scholarly journals Weed discrimination based on the spectral response of the corn crop, Manabí, Ecuador

2021 ◽  
Vol 38 (4) ◽  
pp. 785-805
Author(s):  
Cinthya Pinargote ◽  
Henry Pacheco
Keyword(s):  
Precision Agriculture ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Bare Soil ◽  
Kappa Index ◽  
Map Algebra ◽  
Agronomic Practices ◽  
Corn Crop ◽  
Crop Area

Precision agriculture allows to gain efficiency, sustainability and profitability, since it provides great benefits in reducing the environmental impact of agriculture, economic risks and at the same time contributes to controlling the vigor of crops and improving the quality of their yield. The objective of this research is to discriminate weeds within the corn crop, based on their spectral response. For this, the advanced EBEE SQ agricultural drone was used, with which multispectral images were captured through its Parrot Sequoia camera. The images were processed with software in Geographic Information Systems (GIS). With the multispectral bands, different vegetation indices were calculated such as NDVI, NDVIAS, NGRDI, NDRE, GNDVI, using map algebra tools in specialized programs. A supervised classification was applied to the different indices to discriminate the different land covers, which obtained a precision of 93% and a Kappa index of 0.93. The results allowed to clearly differentiate the coverage of crops, weeds and bare soil. The data showed that both early-growing and developed weeds occupy 38% of the crop area. With this information, it is possible to improve the planning of agronomic practices, adding the herbicide at the specific site of the weeds.

scholarly journals Mapeamento Temático da Cobertura Vegetal na Microrregião do Sertão do São Francisco Alagoano, Utilizando Imagens TM LANDSAT 5 (Thematic Mapping of Plant Cover in Microregion Sertão of the San Francisco Alagoas, Images Using TM LANDSAT 5)

2013 ◽  
Vol 5 (5) ◽  
pp. 1121
Author(s):  
Heliofábio Barros Gomes ◽  
Rosiberto S. da S. Junior ◽  
Frederico Tejo De Paci ◽  
Danilo K. C. De Lima ◽  
Pedro H. P. De Castro ◽  
...  
Keyword(s):  
San Francisco ◽  
Precision Agriculture ◽  
Meteorological Data ◽  
Spectral Response ◽  
Plant Cover ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Area Index ◽  
Thematic Mapping ◽  
Density And Biomass

 Atualmente o uso de técnicas de gerenciamento de fazendas utilizando ferramentas de agricultura de precisão vem se tornando cada vez mais comum. Uma dessas ferramentas é a obtenção de informações da resposta espectral dos alvos, cujas aplicações exigem a consideração de vários fatores como a textura do solo e o tipo de vegetação em estudo, pois os mesmos podem influenciar na interpretação dos dados. Os índices de vegetação têm sido muito utilizados no monitoramento de áreas vegetadas, na determinação e estimativa do índice de área foliar, biomassa e da radiação fotossinteticamente ativa. Os índices foram calculados através de etapas do Algoritmo SEBAL (Balanço de Energia da Superfície do Solo), mediante dados de imagens do TM – LANDSAT 5. Os resultados mostraram que ocorreu uma variação na cobertura vegetal da região em estudo, no sentido de alteração negativa da densidade e biomassa. A variação da densidade foi mais acentuada em 2008 do que em 2006 conforme resultados apresentados nos índices estudados. Os resultados obtidos demonstraram que o algoritmo SEBAL teve bom desempenho em escala regional na estimativa dos Índices de Vegetação, com potenciais para serem aplicados em áreas onde a disponibilidade de dados meteorológicos são limitantes.Palavras-chave: NDVI, SAVI, Sensoriamento Remoto. Thematic Mapping of Plant Cover in Microregion Sertão of the San Francisco Alagoas, Images Using TM LANDSAT 5 ABSTRACTCurrently the use of farm management techniques using tools of precision agriculture is becoming increasingly common. One such tool is to obtain information from the spectral response of the targets whose applications require consideration of several factors such as soil texture and type of vegetation in the study, as they may influence the interpretation of data. The vegetation indices have been used in the monitoring of vegetated areas, the determination and estimation of leaf area index, biomass and PAR. Rates were calculated using the algorithm steps of the SEBAL (Surface Energy Balance of Soil) upon image data from TM – LANDSAT 5. The results showed that there was a change in the vegetation of the study area in order to change negative density and biomass. The variation of density was larger in 2008 than in 2006 according to results presented in the indices studied. The results showed that the algorithm SEBAL performed well on a regional scale in the estimation of vegetation indices, with potential for application in areas where the availability of meteorological data are limited.Keywords: NDVI, SAVI, REMOTE SENSING. 

