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Cudjoe DK, Virlet N, Castle M, Riche AB, Mhada M, Waine TW, Mohareb F, Hawkesford MJ. Field phenotyping for African crops: overview and perspectives. FRONTIERS IN PLANT SCIENCE 2023; 14:1219673. [PMID: 37860243 PMCID: PMC10582954 DOI: 10.3389/fpls.2023.1219673] [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: 05/09/2023] [Accepted: 09/07/2023] [Indexed: 10/21/2023]
Abstract
Improvements in crop productivity are required to meet the dietary demands of the rapidly-increasing African population. The development of key staple crop cultivars that are high-yielding and resilient to biotic and abiotic stresses is essential. To contribute to this objective, high-throughput plant phenotyping approaches are important enablers for the African plant science community to measure complex quantitative phenotypes and to establish the genetic basis of agriculturally relevant traits. These advances will facilitate the screening of germplasm for optimum performance and adaptation to low-input agriculture and resource-constrained environments. Increasing the capacity to investigate plant function and structure through non-invasive technologies is an effective strategy to aid plant breeding and additionally may contribute to precision agriculture. However, despite the significant global advances in basic knowledge and sensor technology for plant phenotyping, Africa still lags behind in the development and implementation of these systems due to several practical, financial, geographical and political barriers. Currently, field phenotyping is mostly carried out by manual methods that are prone to error, costly, labor-intensive and may come with adverse economic implications. Therefore, improvements in advanced field phenotyping capabilities and appropriate implementation are key factors for success in modern breeding and agricultural monitoring. In this review, we provide an overview of the current state of field phenotyping and the challenges limiting its implementation in some African countries. We suggest that the lack of appropriate field phenotyping infrastructures is impeding the development of improved crop cultivars and will have a detrimental impact on the agricultural sector and on food security. We highlight the prospects for integrating emerging and advanced low-cost phenotyping technologies into breeding protocols and characterizing crop responses to environmental challenges in field experimentation. Finally, we explore strategies for overcoming the barriers and maximizing the full potential of emerging field phenotyping technologies in African agriculture. This review paper will open new windows and provide new perspectives for breeders and the entire plant science community in Africa.
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Affiliation(s)
- Daniel K. Cudjoe
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
- School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire, United Kingdom
| | - Nicolas Virlet
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
| | - March Castle
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
| | - Andrew B. Riche
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
| | - Manal Mhada
- AgroBiosciences Department, Mohammed VI Polytechnic University (UM6P), Benguérir, Morocco
| | - Toby W. Waine
- School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire, United Kingdom
| | - Fady Mohareb
- School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire, United Kingdom
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Oteng-Frimpong R, Karikari B, Sie EK, Kassim YB, Puozaa DK, Rasheed MA, Fonceka D, Okello DK, Balota M, Burow M, Ozias-Akins P. Multi-locus genome-wide association studies reveal genomic regions and putative candidate genes associated with leaf spot diseases in African groundnut ( Arachis hypogaea L.) germplasm. FRONTIERS IN PLANT SCIENCE 2023; 13:1076744. [PMID: 36684745 PMCID: PMC9849250 DOI: 10.3389/fpls.2022.1076744] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
Early leaf spot (ELS) and late leaf spot (LLS) diseases are the two most destructive groundnut diseases in Ghana resulting in ≤ 70% yield losses which is controlled largely by chemical method. To develop leaf spot resistant varieties, the present study was undertaken to identify single nucleotide polymorphism (SNP) markers and putative candidate genes underlying both ELS and LLS. In this study, six multi-locus models of genome-wide association study were conducted with the best linear unbiased predictor obtained from 294 African groundnut germplasm screened for ELS and LLS as well as image-based indices of leaf spot diseases severity in 2020 and 2021 and 8,772 high-quality SNPs from a 48 K SNP array Axiom platform. Ninety-seven SNPs associated with ELS, LLS and five image-based indices across the chromosomes in the 2 two sub-genomes. From these, twenty-nine unique SNPs were detected by at least two models for one or more traits across 16 chromosomes with explained phenotypic variation ranging from 0.01 - 62.76%, with exception of chromosome (Chr) 08 (Chr08), Chr10, Chr11, and Chr19. Seventeen potential candidate genes were predicted at ± 300 kbp of the stable/prominent SNP positions (12 and 5, down- and upstream, respectively). The results from this study provide a basis for understanding the genetic architecture of ELS and LLS diseases in African groundnut germplasm, and the associated SNPs and predicted candidate genes would be valuable for breeding leaf spot diseases resistant varieties upon further validation.
