scholarly journals Smartphone Application Development for Rice Field Management Through Aerial Imagery and Normalised Difference Vegetation Index (NDVI) Analysis

2021 ◽  
Vol 29 (2) ◽  
Author(s):  
Nor Athirah Roslin ◽  
Nik Norasma Che’Ya ◽  
Rhushalshafira Rosle ◽  
Mohd Razi Ismail
Keyword(s):  
Weed Management ◽  
Human Error ◽  
Vegetation Index ◽  
Smartphone Application ◽  
Mobile App ◽  
Acceptance Test ◽  
Multispectral Imagery ◽  
Application Development ◽  
Spad Value ◽  
Normalised Difference Vegetation Index

In the current practices, farmers typically rely on the traditional method paper-based for farming data records, which leads to human error. However, the paper-based system can be improved by the mobile app technology to ease the farmers acquiring farm data as all of the farm information will be stored in digital form. This study aimed to develop a smartphone agricultural management app known as Padi2U and implement User Acceptance Test (UAT) for end-users. Padi2U was developed using Master App Builder software and integration with the multispectral imagery. Padi2U provides recommendations based on the Department of Agriculture’s (DOA), such as rice check, pest and disease control, and weed management. Through the Padi2U, farmers can access the field data to understand the crop health status online using the Normalised Difference Vegetation Index (NDVI) map derived from the multispectral images. The NDVI is correlated to the Soil Plant Analysis Development (SPAD) value, corresponding to R² = 0.4012. UAT results showed a 100 percent satisfaction score with suggestions were given to enhance the Padi2U performance. It shows that Padi2U can be improved to help farmers in the field monitoring virtually by integrating multispectral imagery and information from the field.

Yield and Physiological Response of Common Bean in Three Semi-Commercial Farmers’ Fields in Limpopo Province

2019 ◽  
Author(s):  
K K Ayisi ◽  
L Munjonji ◽  
K V Masekela
Keyword(s):  
Grain Yield ◽  
Common Bean ◽  
Physiological Response ◽  
Vegetation Index ◽  
Irrigation Management ◽  
Limpopo Province ◽  
Area Index ◽  
Spad Value ◽  
Normalised Difference Vegetation Index ◽  
Consumption Rates

Despite the importance of common bean (Phaseolus vulgaris) in human nutrition, its production in developing countries is far below consumption rates. A study was established to understand the yield performance and physiological response of common bean under three farmers’ field in the Limpopo Province. The results revealed significant variation in grain yield and biomass between the locations. Physiological parameters such as the Normalised Difference Vegetation Index (NDVI), Leaf Area Index (LAI), SPAD value and leaf gas exchanges were strongly influenced by the location, the crop growth stage and crop management. Dry bean crop performance was significantly lower at Masemola. Grain yield at Masemola was 30% less than that achieved at Dalmada and Mokopane. NDVI and LAI were also consistently lower at Masemola when compared to the other locations. This study revealed that poor yields by emerging farmers in dry areas are due to poor agronomic and irrigation management.

scholarly journals Comparison of Satellite and UAV-Based Multispectral Imagery for Vineyard Variability Assessment

2019 ◽  
Vol 11 (4) ◽  
pp. 436 ◽  
Author(s):  
Aleem Khaliq ◽  
Lorenzo Comba ◽  
Alessandro Biglia ◽  
Davide Ricauda Aimonino ◽  
Marcello Chiaberge ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Vegetation Index ◽  
Remotely Sensed ◽  
Aerial Images ◽  
Spatial And Temporal Variability ◽  
Multispectral Imagery ◽  
Remotely Sensed Data ◽  
Spectral Contribution ◽  
Normalised Difference Vegetation Index ◽  
Soil Status

In agriculture, remotely sensed data play a crucial role in providing valuable information on crop and soil status to perform effective management. Several spectral indices have proven to be valuable tools in describing crop spatial and temporal variability. In this paper, a detailed analysis and comparison of vineyard multispectral imagery, provided by decametric resolution satellite and low altitude Unmanned Aerial Vehicle (UAV) platforms, is presented. The effectiveness of Sentinel-2 imagery and of high-resolution UAV aerial images was evaluated by considering the well-known relation between the Normalised Difference Vegetation Index (NDVI) and crop vigour. After being pre-processed, the data from UAV was compared with the satellite imagery by computing three different NDVI indices to properly analyse the unbundled spectral contribution of the different elements in the vineyard environment considering: (i) the whole cropland surface; (ii) only the vine canopies; and (iii) only the inter-row terrain. The results show that the raw s resolution satellite imagery could not be directly used to reliably describe vineyard variability. Indeed, the contribution of inter-row surfaces to the remotely sensed dataset may affect the NDVI computation, leading to biased crop descriptors. On the contrary, vigour maps computed from the UAV imagery, considering only the pixels representing crop canopies, resulted to be more related to the in-field assessment compared to the satellite imagery. The proposed method may be extended to other crop typologies grown in rows or without intensive layout, where crop canopies do not extend to the whole surface or where the presence of weeds is significant.

scholarly journals Status of Phenological Research Using Sentinel-2 Data: A Review

