1
|
Blasch G, Anberbir T, Negash T, Tilahun L, Belayineh FY, Alemayehu Y, Mamo G, Hodson DP, Rodrigues FA. The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia. Sci Rep 2023; 13:16768. [PMID: 37798287 PMCID: PMC10556098 DOI: 10.1038/s41598-023-43770-y] [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: 02/09/2023] [Accepted: 09/28/2023] [Indexed: 10/07/2023] Open
Abstract
Very high (spatial and temporal) resolution satellite (VHRS) and high-resolution unmanned aerial vehicle (UAV) imagery provides the opportunity to develop new crop disease detection methods at early growth stages with utility for early warning systems. The capability of multispectral UAV, SkySat and Pleiades imagery as a high throughput phenotyping (HTP) and rapid disease detection tool for wheat rusts is assessed. In a randomized trial with and without fungicide control, six bread wheat varieties with differing rust resistance were monitored using UAV and VHRS. In total, 18 spectral features served as predictors for stem and yellow rust disease progression and associated yield loss. Several spectral features demonstrated strong predictive power for the detection of combined wheat rust diseases and the estimation of varieties' response to disease stress and grain yield. Visible spectral (VIS) bands (Green, Red) were more useful at booting, shifting to VIS-NIR (near-infrared) vegetation indices (e.g., NDVI, RVI) at heading. The top-performing spectral features for disease progression and grain yield were the Red band and UAV-derived RVI and NDVI. Our findings provide valuable insight into the upscaling capability of multispectral sensors for disease detection, demonstrating the possibility of upscaling disease detection from plot to regional scales at early growth stages.
Collapse
Affiliation(s)
- Gerald Blasch
- International Maize and Wheat Improvement Center (CIMMYT), Addis Ababa, Ethiopia.
| | - Tadesse Anberbir
- Ethiopian Institute of Agricultural Research (EIAR), Addis Ababa, Ethiopia
| | - Tamirat Negash
- Kulumsa Agricultural Research Center (KARC), Asella, Ethiopia
| | - Lidiya Tilahun
- Kulumsa Agricultural Research Center (KARC), Asella, Ethiopia
| | | | - Yoseph Alemayehu
- International Maize and Wheat Improvement Center (CIMMYT), Addis Ababa, Ethiopia
| | - Girma Mamo
- Ethiopian Institute of Agricultural Research (EIAR), Addis Ababa, Ethiopia
| | - David P Hodson
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Francelino A Rodrigues
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
- Lincoln Agritech Ltd, Lincoln University, Lincoln, New Zealand
| |
Collapse
|
2
|
Zhang C, Lane B, Fernández-Campos M, Cruz-Sancan A, Lee DY, Gongora-Canul C, Ross TJ, Da Silva CR, Telenko DEP, Goodwin SB, Scofield SR, Oh S, Jung J, Cruz CD. Monitoring tar spot disease in corn at different canopy and temporal levels using aerial multispectral imaging and machine learning. FRONTIERS IN PLANT SCIENCE 2023; 13:1077403. [PMID: 36756236 PMCID: PMC9900023 DOI: 10.3389/fpls.2022.1077403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 12/21/2022] [Indexed: 06/18/2023]
Abstract
Introduction Tar spot is a high-profile disease, causing various degrees of yield losses on corn (Zea mays L.) in several countries throughout the Americas. Disease symptoms usually appear at the lower canopy in corn fields with a history of tar spot infection, making it difficult to monitor the disease with unmanned aircraft systems (UAS) because of occlusion. Methods UAS-based multispectral imaging and machine learning were used to monitor tar spot at different canopy and temporal levels and extract epidemiological parameters from multiple treatments. Disease severity was assessed visually at three canopy levels within micro-plots, while aerial images were gathered by UASs equipped with multispectral cameras. Both disease severity and multispectral images were collected from five to eleven time points each year for two years. Image-based features, such as single-band reflectance, vegetation indices (VIs), and their statistics, were extracted from ortho-mosaic images and used as inputs for machine learning to develop disease quantification models. Results and discussion The developed models showed encouraging performance in estimating disease severity at different canopy levels in both years (coefficient of determination up to 0.93 and Lin's concordance correlation coefficient up to 0.97). Epidemiological parameters, including initial disease severity or y0 and area under the disease progress curve, were modeled using data derived from multispectral imaging. In addition, results illustrated that digital phenotyping technologies could be used to monitor the onset of tar spot when disease severity is relatively low (< 1%) and evaluate the efficacy of disease management tactics under micro-plot conditions. Further studies are required to apply and validate our methods to large corn fields.