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Affiliation(s)
- Richard Oteng-Frimpong
- Groundnut Improvement Program, Council for Scientific and Industrial Research (CSIR)-Savanna Agricultural Research Institute, Tamale, Ghana
| | - Benjamin Karikari
- Department of Agricultural Biotechnology, Faculty of Agriculture, Food and Consumer Sciences, University for Development Studies, Tamale, Ghana
| | - Emmanuel Kofi Sie
- Groundnut Improvement Program, Council for Scientific and Industrial Research (CSIR)-Savanna Agricultural Research Institute, Tamale, Ghana
| | - Yussif Baba Kassim
- Groundnut Improvement Program, Council for Scientific and Industrial Research (CSIR)-Savanna Agricultural Research Institute, Tamale, Ghana
| | - Doris Kanvenaa Puozaa
- Groundnut Improvement Program, Council for Scientific and Industrial Research (CSIR)-Savanna Agricultural Research Institute, Tamale, Ghana
| | - Masawudu Abdul Rasheed
- Groundnut Improvement Program, Council for Scientific and Industrial Research (CSIR)-Savanna Agricultural Research Institute, Tamale, Ghana
| | - Daniel Fonceka
- Centre d’Etude Régional pour l’Amélioration de l’Adaptation àla Sécheresse (CERAAS), Institut Sénégalais de Recherches Agricoles (ISRA), Thiès, Senegal
| | - David Kallule Okello
- Oil Crops Research Program, National Semi-Arid Resources Research Institute (NaSARRI), Soroti, Uganda
| | - Maria Balota
- School of Plant and Environmental Sciences, Tidewater Agricultural Research and Extension Center (AREC), Virginia Tech, Suffolk, VA, United States
| | - Mark Burow
- Texas A&M AgriLife Research and Department of Plant and Soil Science, Texas Tech University, Lubbock, TX, United States
| | - Peggy Ozias-Akins
- Institute of Plant Breeding Genetics and Genomics, University of Georgia, Tifton, GA, United States
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Pipatsitee P, Tisarum R, Taota K, Samphumphuang T, Eiumnoh A, Singh HP, Cha-Um S. Effectiveness of vegetation indices and UAV-multispectral imageries in assessing the response of hybrid maize (Zea mays L.) to water deficit stress under field environment. ENVIRONMENTAL MONITORING AND ASSESSMENT 2022; 195:128. [PMID: 36402920 DOI: 10.1007/s10661-022-10766-6] [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: 07/09/2021] [Accepted: 11/12/2022] [Indexed: 06/16/2023]
Abstract
Unmanned aerial vehicles (UAVs) equipped with multi-sensors are one of the most innovative technologies for measuring plant health and predicting final yield in field conditions, especially in the water deficit situation in rain-deprived regions. The objective of this investigation was to evaluate the individual plant and canopy-level measurements using UAV imageries in three different genotypes, Suwan4452 (drought-tolerant), Pac339, and S7328 (drought-sensitive) of maize (Zea mays L.) at vegetative and reproductive stages under WW (well-watered) and WD (water deficit) conditions. At the vegetative stage, only CWSI (crop water stress index) of Pac339 and S7328 under WD increased significantly by 1.86- and 1.69-fold over WW, whereas the vegetation indices (EVI2 (Enhanced Vegetation Index 2), OSAVI (Optimized Soil-Adjusted Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), NDRE (Normalized Difference Red Edge Index), and NDVI (Normalized Difference Vegetation Index)) derived from UAV multi-sensors did not vary. At the reproductive stage, CWSI in drought-sensitive genotype (S7328) under WD increased by 1.92-fold over WW. All the vegetation indices (EVI2, OSAVI, GNDVI, NDRE, and NDVI) of Pac339 and S7328 under WD decreased when compared with those of Suwan4452. NDVI derived from GreenSeeker® handheld and NDVI from UAV data was closely related (R2 = 0.5924). An increase in leaf temperature (Tleaf) and reduction in NDVI of WD stressed maize plants was observed (R2 = 0.5829) leading to yield loss (R2 = 0.5198). In summary, a close correlation was observed between the physiological data of individual plants and vegetation indices of canopy level (collected using a UAV platform) in drought-sensitive genotypes of maize crops under WD conditions, thus indicating its effectiveness in the classification of drought-tolerant genotypes.