2020 ◽  
Vol 12 (17) ◽  
pp. 2760
Author(s):  
Gourav Misra ◽  
Fiona Cawkwell ◽  
Astrid Wingler
Keyword(s):  
Vegetation Index ◽  
Temporal Frequency ◽  
Short Wave ◽  
Current State ◽  
Red Edge ◽  
Natural Grasslands ◽  
Wide Range ◽  
Normalised Difference Vegetation Index ◽  
Satellite Sensors ◽  
Sentinel 2

Remote sensing of plant phenology as an indicator of climate change and for mapping land cover has received significant scientific interest in the past two decades. The advancing of spring events, the lengthening of the growing season, the shifting of tree lines, the decreasing sensitivity to warming and the uniformity of spring across elevations are a few of the important indicators of trends in phenology. The Sentinel-2 satellite sensors launched in June 2015 (A) and March 2017 (B), with their high temporal frequency and spatial resolution for improved land mapping missions, have contributed significantly to knowledge on vegetation over the last three years. However, despite the additional red-edge and short wave infra-red (SWIR) bands available on the Sentinel-2 multispectral instruments, with improved vegetation species detection capabilities, there has been very little research on their efficacy to track vegetation cover and its phenology. For example, out of approximately every four papers that analyse normalised difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from Sentinel-2 imagery, only one mentions either SWIR or the red-edge bands. Despite the short duration that the Sentinel-2 platforms have been operational, they have proved their potential in a wide range of phenological studies of crops, forests, natural grasslands, and other vegetated areas, and in particular through fusion of the data with those from other sensors, e.g., Sentinel-1, Landsat and MODIS. This review paper discusses the current state of vegetation phenology studies based on the first five years of Sentinel-2, their advantages, limitations, and the scope for future developments.

Assessment of changes in the state of the rangelands of Inner Mongolia, China between 1998 and 2007 using remotely sensed data

2012 ◽  
Vol 34 (1) ◽  
pp. 103 ◽  
Author(s):  
Z. M. Hu ◽  
S. G. Li ◽  
J. W. Dong ◽  
J. W. Fan
Keyword(s):  
Inner Mongolia ◽  
Vegetation Index ◽  
Net Primary Productivity ◽  
Temporal Dynamics ◽  
Remotely Sensed ◽  
The State ◽  
Mean Annual Precipitation ◽  
Aboveground Net Primary Productivity ◽  
Remotely Sensed Data ◽  
Normalised Difference Vegetation Index

The spatial annual patterns of aboveground net primary productivity (ANPP) and precipitation-use efficiency (PUE) of the rangelands of the Inner Mongolia Autonomous Region of China, a region in which several projects for ecosystem restoration had been implemented, are described for the years 1998–2007. Remotely sensed normalised difference vegetation index and ANPP data, measured in situ, were integrated to allow the prediction of ANPP and PUE in each 1 km2 of the 12 prefectures of Inner Mongolia. Furthermore, the temporal dynamics of PUE and ANPP residuals, as indicators of ecosystem deterioration and recovery, were investigated for the region and each prefecture. In general, both ANPP and PUE were positively correlated with mean annual precipitation, i.e. ANPP and PUE were higher in wet regions than in arid regions. Both PUE and ANPP residuals indicated that the state of the rangelands of the region were generally improving during the period of 2000–05, but declined by 2007 to that found in 1999. Among the four main grassland-dominated prefectures, the recovery in the state of the grasslands in the Erdos and Chifeng prefectures was highest, and Xilin Gol and Chifeng prefectures was 2 years earlier than Erdos and Hunlu Buir prefectures. The study demonstrated that the use of PUE or ANPP residuals has some limitations and it is proposed that both indices should be used together with relatively long-term datasets in order to maximise the reliability of the assessments.

Assessing the impact of seasonal precipitation and temperature on vegetation in a grass-dominated rangeland

2014 ◽  
Vol 36 (2) ◽  
pp. 185 ◽  
Author(s):  
Fang Chen ◽  
Keith T. Weber
Keyword(s):  
Vegetation Index ◽  
Climatic Factors ◽  
Growing Season ◽  
Vegetation Growth ◽  
Factors Affecting ◽  
Growing Seasons ◽  
Normalised Difference Vegetation Index ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Impact ◽  
Precipitation And Temperature

Changes in vegetation are affected by many climatic factors and have been successfully monitored through satellite remote sensing over the past 20 years. In this study, the Normalised Difference Vegetation Index (NDVI), derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite, was selected as an indicator of change in vegetation. Monthly MODIS composite NDVI at a 1-km resolution was acquired throughout the 2004–09 growing seasons (i.e. April–September). Data describing daily precipitation and temperature, primary factors affecting vegetation growth in the semiarid rangelands of Idaho, were derived from the Surface Observation Gridding System and local weather station datasets. Inter-annual and seasonal fluctuations of precipitation and temperature were analysed and temporal relationships between monthly NDVI, precipitation and temperature were examined. Results indicated NDVI values observed in June and July were strongly correlated with accumulated precipitation (R2 >0.75), while NDVI values observed early in the growing season (May) as well as late in the growing season (August and September) were only moderately related with accumulated precipitation (R2 ≥0.45). The role of ambient temperature was also apparent, especially early in the growing season. Specifically, early growing-season temperatures appeared to significantly affect plant phenology and, consequently, correlations between NDVI and accumulated precipitation. It is concluded that precipitation during the growing season is a better predictor of NDVI than temperature but is interrelated with influences of temperature in parts of the growing season.

