List of articles (by subject) Soil
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Open Access Article
1 - Digital soil mapping of Maniyari Basin using Geospatial Techniques
Dipak Bej Naresh Kumar BaghmarBackground and objective:Remote sensing image data are often used as input in digital soil mapping (DSM). DSM nowadays is very popular rather than conventional soil maps it is an important tool in soil survey and sustainable agriculture planning. Spatial distribution of MoreBackground and objective:Remote sensing image data are often used as input in digital soil mapping (DSM). DSM nowadays is very popular rather than conventional soil maps it is an important tool in soil survey and sustainable agriculture planning. Spatial distribution of soil information in each pixel using laboratory observation data of soil samples plays an important role. The purpose of this study is to prepare a digital soil map using remote sensing.Materials and methods:Ninety soil samples were collected at a depth of up to 50 cm from various Physiography land units made with the help of the basis of slope and Land use Land cover (LULC) as well as physiography. Sentinel 2 satellite data (10 m.) and Aster DEM (30 m.) have been used to prepare the digital soil map. Soil samples were analyzed to determine the Macro (N, P, and K) Micro (Fe, Zn, Cu, Mn, S, and Br) Nutrients and Some Physico (Texture, Bulk density, depth) Chemical Properties (pH, EC, and OC).Results and conclusion:Six textural classes identified were sandy clay loam and sandy clay, clay, clay loam, loam, and sandy loam. The bulk density, and the depth varied from 1.08 to 1.8 Mg m-3, and 14 to 90 cm. respectively. The pH, EC, and OC are varied from 5 to 8.36, 0.1 to 1.2 ds/m, and 0.03 to 1.47 respectively. Nitrogen (N), Phosphorus (P), and Potassium(K) varied from 125 to 476 kg/ha., 4.44 to 77.78 kg/ha, and 79.6 to 504 kg/ha respectively. The digital soil database along with all its properties called a physiographic soil map which has been prepared with the help of the inverse distance weightage (IDW) interpolation method, which will help to select crops and get the best sustainable cultivation. Manuscript profile -
Open Access Article
2 - Deep learning approach to help Chenopodiaceae biodiversity protection to prevent soil erosion (case study: Yazd province, Iran)
Ahmad Heidary-Sharifabad Najma SoltaniBackground and objective:Chenopodiaceae species are important vegetation around the world, especially in the desert and semi-desert areas. Preserving the biodiversity of Chenopodiaceae species is crucial to preventing soil erosion. In addition, most of them are of ecolo MoreBackground and objective:Chenopodiaceae species are important vegetation around the world, especially in the desert and semi-desert areas. Preserving the biodiversity of Chenopodiaceae species is crucial to preventing soil erosion. In addition, most of them are of ecological and economic importance and also play an important role in biodiversity around the world. Conservation of this biodiversity is vital to the survival and sustainability of the ecosystem. To protect plant biodiversity, it is essential to know the plant species in their natural habitats. Therefore, automatic identification of plant species in their habitat helps to analyze the species and thus take care of their biodiversity. Computer vision approaches can be used to automatically identify and classify plant species. Modern approaches use deep learning in computer vision.Materials and methods:In this study, the ACHENY data set that consists of 27030 images of 30 species of Chenopodiaceae are used. Firstly, using the SuperPixel method, larger size images (448×448) than existing ACHENY dataset images size (224×224) are created. Secondly, based on the newly created dataset we introduce a proper deep learning model to identify Chenopodiaceae species.Results and conclusion:The results of the evaluation confirm the improvement of the classification accuracy of ACHENY species by the proposed model compared to the previously presented models. The results of the experiments indicate a superiority of about 3% accuracy of the proposed method and all evaluation parameters of the research have increased to a reasonable extent. Manuscript profile