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Affiliation(s)
- Piyanan Pipatsitee
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), 113 Thailand Science Park, Paholyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani, 12120, Thailand
| | - Rujira Tisarum
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), 113 Thailand Science Park, Paholyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani, 12120, Thailand
| | - Kanyarat Taota
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), 113 Thailand Science Park, Paholyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani, 12120, Thailand
| | - Thapanee Samphumphuang
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), 113 Thailand Science Park, Paholyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani, 12120, Thailand
| | - Apisit Eiumnoh
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), 113 Thailand Science Park, Paholyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani, 12120, Thailand
| | - Harminder Pal Singh
- Department of Environment Studies, Faculty of Science, Panjab University, Chandigarh, 160014, India
| | - Suriyan Cha-Um
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), 113 Thailand Science Park, Paholyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani, 12120, Thailand.
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Sie EK, Oteng-Frimpong R, Kassim YB, Puozaa DK, Adjebeng-Danquah J, Masawudu AR, Ofori K, Danquah A, Cazenave AB, Hoisington D, Rhoads J, Balota M. RGB-image method enables indirect selection for leaf spot resistance and yield estimation in a groundnut breeding program in Western Africa. FRONTIERS IN PLANT SCIENCE 2022; 13:957061. [PMID: 35991399 PMCID: PMC9387199 DOI: 10.3389/fpls.2022.957061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 07/06/2022] [Indexed: 06/15/2023]
Abstract
Early Leaf Spot (ELS) caused by the fungus Passalora arachidicola and Late Leaf Spot (LLS) also caused by the fungus Nothopassalora personata, are the two major groundnut (Arachis hypogaea L.) destructive diseases in Ghana. Accurate phenotyping and genotyping to develop groundnut genotypes resistant to Leaf Spot Diseases (LSD) and to increase groundnut production is critically important in Western Africa. Two experiments were conducted at the Council for Scientific and Industrial Research-Savanna Agricultural Research Institute located in Nyankpala, Ghana to explore the effectiveness of using RGB-image method as a high-throughput phenotyping tool to assess groundnut LSD and to estimate yield components. Replicated plots arranged in a rectangular alpha lattice design were conducted during the 2020 growing season using a set of 60 genotypes as the training population and 192 genotypes for validation. Indirect selection models were developed using Red-Green-Blue (RGB) color space indices. Data was collected on conventional LSD ratings, RGB imaging, pod weight per plant and number of pods per plant. Data was analyzed using a mixed linear model with R statistical software version 4.0.2. The results showed differences among the genotypes for the traits evaluated. The RGB-image method traits exhibited comparable or better broad sense heritability to the conventionally measured traits. Significant correlation existed between the RGB-image method traits and the conventionally measured traits. Genotypes 73-33, Gha-GAF 1723, Zam-ICGV-SM 07599, and Oug-ICGV 90099 were among the most resistant genotypes to ELS and LLS, and they represent suitable sources of resistance to LSD for the groundnut breeding programs in Western Africa.