scholarly journals Mapping of Winter Crops and Second-Crop Corn in the Paraná State-Brazil, Using Multitemporal Images From MODIS Sensor

2019 ◽  
Vol 11 (2) ◽  
pp. 477
Author(s):  
R. N. Santos ◽  
Erivelto Mercante ◽  
Jerry Adriani Johann ◽  
Carlos Henrique Wachholz de Souza ◽  
Carlos Eduardo Vizzotto Cattani ◽  
...  
Keyword(s):  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Accuracy Analysis ◽  
Kappa Index ◽  
Crop Development ◽  
Liss Iii ◽  
Winter Crops ◽  
Crop Area ◽  
Multitemporal Data

The use of effective technologies for the monitoring of agricultural crops should seek methodologies that provide information regarding crop development, preferably before harvesting. The study of the monitoring and/or estimation of areas using vegetation indices derived from multitemporal data from MODIS sensors is being studied in the search for greater objectivity of the generated values. In this context, the objective of this study was to map areas with winter and second-crop corn using EVI/MODIS time series from the Terra and Aqua satellites, for the seasons from 2012 to 2014 in the Paraná state of Brazil. Accuracy analysis of the mappings was performed in spatial resolution images of 30 m (LISS-III and Landsat-8), to identify and validate the masks the crops of interest. The accuracy of the mapping obtained values of global precision 87.5%, 79.5%, and 82.0%, with Kappa index of 0.81, 0.69, and 0.73, in the 2012, 2013, and 2014 harvests, respectively. Comparing with data from the Brazilian Institute of Geography and Statistics (IBGE), the areas obtained by the mappings were underestimated for the second-crop corn in the 2012 and 2013 seasons and overestimated in 2014. The winter crops were overestimated for the three seasons investigated. The use of remote sensing data and techniques can contribute to a quick estimation of crop area information, and can assist in the surveys conducted by official institutions.

scholarly journals USING CALIBRATED RGB IMAGERY FROM LOW-COST UAVS FOR GRASSLAND MONITORING: CASE STUDY AT THE RENGEN GRASSLAND EXPERIMENT (RGE), GERMANY

2017 ◽  
Vol XLII-2/W6 ◽  
pp. 229-233 ◽  
Author(s):  
U. Lussem ◽  
J. Hollberg ◽  
J. Menne ◽  
J. Schellberg ◽  
G. Bareth
Keyword(s):  
Precision Agriculture ◽  
Low Cost ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Field Measurements ◽  
Fertilizer Application ◽  
Real Time Analysis ◽  
Rgb Images ◽  
Intensively Managed ◽  
Cost Efficient

Monitoring the spectral response of intensively managed grassland throughout the growing season allows optimizing fertilizer inputs by monitoring plant growth. For example, site-specific fertilizer application as part of precision agriculture (PA) management requires information within short time. But, this requires field-based measurements with hyper- or multispectral sensors, which may not be feasible on a day to day farming practice. Exploiting the information of RGB images from consumer grade cameras mounted on unmanned aerial vehicles (UAV) can offer cost-efficient as well as near-real time analysis of grasslands with high temporal and spatial resolution. The potential of RGB imagery-based vegetation indices (VI) from consumer grade cameras mounted on UAVs has been explored recently in several. However, for multitemporal analyses it is desirable to calibrate the digital numbers (DN) of RGB-images to physical units. In this study, we explored the comparability of the RGBVI from a consumer grade camera mounted on a low-cost UAV to well established vegetation indices from hyperspectral field measurements for applications in grassland. The study was conducted in 2014 on the Rengen Grassland Experiment (RGE) in Germany. Image DN values were calibrated into reflectance by using the Empirical Line Method (Smith & Milton 1999). Depending on sampling date and VI the correlation between the UAV-based RGBVI and VIs such as the NDVI resulted in varying R2 values from no correlation to up to 0.9. These results indicate, that calibrated RGB-based VIs have the potential to support or substitute hyperspectral field measurements to facilitate management decisions on grasslands.