Temporal and spatial heterogeneity of drought impact on vegetation growth on the Inner Mongolian Plateau

2018 ◽  
Vol 40 (2) ◽  
pp. 113 ◽  
Author(s):  
Miao Bailing ◽  
Li Zhiyong ◽  
Liang Cunzhu ◽  
Wang Lixin ◽  
Jia Chengzhen ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Severity Index ◽  
Drought Severity ◽  
Summer Drought ◽  
Desert Steppe ◽  
Vegetation Types ◽  
Mongolian Plateau ◽  
Typical Steppe ◽  
Recent Climate ◽  
Normalised Difference Vegetation Index

Drought frequency and intensity have increased in recent decades, with consequences for the structure and function of ecosystems of the Inner Mongolian Plateau. In this study, the Palmer drought severity index (PDSI) was chosen to assess the extent and severity of drought between 1982 and 2011. The normalised difference vegetation index (NDVI) was used to analyse the responses of five different vegetation types (forest, meadow steppe, typical steppe, desert steppe and desert) to drought. Our results show that during the last 30 years, the frequency and intensity of droughts have increased significantly, especially in summer and autumn. The greatest decline in NDVI in response to drought was observed in typical steppe and desert steppe vegetation types. Compared with other seasons, maximum decline in NDVI was observed in summer. In addition, we found that NDVI in the five vegetation types showed a lag time of 1–2 months from drought in the spring and summer. Ancillary soil moisture conditions influenced the drought response, with desert steppe showing a stronger lag effect to spring and summer drought than the other vegetation types. Our results show that drought explains a high proportion of changes in NDVI, and suggest that recent climate change has been an important factor affecting vegetation productivity in the area.

scholarly journals Development of the TabacoQuest app for computerization of data collection on smoking in psychiatric nursing

2016 ◽  
Vol 24 (0) ◽  
Author(s):  
Renata Marques de Oliveira ◽  
Alexandre Freitas Duarte ◽  
Domingos Alves ◽  
Antonia Regina Ferreira Furegato
Keyword(s):  
Data Collection ◽  
Human Error ◽  
Technological Development ◽  
Psychiatric Patients ◽  
Mobile App ◽  
Psychiatric Nursing ◽  
Field Validation ◽  
Final Test ◽  
Computational Resources

ABSTRACT Objective: to develop a mobile app for research on the use of tobacco among psychiatric patients and the general population. Method: applied research with the technological development of an app for data collection on an Android tablet. For its development, we considered three criteria: data security, benefits for participants and optimization of the time of researchers. We performed tests with twenty fictitious participants and a final test with six pilots. Results: the app collects data, stores them in the database of the tablet and export then to an Excel spreadsheet. Resources: calculator, stopwatch, offline operation, branching logic, field validation and automatic tabulation. Conclusion: the app prevents human error, increases the quality of the data by validating them during the interview, allows the performing of automatic tabulation and makes the interviews less tiring. Its success may encourage the use of this and other computational resources by nurses as a research tool.

scholarly journals Soil bulk electrical resistivity and forage ground cover: nonlinear models in an alfalfa (Medicago sativa L.) case study

2015 ◽  
Vol 10 (4) ◽  
pp. 215 ◽  
Author(s):  
Roberta Rossi ◽  
Alessio Pollice ◽  
Gianfranco Bitella ◽  
Rocco Bochicchio ◽  
Amedeo D'Antonio ◽  
...  
Keyword(s):  
Electrical Resistivity ◽  
Medicago Sativa ◽  
Vegetation Index ◽  
Nonlinear Models ◽  
Ground Cover ◽  
Plant Productivity ◽  
Additive Models ◽  
Deep Soil ◽  
Soil Features ◽  
Normalised Difference Vegetation Index

Alfalfa is a highly productive and fertility-building forage crop; its performance, can be highly variable as influenced by within-field soil spatial variability. Characterising the relations between soil and forage- variation is important for optimal management. The aim of this work was to model the relationship between soil electrical resistivity (ER) and plant productivity in an alfalfa (<em>Medicago sativa</em> L.) field in Southern Italy. ER mapping was accomplished by a multi-depth automatic resistivity profiler. Plant productivity was assessed through normalised difference vegetation index (NDVI) at 2 dates. A non-linear relationship between NDVI and deep soil ER was modelled within the framework of generalised additive models. The best model explained 70% of the total variability. Soil profiles at six locations selected along a gradient of ER showed differences related to texture (ranging from clay to sandy-clay loam), gravel content (0 to 55%) and to the presence of a petrocalcic horizon. Our results prove that multi-depth ER can be used to localise permanent soil features that drive plant productivity.

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