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Affiliation(s)
- Emmanuel Kofi Sie
- Council for Scientific and Industrial Research-Savanna Agricultural Research Institute, Nyankpala, Ghana
- West Africa Centre for Crop Improvement (WACCI), University of Ghana, Legon, Ghana
| | - Richard Oteng-Frimpong
- Council for Scientific and Industrial Research-Savanna Agricultural Research Institute, Nyankpala, Ghana
| | - Yussif Baba Kassim
- Council for Scientific and Industrial Research-Savanna Agricultural Research Institute, Nyankpala, Ghana
| | - Doris Kanvenaa Puozaa
- Council for Scientific and Industrial Research-Savanna Agricultural Research Institute, Nyankpala, Ghana
| | - Joseph Adjebeng-Danquah
- Council for Scientific and Industrial Research-Savanna Agricultural Research Institute, Nyankpala, Ghana
| | - Abdul Rasheed Masawudu
- Council for Scientific and Industrial Research-Savanna Agricultural Research Institute, Nyankpala, Ghana
| | - Kwadwo Ofori
- West Africa Centre for Crop Improvement (WACCI), University of Ghana, Legon, Ghana
| | - Agyemang Danquah
- West Africa Centre for Crop Improvement (WACCI), University of Ghana, Legon, Ghana
| | | | - David Hoisington
- Feed the Future Innovation Lab for Peanut, University of Georgia, Athens, GA, United States
| | - James Rhoads
- Feed the Future Innovation Lab for Peanut, University of Georgia, Athens, GA, United States
| | - Maria Balota
- School of Plant and Environmental Sciences, Virginia Tech, Tidewater Agricultural Research and Extension Center, Suffolk, VA, United States
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Assessing Phytosanitary Application Efficiency of a Boom Sprayer Machine Using RGB Sensor in Grassy Fields. SUSTAINABILITY 2022. [DOI: 10.3390/su14063666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The systematic use of plant protection products is now being called into question with the growing awareness of the risks they can represent for the environment and human health. The application of precision agriculture technologies helps to improve agricultural production but also to rationalize input costs and improve ecological footprints. Here we present a study on fungicide application efficiency and its impact on the grass quality of a golf course green using the free open-source image analysis software FIJI (Image J) to analyze ground RGB (high-resolution digital cameras) and multispectral aerial imagery in combination with experimental data of spray pressure and hydraulic slot nozzle size of a boom sprayer machine. The multivariate regression model best explained variance in the normalized green-red difference index (NGRDI) as a relevant indicator of healthy turfgrass fields from the aerial, ground, and machine data set.
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Sarkar S, Ramsey AF, Cazenave AB, Balota M. Peanut Leaf Wilting Estimation From RGB Color Indices and Logistic Models. FRONTIERS IN PLANT SCIENCE 2021; 12:658621. [PMID: 34220885 PMCID: PMC8253229 DOI: 10.3389/fpls.2021.658621] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 03/30/2021] [Indexed: 06/13/2023]
Abstract
Peanut (Arachis hypogaea L.) is an important crop for United States agriculture and worldwide. Low soil moisture is a major constraint for production in all peanut growing regions with negative effects on yield quantity and quality. Leaf wilting is a visual symptom of low moisture stress used in breeding to improve stress tolerance, but visual rating is slow when thousands of breeding lines are evaluated and can be subject to personnel scoring bias. Photogrammetry might be used instead. The objective of this article is to determine if color space indices derived from red-green-blue (RGB) images can accurately estimate leaf wilting for breeding selection and irrigation triggering in peanut production. RGB images were collected with a digital camera proximally and aerially by a unmanned aerial vehicle during 2018 and 2019. Visual rating was performed on the same days as image collection. Vegetation indices were intensity, hue, saturation, lightness, a∗, b∗, u∗, v∗, green area (GA), greener area (GGA), and crop senescence index (CSI). In particular, hue, a∗, u∗, GA, GGA, and CSI were significantly (p ≤ 0.0001) associated with leaf wilting. These indices were further used to train an ordinal logistic regression model for wilting estimation. This model had 90% accuracy when images were taken aerially and 99% when images were taken proximally. This article reports on a simple yet key aspect of peanut screening for tolerance to low soil moisture stress and uses novel, fast, cost-effective, and accurate RGB-derived models to estimate leaf wilting.
