a prerequisite for the timely and rational implementation of plant protection measures. This review presents modern advances in early plant disease detection based on hyperspectral remote sensing. The review identifies current gaps in the methodologies of experiments. A further direction for experimental methodological development is indicated. A comparative study of the existing results is performed and a systematic table of different plants' disease detection by hyperspectral remote sensing is presented, including important wave bands and sensor model information.
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Affiliation(s)
- Anton Terentev
- All-Russian Institute of Plant Protection, 3 Podbelsokogo Str., Pushkin, 196608 Saint Petersburg, Russia;
- Correspondence: (A.T.); (A.F.); Tel.: +7-921-937-1550 (A.T.); +7-921-741-6303 (A.F.)
| | - Viktor Dolzhenko
- All-Russian Institute of Plant Protection, 3 Podbelsokogo Str., Pushkin, 196608 Saint Petersburg, Russia;
| | - Alexander Fedotov
- World-Class Research Center «Advanced Digital Technologies», Peter the Great St. Petersburg Polytechnic University, 29 Polytechnicheskaya Str., 195251 Saint Petersburg, Russia;
- Correspondence: (A.T.); (A.F.); Tel.: +7-921-937-1550 (A.T.); +7-921-741-6303 (A.F.)
| | - Danila Eremenko
- World-Class Research Center «Advanced Digital Technologies», Peter the Great St. Petersburg Polytechnic University, 29 Polytechnicheskaya Str., 195251 Saint Petersburg, Russia;
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23
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Unmanned Aerial Vehicles (UAV) in Precision Agriculture: Applications and Challenges. ENERGIES 2021. [DOI: 10.3390/en15010217] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Agriculture is the primary source of income in developing countries like India. Agriculture accounts for 17 percent of India’s total GDP, with almost 60 percent of the people directly or indirectly employed. While researchers and planters focus on a variety of elements to boost productivity, crop loss due to disease is one of the most serious issues they confront. Crop growth monitoring and early detection of pest infestations are still a problem. With the expansion of cultivation to wider fields, manual intervention to monitor and diagnose insect and pest infestations is becoming increasingly difficult. Failure to apply on time fertilizers and pesticides results in more crop loss and so lower output. Farmers are putting in greater effort to conserve crops, but they are failing most of the time because they are unable to adequately monitor the crops when they are infected by pests and insects. Pest infestation is also difficult to predict because it is not evenly distributed. In the recent past, modern equipment, tools, and approaches have been used to replace manual involvement. Unmanned aerial vehicles serve a critical role in crop disease surveillance and early detection in this setting. This research attempts to give a review of the most successful techniques to have precision-based crop monitoring and pest management in agriculture fields utilizing unmanned aerial vehicles (UAVs) or unmanned aircraft. The researchers’ reports on the various types of UAVs and their applications to early detection of agricultural diseases are rigorously assessed and compared. This paper also discusses the deployment of aerial, satellite, and other remote sensing technologies for disease detection, as well as their Quality of Service (QoS).
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24
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Feng Z, Song L, Duan J, He L, Zhang Y, Wei Y, Feng W. Monitoring Wheat Powdery Mildew Based on Hyperspectral, Thermal Infrared, and RGB Image Data Fusion. SENSORS 2021; 22:s22010031. [PMID: 35009575 PMCID: PMC8747141 DOI: 10.3390/s22010031] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 12/18/2021] [Accepted: 12/19/2021] [Indexed: 11/23/2022]
Abstract
Powdery mildew severely affects wheat growth and yield; therefore, its effective monitoring is essential for the prevention and control of the disease and global food security. In the present study, a spectroradiometer and thermal infrared cameras were used to obtain hyperspectral signature and thermal infrared images data, and thermal infrared temperature parameters (TP) and texture features (TF) were extracted from the thermal infrared images and RGB images of wheat with powdery mildew, during the wheat flowering and filling periods. Based on the ten vegetation indices from the hyperspectral data (VI), TF and TP were integrated, and partial least square regression, random forest regression (RFR), and support vector machine regression (SVR) algorithms were used to construct a prediction model for a wheat powdery mildew disease index. According to the results, the prediction accuracy of RFR was higher than in other models, under both single data source modeling and multi-source data modeling; among the three data sources, VI was the most suitable for powdery mildew monitoring, followed by TP, and finally TF. The RFR model had stable performance in multi-source data fusion modeling (VI&TP&TF), and had the optimal estimation performance with 0.872 and 0.862 of R2 for calibration and validation, respectively. The application of multi-source data collaborative modeling could improve the accuracy of remote sensing monitoring of wheat powdery mildew, and facilitate the achievement of high-precision remote sensing monitoring of crop disease status.
