Precision Agriculture Using Remote Sensing and GIS for Peanut Crop Production in Arid Land

Elsharkawy, Mohamed M.; Sheta, Abdel Aziz; El-Wahed, M. S. A. A.; Arafat, S.M. Yasir; Behiery, O

doi:10.9734/ijpss/2016/20539

Cited by 9 publications

(7 citation statements)

References 15 publications

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“…Hyperspectral analysis, spanning ultraviolet (UV), visible, near-infrared (NIR), and shortwave infrared (SWIR) ranges, has emerged as a powerful tool in the realm of agriculture and plant sciences [ 2 , 11 , 12 ]. This technology leverages the unique spectral “fingerprints” of different plant species and conditions, thereby facilitating applications such as plant classification, growth assessments, and the monitoring of plant health and leaf structure–water characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…However, it is essential to acknowledge that the success of these methods hinges on several factors. These include the quality of the spectral data, the accuracy of the classification algorithms employed, and the specific conditions in which the specific pigments are found in the leaves of plants [ 2 , 11 , 12 ]. Hence, while promising, the application of hyperspectral analysis demands careful consideration of these variables.…”

Section: Introductionmentioning

confidence: 99%

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Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops

Falcioni

Antunes

Demattê

et al. 2023

Plants

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Reflectance spectroscopy, in combination with machine learning and artificial intelligence algorithms, is an effective method for classifying and predicting pigments and phenotyping in agronomic crops. This study aims to use hyperspectral data to develop a robust and precise method for the simultaneous evaluation of pigments, such as chlorophylls, carotenoids, anthocyanins, and flavonoids, in six agronomic crops: corn, sugarcane, coffee, canola, wheat, and tobacco. Our results demonstrate high classification accuracy and precision, with principal component analyses (PCAs)-linked clustering and a kappa coefficient analysis yielding results ranging from 92 to 100% in the ultraviolet–visible (UV–VIS) to near-infrared (NIR) to shortwave infrared (SWIR) bands. Predictive models based on partial least squares regression (PLSR) achieved R2 values ranging from 0.77 to 0.89 and ratio of performance to deviation (RPD) values over 2.1 for each pigment in C3 and C4 plants. The integration of pigment phenotyping methods with fifteen vegetation indices further improved accuracy, achieving values ranging from 60 to 100% across different full or range wavelength bands. The most responsive wavelengths were selected based on a cluster heatmap, β-loadings, weighted coefficients, and hyperspectral vegetation index (HVI) algorithms, thereby reinforcing the effectiveness of the generated models. Consequently, hyperspectral reflectance can serve as a rapid, precise, and accurate tool for evaluating agronomic crops, offering a promising alternative for monitoring and classification in integrated farming systems and traditional field production. It provides a non-destructive technique for the simultaneous evaluation of pigments in the most important agronomic plants.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops

Falcioni

Antunes

Demattê

et al. 2023

Plants

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“…Assessment of soil properties including soil fertility parameter using hyperspectral remote sensing (HRS) data from Hyperion satellite and spectroradiometer instrument was initiated and also reviewed in Indian context [52]. Soil peanut productivity zones at the eastern Nile Delta, Egypt were developed with good accuracy using soil properties, topography -slope, climate based on the structure of FAO land suitability & Spatial Multi Criteria Evaluation tree (SMCE) in GIS and subsequently field management zones were prepared with herbs infection map and soil adjusted vegetation Index (SAVI) from Landsat image data [53].…”