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Affiliation(s)
- Sayantan Sarkar
- School of Plant and Environmental Sciences, Virginia Tech, Tidewater AREC, Suffolk, VA, United States
| | - A. Ford Ramsey
- Department of Agricultural and Applied Economics, Virginia Tech, Blacksburg, VA, United States
| | - Alexandre-Brice Cazenave
- School of Plant and Environmental Sciences, Virginia Tech, Tidewater AREC, Suffolk, VA, United States
| | - Maria Balota
- School of Plant and Environmental Sciences, Virginia Tech, Tidewater AREC, Suffolk, VA, United States
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Using Unmanned Aerial Vehicle and Ground-Based RGB Indices to Assess Agronomic Performance of Wheat Landraces and Cultivars in a Mediterranean-Type Environment. REMOTE SENSING 2021. [DOI: 10.3390/rs13061187] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The adaptability and stability of new bread wheat cultivars that can be successfully grown in rainfed conditions are of paramount importance. Plant improvement can be boosted using effective high-throughput phenotyping tools in dry areas of the Mediterranean basin, where drought and heat stress are expected to increase yield instability. Remote sensing has been of growing interest in breeding programs since it is a cost-effective technology useful for assessing the canopy structure as well as the physiological traits of large genotype collections. The purpose of this study was to evaluate the use of a 4-band multispectral camera on-board an unmanned aerial vehicle (UAV) and ground-based RGB imagery to predict agronomic traits as well as quantify the best estimation of leaf area index (LAI) in rainfed conditions. A collection of 365 bread wheat genotypes, including 181 Mediterranean landraces and 184 modern cultivars, was evaluated during two consecutive growing seasons. Several vegetation indices (VI) derived from multispectral UAV and ground-based RGB images were calculated at different image acquisition dates of the crop cycle. The modified triangular vegetation index (MTVI2) proved to have a good accuracy to estimate LAI (R2 = 0.61). Although the stepwise multiple regression analysis showed that grain yield and number of grains per square meter (NGm2) were the agronomic traits most suitable to be predicted, the R2 were low due to field trials were conducted under rainfed conditions. Moreover, the prediction of agronomic traits was slightly better with ground-based RGB VI rather than with UAV multispectral VIs. NDVI and GNDVI, from multispectral images, were present in most of the prediction equations. Repeated measurements confirmed that the ability of VIs to predict yield depends on the range of phenotypic data. The current study highlights the potential use of VI and RGB images as an efficient tool for high-throughput phenotyping under rainfed Mediterranean conditions.
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Su Y, Gabrielle B, Makowski D. A global dataset for crop production under conventional tillage and no tillage systems. Sci Data 2021; 8:33. [PMID: 33510175 PMCID: PMC7844240 DOI: 10.1038/s41597-021-00817-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 12/16/2020] [Indexed: 01/30/2023] Open
Abstract
No tillage (NT) is often presented as a means to grow crops with positive environmental externalities, such as enhanced carbon sequestration, improved soil quality, reduced soil erosion, and increased biodiversity. However, whether NT systems are as productive as those relying on conventional tillage (CT) is a controversial issue, fraught by a high variability over time and space. Here, we expand existing datasets to include the results of the most recent field experiments, and we produce a global dataset comparing the crop yields obtained under CT and NT systems. In addition to crop yield, our dataset also reports information on crop growing season, management practices, soil characteristics and key climate parameters throughout the experimental year. The final dataset contains 4403 paired yield observations between 1980 and 2017 for eight major staple crops in 50 countries. This dataset can help to gain insight into the main drivers explaining the variability of the productivity of NT and the consequence of its adoption on crop yields.