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Affiliation(s)
- Ziheng Feng
- State Key Laboratory of Wheat and Maize Crop Science, Agronomy College, Henan Agriculture University, Zhengzhou 450046, China; (Z.F.); (L.S.); (J.D.); (L.H.); (Y.Z.); (Y.W.)
- Information and Management Science College, Henan Agricultural University, Zhengzhou 450046, China
| | - Li Song
- State Key Laboratory of Wheat and Maize Crop Science, Agronomy College, Henan Agriculture University, Zhengzhou 450046, China; (Z.F.); (L.S.); (J.D.); (L.H.); (Y.Z.); (Y.W.)
| | - Jianzhao Duan
- State Key Laboratory of Wheat and Maize Crop Science, Agronomy College, Henan Agriculture University, Zhengzhou 450046, China; (Z.F.); (L.S.); (J.D.); (L.H.); (Y.Z.); (Y.W.)
| | - Li He
- State Key Laboratory of Wheat and Maize Crop Science, Agronomy College, Henan Agriculture University, Zhengzhou 450046, China; (Z.F.); (L.S.); (J.D.); (L.H.); (Y.Z.); (Y.W.)
| | - Yanyan Zhang
- State Key Laboratory of Wheat and Maize Crop Science, Agronomy College, Henan Agriculture University, Zhengzhou 450046, China; (Z.F.); (L.S.); (J.D.); (L.H.); (Y.Z.); (Y.W.)
| | - Yongkang Wei
- State Key Laboratory of Wheat and Maize Crop Science, Agronomy College, Henan Agriculture University, Zhengzhou 450046, China; (Z.F.); (L.S.); (J.D.); (L.H.); (Y.Z.); (Y.W.)
| | - Wei Feng
- State Key Laboratory of Wheat and Maize Crop Science, Agronomy College, Henan Agriculture University, Zhengzhou 450046, China; (Z.F.); (L.S.); (J.D.); (L.H.); (Y.Z.); (Y.W.)
- Correspondence:
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Xu Y, Shrestha V, Piasecki C, Wolfe B, Hamilton L, Millwood RJ, Mazarei M, Stewart CN. Sustainability Trait Modeling of Field-Grown Switchgrass ( Panicum virgatum) Using UAV-Based Imagery. PLANTS (BASEL, SWITZERLAND) 2021; 10:plants10122726. [PMID: 34961199 PMCID: PMC8709265 DOI: 10.3390/plants10122726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 11/29/2021] [Accepted: 12/07/2021] [Indexed: 05/17/2023]
Abstract
Unmanned aerial vehicles (UAVs) provide an intermediate scale of spatial and spectral data collection that yields increased accuracy and consistency in data collection for morphological and physiological traits than satellites and expanded flexibility and high-throughput compared to ground-based data collection. In this study, we used UAV-based remote sensing for automated phenotyping of field-grown switchgrass (Panicum virgatum), a leading bioenergy feedstock. Using vegetation indices calculated from a UAV-based multispectral camera, statistical models were developed for rust disease caused by Puccinia novopanici, leaf chlorophyll, nitrogen, and lignin contents. For the first time, UAV remote sensing technology was used to explore the potentials for multiple traits associated with sustainable production of switchgrass, and one statistical model was developed for each individual trait based on the statistical correlation between vegetation indices and the corresponding trait. Also, for the first time, lignin content was estimated in switchgrass shoots via UAV-based multispectral image analysis and statistical analysis. The UAV-based models were verified by ground-truthing via correlation analysis between the traits measured manually on the ground-based with UAV-based data. The normalized difference red edge (NDRE) vegetation index outperformed the normalized difference vegetation index (NDVI) for rust disease and nitrogen content, while NDVI performed better than NDRE for chlorophyll and lignin content. Overall, linear models were sufficient for rust disease and chlorophyll analysis, but for nitrogen and lignin contents, nonlinear models achieved better results. As the first comprehensive study to model switchgrass sustainability traits from UAV-based remote sensing, these results suggest that this methodology can be utilized for switchgrass high-throughput phenotyping in the field.