The Orientation of Gastric Biopsy Samples Improves the Interobserver Agreement of the OLGA Staging System

2017 ◽  
Vol 26 (4) ◽  
pp. 351-356 ◽  
Author(s):  
Bogdan Cotruta ◽  
Cristian Gheorghe ◽  
Razvan Iacob ◽  
Mona Dumbrava ◽  
Cristina Radu ◽  
...  
Keyword(s):  
Intestinal Metaplasia ◽  
Lamina Propria ◽  
Case Series ◽  
Gastric Biopsy ◽  
Gastric Atrophy ◽  
Observer Agreement ◽  
Gastric Body ◽  
Kappa Index ◽  
Gastric Biopsies

Background & Aims: Evaluation of severity and extension of gastric atrophy and intestinal metaplasia is recommended to identify subjects with a high risk for gastric cancer. The inter-observer agreement for the assessment of gastric atrophy is reported to be low. The aim of the study was to evaluate the inter-observer agreement for the assessment of severity and extension of gastric atrophy using oriented and unoriented gastric biopsy samples. Furthermore, the quality of biopsy specimens in oriented and unoriented samples was analyzed.Methods: A total of 35 subjects with dyspeptic symptoms addressed for gastrointestinal endoscopy that agreed to enter the study were prospectively enrolled. The OLGA/OLGIM gastric biopsies protocol was used. From each subject two sets of biopsies were obtained (four from the antrum, two oriented and two unoriented, two from the gastric incisure, one oriented and one unoriented, four from the gastric body, two oriented and two unoriented). The orientation of the biopsy samples was completed using nitrocellulose filters (Endokit®, BioOptica, Milan, Italy). The samples were blindly examined by two experienced pathologists. Inter-observer agreement was evaluated using kappa statistic for inter-rater agreement. The quality of histopathology specimens taking into account the identification of lamina propria was analyzed in oriented vs. unoriented samples. The samples with detectable lamina propria mucosae were defined as good quality specimens. Categorical data was analyzed using chi-square test and a two-sided p value <0.05 was considered statistically significant.Results: A total of 350 biopsy samples were analyzed (175 oriented / 175 unoriented). The kappa index values for oriented/unoriented OLGA 0/I/II/III and IV stages have been 0.62/0.13, 0.70/0.20, 0.61/0.06, 0.62/0.46, and 0.77/0.50, respectively. For OLGIM 0/I/II/III stages the kappa index values for oriented/unoriented samples were 0.83/0.83, 0.88/0.89, 0.70/0.88 and 0.83/1, respectively. No case of OLGIM IV stage was found in the present case series. Good quality histopathology specimens were described in 95.43% of the oriented biopsy samples, and in 89.14% of the unoriented biopsy samples, respectively (p=0.0275).Conclusion: The orientation of gastric biopsies specimens improves the inter-observer agreement for the assessment of gastric atrophy.Key words:  –  –  – .Abbreviations: H. pylori: Helicobacter pylori; OLGA: operative link for gastritis assessment; OLGIM: operative link on intestinal metaplasia assessment.

scholarly journals QVigourMap: A GIS Open Source Application for the Creation of Canopy Vigour Maps

Agronomy ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 952
Author(s):  
Lia Duarte ◽  
Ana Cláudia Teodoro ◽  
Joaquim J. Sousa ◽  
Luís Pádua
Keyword(s):  
Open Source ◽  
Precision Agriculture ◽  
Vegetation Indices ◽  
Data Interpretation ◽  
Vegetation Monitoring ◽  
Distribution Maps ◽  
Monitoring Process ◽  
Multi Temporal ◽  
The Creation ◽  
The Right

In a precision agriculture context, the amount of geospatial data available can be difficult to interpret in order to understand the crop variability within a given terrain parcel, raising the need for specific tools for data processing and analysis. This is the case for data acquired from Unmanned Aerial Vehicles (UAV), in which the high spatial resolution along with data from several spectral wavelengths makes data interpretation a complex process regarding vegetation monitoring. Vegetation Indices (VIs) are usually computed, helping in the vegetation monitoring process. However, a crop plot is generally composed of several non-crop elements, which can bias the data analysis and interpretation. By discarding non-crop data, it is possible to compute the vigour distribution for a specific crop within the area under analysis. This article presents QVigourMaps, a new open source application developed to generate useful outputs for precision agriculture purposes. The application was developed in the form of a QGIS plugin, allowing the creation of vigour maps, vegetation distribution maps and prescription maps based on the combination of different VIs and height information. Multi-temporal data from a vineyard plot and a maize field were used as case studies in order to demonstrate the potential and effectiveness of the QVigourMaps tool. The presented application can contribute to making the right management decisions by providing indicators of crop variability, and the outcomes can be used in the field to apply site-specific treatments according to the levels of vigour.