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Affiliation(s)
- Yang Su
- UMR ECOSYS, INRAE AgroParisTech, Université Paris-Saclay, 78850, Thiverval-Grignon, France.
| | - Benoit Gabrielle
- UMR ECOSYS, INRAE AgroParisTech, Université Paris-Saclay, 78850, Thiverval-Grignon, France
| | - David Makowski
- UMR Agronomie, INRAE AgroParisTech, Université Paris-Saclay, 78850, Thiverval-Grignon, France
- Applied mathematics and computer science (MIA 518), INRAE AgroParisTech, Université Paris-Saclay, 75005, Paris, France
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Leaf versus whole-canopy remote sensing methodologies for crop monitoring under conservation agriculture: a case of study with maize in Zimbabwe. Sci Rep 2020; 10:16008. [PMID: 32994539 PMCID: PMC7524805 DOI: 10.1038/s41598-020-73110-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 09/10/2020] [Indexed: 11/29/2022] Open
Abstract
Enhancing nitrogen fertilization efficiency for improving yield is a major challenge for smallholder farming systems. Rapid and cost-effective methodologies with the capability to assess the effects of fertilization are required to facilitate smallholder farm management. This study compares maize leaf and canopy-based approaches for assessing N fertilization performance under different tillage, residue coverage and top-dressing conditions in Zimbabwe. Among the measurements made on individual leaves, chlorophyll readings were the best indicators for both N content in leaves (R < 0.700) and grain yield (GY) (R < 0.800). Canopy indices reported even higher correlation coefficients when assessing GY, especially those based on the measurements of the vegetation density as the green area indices (R < 0.850). Canopy measurements from both ground and aerial platforms performed very similar, but indices assessed from the UAV performed best in capturing the most relevant information from the whole plot and correlations with GY and leaf N content were slightly higher. Leaf-based measurements demonstrated utility in monitoring N leaf content, though canopy measurements outperformed the leaf readings in assessing GY parameters, while providing the additional value derived from the affordability and easiness of using a pheno-pole system or the high-throughput capacities of the UAVs.
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Orsini R, Fiorentini M, Zenobi S. Evaluation of Soil Management Effect on Crop Productivity and Vegetation Indices Accuracy in Mediterranean Cereal-Based Cropping Systems. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3383. [PMID: 32549373 PMCID: PMC7348749 DOI: 10.3390/s20123383] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 06/05/2020] [Accepted: 06/12/2020] [Indexed: 12/12/2022]
Abstract
Mostly, precision agriculture applications include the acquisition and elaboration of images, and it is fundamental to understand how farmers' practices, such as soil management, affect those images and relate to the vegetation index. We investigated how long-term conservation agriculture practices, in comparison with conventional practices, can affect the yield components and the accuracy of five vegetation indexes. The experimental site is a part of a long-term experiment established in 1994 and is still ongoing that consists of a rainfed 2-year rotation with durum wheat and maize, where two unfertilized soil managements were repeated in the same plots every year. This study shows the superiority of no tillage over conventional tillage for both nutritional and productive aspects on durum wheat. The soil management affects the vegetation indexes' accuracy, which is related to the nitrogen nutrition status. No-tillage management, which is characterized by a higher content of soil organic matter and nitrogen availability into the soil, allows obtaining a higher accuracy than the conventional tillage. So, the users of multispectral cameras for precision agriculture applications must take into account the soil management, organic matter, and nitrogen content.
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Affiliation(s)
- Roberto Orsini
- Department of Agricultural, Food and Environmental Sciences (D3A), Agronomy and Crop Science Section, Marche Polytechnic University, 60131 Ancona, Italy; (M.F.); (S.Z.)
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Editorial for the Special Issue “Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery”. REMOTE SENSING 2020. [DOI: 10.3390/rs12060940] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
High-throughput crop phenotyping is harnessing the potential of genomic resources for the genetic improvement of crop production under changing climate conditions. As global food security is not yet assured, crop phenotyping has received increased attention during the past decade. This spectral issue (SI) collects 30 papers reporting research on estimation of crop phenotyping traits using unmanned ground vehicle (UGV) and unmanned aerial vehicle (UAV) imagery. Such platforms were previously not widely available. The special issue includes papers presenting recent advances in the field, with 22 UAV-based papers and 12 UGV-based articles. The special issue covers 16 RGB sensor papers, 11 papers on multi-spectral imagery, and further 4 papers on hyperspectral and 3D data acquisition systems. A total of 13 plants’ phenotyping traits, including morphological, structural, and biochemical traits are covered. Twenty different data processing and machine learning methods are presented. In this way, the special issue provides a good overview regarding potential applications of the platforms and sensors, to timely provide crop phenotyping traits in a cost-efficient and objective manner. With the fast development of sensors technology and image processing algorithms, we expect that the estimation of crop phenotyping traits supporting crop breeding scientists will gain even more attention in the future.