Section: Remote Sensing Techniquesmentioning

confidence: 99%

Site-specific Crop Nutrient Management for Precision Agriculture – A Review

Gorai

Yadav

Choudhary

et al. 2021

CJAST

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Present irrational crop and nutrient management practices have raised several concerns of high merit. The concerns include low factor productivity or nutrient use efficiency (NUE), declining crop productivity, farmer’s profitability, impaired soil health and ecological contamination. Site-specific nutrient management (SSNM), after considering indigenous nutrient supplying capacity of soil using plant and soil analysis, can feed the crop in synchrony with its nutrient requirement in different physiological growth stages. Besides, several modern geospatial techniques viz. remote sensing techniques, geographic information system (GIS), global positioning system (GPS), proximal sensing; information and communication technologies (ICTs) including decision support system, smartphone apps and web services can also assist in diagnosis of soil and crop nutrient status, fertilizer recommendation and its dissemination to users. Optical and thermal remote sensing can effectively detect crop stress including nitrogen (N) deficiency through several vegetation indices especially normalized difference vegetation index (NDVI). GIS techniques with spatial data acquired by GPS, can create spatial variability map and management zone (MZ) for precise farm operations including variable rate fertilization. Proximal crop sensors viz. chlorophyll meter and Green Seeker can also recognize crop nitrogen status and promote fertilizer N use efficiency by synchronizing fertilizer N supply with crop requirement. Even proximal soil sensing using electromagnetic radiation and contact electrode can estimate soil properties like soil pH, electrical conductivity, major and micronutrient content. Several decision support systems such as QUEFTS based model, crop manager, nutrient expert® and smartphone apps like ‘crop doctor’ can suggest for precise application of agro-inputs to rural youths and farmers. Yield monitoring and mapping tool can generate historical GIS database for spatial variability of crop yield under farmers’ crop management practices and assessment of nutrient uptake. Variable rate machinery based on variability map and sensor technologies can also be used for fertilization under different management zones. Therefore, SSNM technologies can enhance NUE; improve and sustain crop productivity, profitability; avoid nutrient wastage; maintain good soil health and environmental safety.

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“…The FAO-Land Cover Classification System (LCCS) was used for mapping many desert areas in Egypt (Afify, 2009;Afify et al, 2022;Arafat et al, 2014b;Sheta et al, 2016). Remote sensing imagery (RS) have been used widely for mapping crops (Elsharkawy et al, 2016a(Elsharkawy et al, , 2016bFarg et al, 2020). Satellite sensors produce imageries of high spatial features in a fast, precise and low-cost data over large areas for mapping soil and water quality, compared with classic techniques.…”

Section: Introductionmentioning

confidence: 99%

Land evaluation of different land cover classes for agricultural use in south east of Egypt using remote sensing data

Mohamed

2023

Scientific Journal of Agricultural Sciences

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This study carried out in an area south Egypt covering 386,171.04 hectares (ha). Remote sensed data were manipulated for defining the land cover features. Setting up Digital Elevation Model (DEM) configures the network of natural drainage flows via the descending slopes. It was a guide for allocating micro dams as water harvesting sites as well as minimizing the runoff hazards and managing the priority of land use suitability versus flood hazards. Land cover units were defined as terrestrial irrigated areas of sequentially herbaceous crops and permanent trees covering 1387.83 ha in levees; 1608.82 ha in point bar; 718.89 ha in bow bar and 23938.29 ha in alluvial plain. Terrestrial natural vegetation is dominated by xerophytes herbaceous in wadis (123533.56 ha). Terrestrial non-vegetated areas includes bare areas of rock land (98102.59 ha) and 67201.57 in bajada. Artificial non-linear surfaces include buildings (5403.59 ha) and linear features include roads and railways (875.78 ha). Aquatic areas include artificial irrigation and drainage canals (1062.15 ha) and natural water body of River Nile course (2785.92 ha). Current land suitability for specific Land Utilization Types (LUTs) was assessed by matching soil attributes with the growth requirement of each LUT. Current land suitability can be improved by decreasing of salinity and sodicity practices. Accordingly, the land units can be potentially more profitable for increasing the ability of extra crops to be more productive. Old cultivated areas are the most profitable land cover/use units highly suitable (S1) for all LUTs comparing with other land cover categories.

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Precision Agriculture Using Remote Sensing and GIS for Peanut Crop Production in Arid Land

Cited by 9 publications

References 15 publications

Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops

Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops

Site-specific Crop Nutrient Management for Precision Agriculture – A Review

Land evaluation of different land cover classes for agricultural use in south east of Egypt using remote sensing data

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