Variation in Postharvest Quality Attributes of Fresh Cowpea (Vigna unguiculata L.) Beans Harvested from Different Crop Mulching Regime

2021 ◽  
Author(s):  
Pankaj Kumar Kannaujia ◽  
Sakharam Kale ◽  
Ajinath Dukare ◽  
Vijay Singh Meena ◽  
Prerna Nath ◽  
...  
Keyword(s):  
Phenolic Content ◽  
Quality Parameters ◽  
Postharvest Quality ◽  
Total Phenolic ◽  
Organic Mulch ◽  
Agronomic Practices ◽  
Total Antioxidant ◽  
Wheat Straw Mulch ◽  
Physiological And Biochemical

Background: Present study, aimed to assess effect of organic and inorganic crop mulching on physical, physiological and biochemical quality of fresh cowpea beans.Methods: Cowpea (cv. Kashi Kanchan bush-type) was grown during two consecutive seasons from April 2018 to July 2019 under four different mulching treatments. Mulching treatments included no mulch; wheat straw mulch (organic mulch); black mulch and silver mulch. Black and silver mulches were made of 25 microns LDPE sheet. Cowpea was grown as per standard agronomic practices and physical, biochemical and postharvest quality parameters of beans were evaluated.Result: Results indicated that bean length (28.7cm) was highest under silver mulch whereas bean thickness (9.10mm), width (9.29mm) and 100 bean weight (1094.5g) were highest under organic mulch. Likewise, protein content (28.63%), total phenolic content (17.0µg GAE/100g) and total antioxidant activity (46.84µmol trolox equiv./100g) were found highest in beans produced under organic mulch. Overall results demonstrated that crop mulching could be used for enhancing the antioxidants, phenolic content of cowpea beans.

scholarly journals Implementation of Climate-Smart Agriculture to Boost Sugarcane Productivity in Indonesia

2021 ◽  
Vol 40 (2) ◽  
pp. 89
Author(s):  
Rivandi Pranandita Putra ◽  
Nindya Arini ◽  
Muhammad Rasyid Ridla Ranomahera
Keyword(s):  
Climate Change ◽  
Precision Agriculture ◽  
Sugar Industry ◽  
Limited Information ◽  
Agronomic Practices ◽  
Agronomic Management ◽  
The Government ◽  
Smart Agriculture ◽  
Sugarcane Productivity ◽  
Multi Stakeholder

<p>Sugar is one of Indonesia’s strategic commodities, but its production fluctuates over time and is still unable to comply with the national sugar demand. This condition may even get worst with climate change. Although climate-smart agriculture is a promising thing, it is basically a genuine concept for many farmers in Indonesia, including sugarcane growers. The paper briefly reviews and argues agronomic practices as a climate-smart agriculture approach adapted by sugarcane growers in Indonesia to increase its production under the changing climate. Some agronomic practices can be adopted by the Indonesian sugarcane growers as climate-smart agriculture, i.e., efficient irrigation, improved drainage of sugarcane plantations, the use of suitable sugarcane cultivars, green cane harvesting-trash blanketing, the amendment of soil organic matter, crop diversification, precision agriculture, and integrated pest management. From the Indonesian government’s side, research should be propped as there is limited information about the effectiveness of each aforementioned agronomic intervention to alleviating the adverse effect of climate change and to improving sugarcane growth. Practically, to ensure the success of climate-smart agriculture implementation in the Indonesian sugar industry, multistakeholders, i.e., sugarcane growers, researchers, civil society, and policymakers, should be involved, and the government needs to link these stakeholders.</p><p>Keywords: Sugarcane, productivity, climate-smart agriculture, agronomic management, precision agriculture</p><p> </p><p><strong>Abstrak</strong></p><p><strong>Implementasi Pertanian Cerdas Iklim untuk Meningkatkan Produktivitas Tebu di Indonesia</strong></p><p>Gula merupakan salah satu komoditas strategis Indonesia, namun produksinya mengalami fluktuasi dan belum dapat memenuhi kebutuhan gula nasional. Kondisi ini diperburuk oleh perubahan iklim. Pertanian cerdas iklim memberikan peluang besar bagi tanaman tebu untuk dapat beradaptasi dan memitigasi dampak perubahan iklim. Meskipun pertanian cerdas iklim menjanjikan, namun merupakan hal baru bagi banyak petani di Indonesia, termasuk petani tebu. Tulisan ini menelaah dan mengemukakan praktek agronomi sebagai pendekatan pertanian cerdas iklim yang dapat diterapkan petani tebu di Indonesia dengan tujuan meningkatkan produksi tebu di bawah kondisi perubahan iklim. Terdapat beberapa praktik agronomis sebagai bagian dari pertanian cerdas iklim yang dapat diadopsi petani tebu di Indonesia, seperti efisiensi irigasi, perbaikan sistem drainase, pemilihan kultivar tebu yang sesuai, pemanfaatan residu serasah tebu, peningkatan bahan organik tanah, diversifikasi tanaman, pertanian presisi, dan pengelolaan hama terpadu. Dari perspektif pemerintah Indonesia, penelitian harus didukung karena terbatasnya informasi efektivitas masing-masing intervensi agronomi tersebut untuk mengurangi dampak buruk perubahan iklim dan untuk meningkatkan pertumbuhan tebu. Secara praktis, untuk memastikan keberhasilan penerapan pertanian cerdas iklim pada industri gula Indonesia, multi-stakeholder yang terdiri atas petani tebu, peneliti, masyarakat sipil, dan pembuat kebijakan harus saling terlibat dan pemerintah perlu menghubungkan para pemangku kepentingan ini.</p><p>Kata kunci: Tebu, produktivitas, pertanian cerdas iklim, manajemen agronomis, pertanian presisi</p>