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Affiliation(s)
- Yaping Xu
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA; (Y.X.); (V.S.); (C.P.); (B.W.); (L.H.)
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Vivek Shrestha
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA; (Y.X.); (V.S.); (C.P.); (B.W.); (L.H.)
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Cristiano Piasecki
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA; (Y.X.); (V.S.); (C.P.); (B.W.); (L.H.)
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
- ATSI Brasil Pesquisa e Consultoria, Passo Fundo 99054-328, RS, Brazil
| | - Benjamin Wolfe
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA; (Y.X.); (V.S.); (C.P.); (B.W.); (L.H.)
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Lance Hamilton
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA; (Y.X.); (V.S.); (C.P.); (B.W.); (L.H.)
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Reginald J. Millwood
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA; (Y.X.); (V.S.); (C.P.); (B.W.); (L.H.)
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
- Correspondence: (R.J.M.); (M.M.); (C.N.S.J.)
| | - Mitra Mazarei
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA; (Y.X.); (V.S.); (C.P.); (B.W.); (L.H.)
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
- Correspondence: (R.J.M.); (M.M.); (C.N.S.J.)
| | - Charles Neal Stewart
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA; (Y.X.); (V.S.); (C.P.); (B.W.); (L.H.)
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
- Correspondence: (R.J.M.); (M.M.); (C.N.S.J.)
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Boost Precision Agriculture with Unmanned Aerial Vehicle Remote Sensing and Edge Intelligence: A Survey. REMOTE SENSING 2021. [DOI: 10.3390/rs13214387] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
In recent years unmanned aerial vehicles (UAVs) have emerged as a popular and cost-effective technology to capture high spatial and temporal resolution remote sensing (RS) images for a wide range of precision agriculture applications, which can help reduce costs and environmental impacts by providing detailed agricultural information to optimize field practices. Furthermore, deep learning (DL) has been successfully applied in agricultural applications such as weed detection, crop pest and disease detection, etc. as an intelligent tool. However, most DL-based methods place high computation, memory and network demands on resources. Cloud computing can increase processing efficiency with high scalability and low cost, but results in high latency and great pressure on the network bandwidth. The emerging of edge intelligence, although still in the early stages, provides a promising solution for artificial intelligence (AI) applications on intelligent edge devices at the edge of the network close to data sources. These devices are with built-in processors enabling onboard analytics or AI (e.g., UAVs and Internet of Things gateways). Therefore, in this paper, a comprehensive survey on the latest developments of precision agriculture with UAV RS and edge intelligence is conducted for the first time. The major insights observed are as follows: (a) in terms of UAV systems, small or light, fixed-wing or industrial rotor-wing UAVs are widely used in precision agriculture; (b) sensors on UAVs can provide multi-source datasets, and there are only a few public UAV dataset for intelligent precision agriculture, mainly from RGB sensors and a few from multispectral and hyperspectral sensors; (c) DL-based UAV RS methods can be categorized into classification, object detection and segmentation tasks, and convolutional neural network and recurrent neural network are the mostly common used network architectures; (d) cloud computing is a common solution to UAV RS data processing, while edge computing brings the computing close to data sources; (e) edge intelligence is the convergence of artificial intelligence and edge computing, in which model compression especially parameter pruning and quantization is the most important and widely used technique at present, and typical edge resources include central processing units, graphics processing units and field programmable gate arrays.