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Abstract
Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related to crops management, by capturing high spatial and temporal resolution images. These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase in the yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not been as robust as expected mainly due to the challenges confronted when selecting and deploying the relevant technologies, including the data acquisition and image processing methods. The main problem is that still there is no standardized workflow for the use of UAVs in such applications, as it is a relatively new area. In this article, we review the most recent applications of UAVs for Precision Agriculture. We discuss the most common applications, the types of UAVs exploited and then we focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of each one. We also point out the most popular processing methods of aerial imagery and discuss the outcomes of each method and the potential applications of each one in the farming operations.
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Buchaillot ML, Gracia-Romero A, Vergara-Diaz O, Zaman-Allah MA, Tarekegne A, Cairns JE, Prasanna BM, Araus JL, Kefauver SC. Evaluating Maize Genotype Performance under Low Nitrogen Conditions Using RGB UAV Phenotyping Techniques. SENSORS (BASEL, SWITZERLAND) 2019; 19:E1815. [PMID: 30995754 PMCID: PMC6514658 DOI: 10.3390/s19081815] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Revised: 04/01/2019] [Accepted: 04/08/2019] [Indexed: 11/29/2022]
Abstract
Maize is the most cultivated cereal in Africa in terms of land area and production, but low soil nitrogen availability often constrains yields. Developing new maize varieties with high and reliable yields using traditional crop breeding techniques in field conditions can be slow and costly. Remote sensing has become an important tool in the modernization of field-based high-throughput plant phenotyping (HTPP), providing faster gains towards the improvement of yield potential and adaptation to abiotic and biotic limiting conditions. We evaluated the performance of a set of remote sensing indices derived from red-green-blue (RGB) images along with field-based multispectral normalized difference vegetation index (NDVI) and leaf chlorophyll content (SPAD values) as phenotypic traits for assessing maize performance under managed low-nitrogen conditions. HTPP measurements were conducted from the ground and from an unmanned aerial vehicle (UAV). For the ground-level RGB indices, the strongest correlations to yield were observed with hue, greener green area (GGA), and a newly developed RGB HTPP index, NDLab (normalized difference Commission Internationale de I´Edairage (CIE)Lab index), while GGA and crop senescence index (CSI) correlated better with grain yield from the UAV. Regarding ground sensors, SPAD exhibited the closest correlation with grain yield, notably increasing in its correlation when measured in the vegetative stage. Additionally, we evaluated how different HTPP indices contributed to the explanation of yield in combination with agronomic data, such as anthesis silking interval (ASI), anthesis date (AD), and plant height (PH). Multivariate regression models, including RGB indices (R2 > 0.60), outperformed other models using only agronomic parameters or field sensors (R2 > 0.50), reinforcing RGB HTPP's potential to improve yield assessments. Finally, we compared the low-N results to the same panel of 64 maize genotypes grown under optimal conditions, noting that only 11% of the total genotypes appeared in the highest yield producing quartile for both trials. Furthermore, we calculated the grain yield loss index (GYLI) for each genotype, which showed a large range of variability, suggesting that low-N performance is not necessarily exclusive of high productivity in optimal conditions.
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Affiliation(s)
- Ma Luisa Buchaillot
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain.
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198 Lleida, Spain.
| | - Adrian Gracia-Romero
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain.
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198 Lleida, Spain.
| | - Omar Vergara-Diaz
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain.