scholarly journals MONITORAMENTO DA COBERTURA VEGETAL POR SENSORIAMENTO REMOTO NO SEMIÁRIDO BRASILEIRO ATRAVÉS DE ÍNDICES DE VEGETAÇÃO

Nativa ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 708
Author(s):  
Caio Victor Santos Silva ◽  
Jhon Lennon Bezerra da Silva ◽  
Geber Barbosa De Albuquerque Moura ◽  
Pabrício Marcos Oliveira Lopes ◽  
Cristina Rodrigues Nascimento ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Environmental Changes ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Digital Processing ◽  
Semiarid Region ◽  
Dry Period ◽  
Biophysical Parameters ◽  
Brazilian Semiarid

São necessárias medidas que visem à proteção e conservação dos recursos hídricos e naturais de forma rápida e eficiente. As técnicas de sensoriamento remoto são essenciais para o monitoramento ambiental dos recursos no semiárido no espaço e no tempo. Objetivou-se monitorar e analisar à dinâmica da cobertura vegetal através da variabilidade espaço-temporal do albedo da superfície e índices de vegetação em região de Caatinga do semiárido brasileiro por sensoriamento remoto. A área de estudo é o município de Arcoverde, localizado no semiárido de Pernambuco. O estudo foi desenvolvido através de seis imagens orbitais do Landsat-5 do sensor TM. O processamento digital dos parâmetros biofísicos foi realizado pelo algoritmo SEBAL. Os resultados foram analisados através da estatística descritiva e quanto a sua variabilidade. Áreas possivelmente degradadas foram identificadas pelos altos valores de albedo e índices de vegetação significativamente menores, localizadas à sudoeste e noroeste da região. Os índices apresentaram comportamento similares, principalmente no período seco, com baixos valores sendo próximos de zero, áreas afetadas pelo período de seca no semiárido. O SAVI apresentou maior precisão, destacando melhor resposta espectral da vegetação. O sensoriamento remoto promoveu monitoramento espaço-temporal adequado, destacando principalmente o período classificado como climaticamente seco através do albedo e índices de vegetação.Palavras-chave: Caatinga; NDVI; SAVI; mudanças ambientais; SEBAL. MONITORING OF VEGETATION COVER BY REMOTE SENSING IN BRAZILIAN SEMIARID THROUGH VEGETATION INDICES ABSTRACT: Measures are needed aimed at the protection and conservation of water and natural resources quickly and efficiently. Remote sensing techniques are essential for the environmental monitoring of resources in the semiarid region in space and time. Aimed to monitor and analyze the dynamics of vegetation cover through the spatial-temporal variability of the surface albedo and indices of vegetation in the Caatinga region of the Brazilian semiarid by remote sensing. The study area is the municipality of Arcoverde, located in the semiarid of Pernambuco. The study was developed through six orbital images of Landsat-5 of the TM sensor. The digital processing of the biophysical parameters was performed by the SEBAL algorithm. The results were analyzed through descriptive statistics and their variability. Possibly degraded areas were identified by high albedo values and significantly lower vegetation indices, located in the southwest and northwest of the region. The indexes showed similar behavior, mainly in the dry period, with low values being close to zero, areas affected by the dry period in the semiarid. The SAVI presented higher accuracy, highlighting better spectral response of the vegetation. Remote sensing promoted adequate space-time monitoring, highlighting mainly the period classified as climatically dry through the albedo and vegetation indexes.Keywords: Caatinga; NDVI; SAVI; environmental changes; SEBAL.