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Using UAV-Based Hyperspectral Imagery to Detect Winter Wheat Fusarium Head Blight. REMOTE SENSING 2021. [DOI: 10.3390/rs13153024] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Fusarium head blight (FHB) is a major winter wheat disease in China. The accurate and timely detection of wheat FHB is vital to scientific field management. By combining three types of spectral features, namely, spectral bands (SBs), vegetation indices (VIs), and wavelet features (WFs), in this study, we explore the potential of using hyperspectral imagery obtained from an unmanned aerial vehicle (UAV), to detect wheat FHB. First, during the wheat filling period, two UAV-based hyperspectral images were acquired. SBs, VIs, and WFs that were sensitive to wheat FHB were extracted and optimized from the two images. Subsequently, a field-scale wheat FHB detection model was formulated, based on the optimal spectral feature combination of SBs, VIs, and WFs (SBs + VIs + WFs), using a support vector machine. Two commonly used data normalization algorithms were utilized before the construction of the model. The single WFs, and the spectral feature combination of optimal SBs and VIs (SBs + VIs), were respectively used to formulate models for comparison and testing. The results showed that the detection model based on the normalized SBs + VIs + WFs, using min–max normalization algorithm, achieved the highest R2 of 0.88 and the lowest RMSE of 2.68% among the three models. Our results suggest that UAV-based hyperspectral imaging technology is promising for the field-scale detection of wheat FHB. Combining traditional SBs and VIs with WFs can improve the detection accuracy of wheat FHB effectively.
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Wheat Fusarium Head Blight Detection Using UAV-Based Spectral and Texture Features in Optimal Window Size. REMOTE SENSING 2021. [DOI: 10.3390/rs13132437] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
By combining the spectral and texture features of images captured by unmanned aerial vehicles (UAVs), the accurate and timely detection of wheat Fusarium head blight (FHB) can be realized. This study presents a methodology to select the optimal window size of the gray-level co-occurrence matrix (GLCM) to extract texture features from UAV images for FHB detection. Host conditions and the disease distribution were combined to construct the model, and its overall accuracy, sensitivity, and generalization ability were evaluated. First, the sensitive spectral features and bands of the UAV-derived hyperspectral images were obtained, and then texture features were selected. Subsequently, spectral features and texture features extracted from windows of different sizes were input to classify the area of severe FHB. According to the model comparison, the optimal window size was obtained. With the collinearity between features eliminated, the best performance of the logistic model reached, with an accuracy, F1 score, and area under the receiver operating characteristic curve of 0.90, 0.79, and 0.79, respectively, when the window size of the GLCM was 5 × 5 pixels on May 3, and of 0.90, 0.83, and 0.82, respectively, when the size was 17 × 17 pixels on May 8. The results showed that the selection of an appropriate GLCM window size for texture feature extraction enabled more accurate disease detection.
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UAVs for Vegetation Monitoring: Overview and Recent Scientific Contributions. REMOTE SENSING 2021. [DOI: 10.3390/rs13112139] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
This paper reviewed a set of twenty-one original and innovative papers included in a special issue on UAVs for vegetation monitoring, which proposed new methods and techniques applied to diverse agricultural and forestry scenarios. Three general categories were considered: (1) sensors and vegetation indices used, (2) technological goals pursued, and (3) agroforestry applications. Some investigations focused on issues related to UAV flight operations, spatial resolution requirements, and computation and data analytics, while others studied the ability of UAVs for characterizing relevant vegetation features (mainly canopy cover and crop height) or for detecting different plant/crop stressors, such as nutrient content/deficiencies, water needs, weeds, and diseases. The general goal was proposing UAV-based technological solutions for a better use of agricultural and forestry resources and more efficient production with relevant economic and environmental benefits.