Collapse
Affiliation(s)
- Chongyuan Zhang
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States
| | - Brenden Lane
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States
| | | | - Andres Cruz-Sancan
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States
| | - Da-Young Lee
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States
| | - Carlos Gongora-Canul
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States
- Tecnológico Nacional de México, Instituto Tecnológico de Conkal, Yucatán, Mexico
| | - Tiffanna J. Ross
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States
| | - Camila R. Da Silva
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States
| | - Darcy E. P. Telenko
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States
| | - Stephen B. Goodwin
- USDA-Agricultural Research Service, Crop Production and Pest Control Research Unit, West Lafayette, IN, United States
| | - Steven R. Scofield
- USDA-Agricultural Research Service, Crop Production and Pest Control Research Unit, West Lafayette, IN, United States
| | - Sungchan Oh
- Institute for Plant Sciences, Purdue University, West Lafayette, IN, United States
| | - Jinha Jung
- Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, United States
| | - C. D. Cruz
- Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States
| |
Collapse
|
3
|
Murguia-Cozar A, Macedo-Cruz A, Fernandez-Reynoso DS, Salgado Transito JA. Recognition of Maize Phenology in Sentinel Images with Machine Learning. SENSORS (BASEL, SWITZERLAND) 2021; 22:94. [PMID: 35009637 PMCID: PMC8747376 DOI: 10.3390/s22010094] [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: 10/21/2021] [Revised: 12/01/2021] [Accepted: 12/07/2021] [Indexed: 06/14/2023]
Abstract
The scarcity of water for agricultural use is a serious problem that has increased due to intense droughts, poor management, and deficiencies in the distribution and application of the resource. The monitoring of crops through satellite image processing and the application of machine learning algorithms are technological strategies with which developed countries tend to implement better public policies regarding the efficient use of water. The purpose of this research was to determine the main indicators and characteristics that allow us to discriminate the phenological stages of maize crops (Zea mays L.) in Sentinel 2 satellite images through supervised classification models. The training data were obtained by monitoring cultivated plots during an agricultural cycle. Indicators and characteristics were extracted from 41 Sentinel 2 images acquired during the monitoring dates. With these images, indicators of texture, vegetation, and colour were calculated to train three supervised classifiers: linear discriminant (LD), support vector machine (SVM), and k-nearest neighbours (kNN) models. It was found that 45 of the 86 characteristics extracted contributed to maximizing the accuracy by stage of development and the overall accuracy of the trained classification models. The characteristics of the Moran's I local indicator of spatial association (LISA) improved the accuracy of the classifiers when applied to the L*a*b* colour model and to the near-infrared (NIR) band. The local binary pattern (LBP) increased the accuracy of the classification when applied to the red, green, blue (RGB) and NIR bands. The colour ratios, leaf area index (LAI), RGB colour model, L*a*b* colour space, LISA, and LBP extracted the most important intrinsic characteristics of maize crops with regard to classifying the phenological stages of the maize cultivation. The quadratic SVM model was the best classifier of maize crop phenology, with an overall accuracy of 82.3%.
Collapse
Affiliation(s)
- Alvaro Murguia-Cozar
- Colegio de Postgraduados, Campus Montecillo, Carretera Federal Mexico-Texcoco, km. 36.5, Montecillo, Texcoco 56230, State of Mexico, Mexico; (A.M.-C.); (D.S.F.-R.)
| | - Antonia Macedo-Cruz
- Colegio de Postgraduados, Campus Montecillo, Carretera Federal Mexico-Texcoco, km. 36.5, Montecillo, Texcoco 56230, State of Mexico, Mexico; (A.M.-C.); (D.S.F.-R.)
| | - Demetrio Salvador Fernandez-Reynoso
- Colegio de Postgraduados, Campus Montecillo, Carretera Federal Mexico-Texcoco, km. 36.5, Montecillo, Texcoco 56230, State of Mexico, Mexico; (A.M.-C.); (D.S.F.-R.)
| | - Jorge Arturo Salgado Transito
- Colegio Mexicano de Especialistas en Recursos Naturales AC, De las Flores no. 8 s/n, San Luis Huexotla, Texcoco 56220, State of Mexico, Mexico;
| |
Collapse
|
4
|
Identification and Severity Monitoring of Maize Dwarf Mosaic Virus Infection Based on Hyperspectral Measurements. REMOTE SENSING 2021. [DOI: 10.3390/rs13224560] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Prompt monitoring of maize dwarf mosaic virus (MDMV) is critical for the prevention and control of disease and to ensure high crop yield and quality. Here, we first analyzed the spectral differences between MDMV-infected red leaves and healthy leaves and constructed a sensitive index (SI) for measurements. Next, based on the characteristic bands (Rλ) associated with leaf anthocyanins (Anth), we determined vegetation indices (VIs) commonly used in plant physiological and biochemical parameter inversion and established a vegetation index (VIc) by utilizing the combination of two arbitrary bands following the construction principles of NDVI, DVI, RVI, and SAVI. Furthermore, we developed classification models based on linear discriminant analysis (LDA) and support vector machine (SVM) in order to distinguish the red leaves from healthy leaves. Finally, we performed UR, MLR, PLSR, PCR, and SVM simulations on Anth based on Rλ, VIs, VIc, and Rλ + VIs + VIc and indirectly estimated the severity of MDMV infection based on the relationship between the reflection spectra and Anth. Distinct from those of the normal leaves, the spectra of red leaves showed strong reflectance characteristics at 640 nm, and SI increased with increasing Anth. Moreover, the accuracy of the two VIc-based classification models was 100%, which is significantly higher than that of the VIs and Rλ-based models. Among the Anth regression models, the accuracy of the MLR model based on Rλ + VIs + VIc was the highest (R2c = 0.85; R2v = 0.74). The developed models could accurately identify MDMV and estimate the severity of its infection, laying the theoretical foundation for large-scale remote sensing-based monitoring of this virus in the future.
Collapse
|