scholarly journals Real-Time Automated Classification of Sky Conditions Using Deep Learning and Edge Computing

2021 ◽  
Vol 13 (19) ◽  
pp. 3859
Author(s):  
Joby M. Prince Czarnecki ◽  
Sathishkumar Samiappan ◽  
Meilun Zhou ◽  
Cary Daniel McCraine ◽  
Louis L. Wasson
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Quality ◽  
Convolutional Neural Network ◽  
Precision Agriculture ◽  
Edge Computing ◽  
Training Data ◽  
Learning Approaches ◽  
Sky Conditions

The radiometric quality of remotely sensed imagery is crucial for precision agriculture applications because estimations of plant health rely on the underlying quality. Sky conditions, and specifically shadowing from clouds, are critical determinants in the quality of images that can be obtained from low-altitude sensing platforms. In this work, we first compare common deep learning approaches to classify sky conditions with regard to cloud shadows in agricultural fields using a visible spectrum camera. We then develop an artificial-intelligence-based edge computing system to fully automate the classification process. Training data consisting of 100 oblique angle images of the sky were provided to a convolutional neural network and two deep residual neural networks (ResNet18 and ResNet34) to facilitate learning two classes, namely (1) good image quality expected, and (2) degraded image quality expected. The expectation of quality stemmed from the sky condition (i.e., density, coverage, and thickness of clouds) present at the time of the image capture. These networks were tested using a set of 13,000 images. Our results demonstrated that ResNet18 and ResNet34 classifiers produced better classification accuracy when compared to a convolutional neural network classifier. The best overall accuracy was obtained by ResNet34, which was 92% accurate, with a Kappa statistic of 0.77. These results demonstrate a low-cost solution to quality control for future autonomous farming systems that will operate without human intervention and supervision.

scholarly journals AUTOMATED CO-REGISTRATION OF INTRA-EPOCH AND INTER-EPOCH SERIES OF MULTISPECTRAL UAV IMAGES FOR CROP MONITORING

2020 ◽  
Vol V-1-2020 ◽  
pp. 309-316
Author(s):  
P. O. Mc’Okeyo ◽  
F. Nex ◽  
C. Persello ◽  
A. Vrieling
Keyword(s):  
Precision Agriculture ◽  
Spectral Response ◽  
Radiometric Calibration ◽  
Registration Accuracy ◽  
Maize Crop ◽  
Crop Monitoring ◽  
Speeded Up Robust Features ◽  
Novel Approach ◽  
Matching Strategy ◽  
Uav Images

Abstract. The application of UAV-based aerial imagery has advanced exponentially in the past two decades. This can be attributed to UAV operational flexibility, ultra-high spatial resolution, inexpensiveness, and UAV-based sensors enhancement. Nonetheless, the application of multitemporal series of multispectral UAV imagery still suffers significant misregistration errors, and therefore becoming a concern for applications such as precision agriculture. Direct image georeferencing and co-registration is commonly done using ground control points; this is usually costly and time consuming. This research proposes a novel approach for automatic co-registration of multitemporal UAV imagery using intensity-based keypoints. The Speeded Up Robust Features (SURF), Binary Robust Invariant Scalable Keypoints (BRISK), Maximally Stable Extremal Regions (MSER) and KAZE algorithms, were tested and parameters optimized. Image matching performance of these algorithms informed the decision to pursue further experiments with only SURF and KAZE. Optimally parametrized SURF and KAZE algorithms obtained co-registration accuracies of 0.1 and 0.3 pixels for intra-epoch and inter-epoch images respectively. To obtain better intra-epoch co-registration accuracy, collective band processing is advised whereas one-to-one matching strategy is recommended for inter-epoch co-registration. The results were tested using a maize crop monitoring case and the; comparison of spectral response of vegetation between the UAV sensors, Parrot Sequoia and Micro MCA was performed. Due to the missing incidence sensor, spectral and radiometric calibration of Micro MCA imagery is observed to be key in achieving optimal response. Also, the cameras have different specifications and thus differ in the quality of their respective photogrammetric outputs.

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