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Zhang J, Cheng T, Guo W, Xu X, Qiao H, Xie Y, Ma X. Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods. PLANT METHODS 2021; 17:49. [PMID: 33941211 PMCID: PMC8094481 DOI: 10.1186/s13007-021-00750-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 04/23/2021] [Indexed: 05/25/2023]
Abstract
BACKGROUND To accurately estimate winter wheat leaf area index (LAI) using unmanned aerial vehicle (UAV) hyperspectral imagery is crucial for crop growth monitoring, fertilization management, and development of precision agriculture. METHODS The UAV hyperspectral imaging data, Analytical Spectral Devices (ASD) data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments. The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models. RESULTS The results show that the correlation coefficient between UAV and ASD hyperspectral data is greater than 0.99, indicating the UAV data can be used for estimation of wheat growth information. The LAI bands selected by using different algorithms were slightly different among the 15 models built in this study. The Xgboost model using nine consecutive characteristic bands selected by CARS_SPA algorithm as input was proved to have the best performance. This model yielded identical results of coefficient of determination (0.89) for both calibration set and validation set, indicating a high accuracy of this model. CONCLUSIONS The Xgboost modeling method in combine with CARS_SPA algorithm can reduce input variables and improve the efficiency of model operation. The results provide reference and technical support for nondestructive and rapid estimation of winter wheat LAI by using UAV.
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Affiliation(s)
- Juanjuan Zhang
- Science College of Information and Management, Henan Agricultural University, #63 Nongye Road, Zhengzhou, 450002, Henan, China
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, #63 Nongye Road, Zhengzhou, 450002, Henan, China
| | - Tao Cheng
- Science College of Information and Management, Henan Agricultural University, #63 Nongye Road, Zhengzhou, 450002, Henan, China
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, #63 Nongye Road, Zhengzhou, 450002, Henan, China
| | - Wei Guo
- Science College of Information and Management, Henan Agricultural University, #63 Nongye Road, Zhengzhou, 450002, Henan, China
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, #63 Nongye Road, Zhengzhou, 450002, Henan, China
| | - Xin Xu
- Science College of Information and Management, Henan Agricultural University, #63 Nongye Road, Zhengzhou, 450002, Henan, China
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, #63 Nongye Road, Zhengzhou, 450002, Henan, China
| | - Hongbo Qiao
- Science College of Information and Management, Henan Agricultural University, #63 Nongye Road, Zhengzhou, 450002, Henan, China.
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, #63 Nongye Road, Zhengzhou, 450002, Henan, China.
| | - Yimin Xie
- Science College of Information and Management, Henan Agricultural University, #63 Nongye Road, Zhengzhou, 450002, Henan, China
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, #63 Nongye Road, Zhengzhou, 450002, Henan, China
| | - Xinming Ma
- Science College of Information and Management, Henan Agricultural University, #63 Nongye Road, Zhengzhou, 450002, Henan, China.
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, #63 Nongye Road, Zhengzhou, 450002, Henan, China.
- College of agronomy, Henan Agricultural University, #63 Nongye Road, ZhengZhou, Henan, 450002, China.
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Sun F, Chen Q, Chen Q, Jiang M, Gao W, Qu Y. Screening of Key Drought Tolerance Indices for Cotton at the Flowering and Boll Setting Stage Using the Dimension Reduction Method. FRONTIERS IN PLANT SCIENCE 2021; 12:619926. [PMID: 34305956 PMCID: PMC8299416 DOI: 10.3389/fpls.2021.619926] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 06/09/2021] [Indexed: 05/16/2023]
Abstract
Drought is one of the main abiotic stresses that seriously influences cotton production. Many indicators can be used to evaluate cotton drought tolerance, but the key indicators remain to be determined. The objective of this study was to identify effective cotton drought tolerance indicators from 19 indices, including morphology, photosynthesis, physiology, and yield-related indices, and to evaluate the yield potential of 104 cotton varieties under both normal and drought-stress field conditions. Combined with principal component analysis (PCA) and a regression analysis method, the results showed that the top five PCs among the 19, with eigenvalues > 1, contributed 65.52, 63.59, and 65.90% of the total variability during 2016 to 2018, respectively, which included plant height (PH), effective fruit branch number (EFBN), single boll weight (SBW), transpiration rate (Tr) and chlorophyll (Chl). Therefore, the indicator dimension decreased from 19 to 5. A comparison of the 19 indicators with the 5 identified indicators through PCA and a combined regression analysis found that the results of the final cluster of drought tolerance on 104 cotton varieties were basically consistent. The results indicated that these five traits could be used in combination to screen cotton varieties or lines for drought tolerance in cotton breeding programs, and Zhong R2016 and Xin lu zao 45 exhibited high drought tolerance and can be selected as superior parents for good yield performance under drought stress.
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