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198 Lleida, Spain.
| | - Mainassara A Zaman-Allah
- International Maize and Wheat Improvement Center, CIMMYT Southern Africa Regional Office, P.O. Box MP163 Harare, Zimbabwe.
| | - Amsal Tarekegne
- International Maize and Wheat Improvement Center, CIMMYT Southern Africa Regional Office, P.O. Box MP163 Harare, Zimbabwe.
| | - Jill E Cairns
- International Maize and Wheat Improvement Center, CIMMYT Southern Africa Regional Office, P.O. Box MP163 Harare, Zimbabwe.
| | - Boddupalli M Prasanna
- International Maize and Wheat Improvement Center (CIMMYT), P.O. Box 1041 Nairobi, Kenya.
| | - Jose Luis Araus
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain.
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198 Lleida, Spain.
| | - Shawn C Kefauver
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain.
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198 Lleida, Spain.
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14
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Das B, Atlin GN, Olsen M, Burgueño J, Tarekegne A, Babu R, Ndou EN, Mashingaidze K, Moremoholo L, Ligeyo D, Matemba-Mutasa R, Zaman-Allah M, San Vicente F, Prasanna BM, Cairns JE. Identification of donors for low-nitrogen stress with maize lethal necrosis (MLN) tolerance for maize breeding in sub-Saharan Africa. EUPHYTICA: NETHERLANDS JOURNAL OF PLANT BREEDING 2019; 215:80. [PMID: 31057179 PMCID: PMC6445404 DOI: 10.1007/s10681-019-2406-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Accepted: 03/21/2019] [Indexed: 05/30/2023]
Abstract
After drought, a major challenge to smallholder farmers in sub-Saharan Africa is low-fertility soils with poor nitrogen (N)-supplying capacity. Many challenges in this region need to be overcome to create a viable fertilizer market. An intermediate solution is the development of maize varieties with an enhanced ability to take up or utilize N in severely depleted soils, and to more efficiently use the small amounts of N that farmers can supply to their crops. Over 400 elite inbred lines from seven maize breeding programs were screened to identify new sources of tolerance to low-N stress and maize lethal necrosis (MLN) for introgression into Africa-adapted elite germplasm. Lines with high levels of tolerance to both stresses were identified. Lines previously considered to be tolerant to low-N stress ranked in the bottom 10% under low-N confirming the need to replace these lines with new donors identified in this study. The lines that performed best under low-N yielded about 0. 5 Mg ha-1 (20%) more in testcross combinations than some widely used commercial parent lines such as CML442 and CML395. This is the first large scale study to identify maize inbred lines with tolerance to low-N stress and MLN in eastern and southern Africa.
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Affiliation(s)
- Biswanath Das
- International Maize and Wheat Improvement Center (CIMMYT), United Nations Avenue, Gigiri, Village Market, PO Box 1041, Nairobi, 00621 Kenya
| | - Gary N. Atlin
- Bill & Melinda Gates Foundation, PO Box 23350, Seattle, WA 98102 USA
| | - Michael Olsen
- International Maize and Wheat Improvement Center (CIMMYT), United Nations Avenue, Gigiri, Village Market, PO Box 1041, Nairobi, 00621 Kenya
| | - Juan Burgueño
- CIMMYT, km. 35 Carr. Mexico-Veracruz, Texcoco, Edo. de Mexico, DF Mexico
| | | | | | - Eric N. Ndou
- Agricultural Research Council-Grain Crop Institute, Private Bag X1251, Potchestroom, South Africa
| | - Kingstone Mashingaidze
- Agricultural Research Council-Grain Crop Institute, Private Bag X1251, Potchestroom, South Africa
| | - Lieketso Moremoholo
- Agricultural Research Council-Grain Crop Institute, Private Bag X1251, Potchestroom, South Africa
| | - Dickson Ligeyo
- Kenya Agriculture and Livestock Research Organization, Kitale, Kenya
| | | | | | - Felix San Vicente
- CIMMYT, km. 35 Carr. Mexico-Veracruz, Texcoco, Edo. de Mexico, DF Mexico
| | - B. M. Prasanna
- International Maize and Wheat Improvement Center (CIMMYT), United Nations Avenue, Gigiri, Village Market, PO Box 1041, Nairobi, 00621 Kenya
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