Soil Texture Analysis Research Articles

Soil texture analysis using controlled image processing

Soil texture analysis is crucial for crop selection, fertilizer recommendation, and production. Traditional soil testing in the lab using chemicals is highly time-consuming, expensive, and risky harmful chemicals; proper equipment and trained professionals are required to get the readings and to conduct the analysis. These issues can be resolved using image processing. In this study, we proposed a Blackbox prototype machine to take images in a controlled environment under the fixed intensity of light, distance, and standard dry conditions to analyze soil texture. This innovative machine, with its efficient and precise image processing capabilities, has the potential to revolutionize soil texture analysis. Also, we marked the center points of each type of soil texture as defined in the USDA texture triangle. Hundreds of soil samples were prepared for each type according to the center point's sand, silt, and clay ratio. The image processing-based model is trained for texture analysis. This research aims to reduce the soil texture analysis time and provide a system that can do extensive analyses automatically and with accuracy. The proposed Blackbox prototype machine has proven effective in providing a controlled environment for taking images. Also, the proposed model detects soil texture with a maximum accuracy of 99.5 %. A proposed model trained on the soil samples of different texture classes available in the USDA texture triangle accurately performed texture analysis. The results benefit the recommendation of appropriate crops and fertilizers based on a given soil sample in a very short time and cost-effectively.

Soil Chemical and Physical Properties in Taungya and Non-Taungya Plots at Sao Hill Forest Plantation, Tanzania

This study compared soil physical and chemical properties in Tungya and Non-Taungya plots at Sao Hill Forest plantation, Tanzania. Soil samples were collected from 0-20cm depth in February and May Using a zigzag pattern to ensure representative coverage. Physical properties (bulk density, porosity, Soil moisture content, soil texture) and chemical properties (soil pH electrical conductivity, total nitrogen and available phosphorous) were analyzed using standard laboratory methods. Results showed significant differences between the two management practices. In February Non-Taungya plots had higher bulk density(1.33±0.151/cm3) compared to Taungya plots (1.16±0.107 g/cm3; p<0.001), while Taungya plots showed higher porosity (56.12%±4.06p<0.001). May sampling also revealed differences with non-Taungya plots maintaining higher bulk density (1.32±0.15 g/cm3) compared to Taungya plots (1.08±0.09 g/cm3); p<0.001). Soil texture analysis showed no significant differences in sand content between treatments. However, silt content was significantly higher in Taungya plots (13.27 ± 3.849%) compared to non-Taungya plots (10.69 ± 3.23%; p=0.007), while clay percentage did not differ significantly. Soil chemical properties, non-Taungya plots had higher pH (4.67±0.47) compared to Taungya plots (4.3±0.22; p<0.001) while electrical conductivity (0.04±0.04 dS/m) compared to Taungya plots (0.01±0.01 dS/m; p<0.001). Taungya plots showed higher total nitrogen (0.04±0.01%; p<0.0031) compared to non-Taungya plots (0.035±0.01%). In additional May available phosphorous in Taungya plots was (3.95±0.55 mg/kg; p<0.001) compared to non-Taungya plots (1.97±0.32 mg/kg). These results suggest that Taungya practices significantly influence soil properties, potentially impacting soil quality and forest productivity. The study provides valuable awareness for sustainable forest management and highlights the need for long-term monitoring of soil properties under different management systems in plantation forests

Establishing a standard protocol for soil texture analysis using the laser diffraction technique

Establishing a standard protocol for soil texture analysis using the laser diffraction technique

Variations in soil erodibility (K-factor) for the Chernozems depending on the method of texture determination

Soil erodibility (K-factor) is an important parameter in erosion modeling, is one of five factors of the Revised Universal Soil Loss Equation (RUSLE), and generally represents the soil's response to rainfall and run-off erosivity. The erodibility could be determined based on direct measurements of soil properties and mathematical calculations. In this study, the K-factor was calculated based on a formula from RUSLE, proposed by Renard et al. (1997). All input parameters: soil organic carbon (SOC), soil structure, and permeability classes were measured by one method, but particle size distribution – in two ways by sedimentation and laser diffraction methods to assess the impact the K-factor variability and the values of soil erosion rates. The 107 soil samples of Chernozems from Kursk Oblast (Russia) were studied. The texture for the most of samples was classified as silty loam in both analyses. However, the laser diffraction underestimates the clay content by an average of 13.2% compared to the pipette method. The average K-factor estimated based on laser diffraction data was 0.050, and 0.034 t ha h ha−1 MJ−1 mm−1 – sedimentation method. Thus, depending on the method of soil texture analysis, the RUSLE calculated soil loss could underestimated/overstated by 32% (or 4 t ha-1 yr-1 on average in the study site). Therefore, we propose a regression equation-based conversion method of laser diffraction data to sedimentation method data for Chernozems.The Laska-TM laser analyzer measured on ∼ 13% less clay fraction (more on ∼ 8% silt and ∼ 5% fine sand) compared with sedimentation method data.For erosional researchers/modelers it is suggested to state the method of soil texture analysis (based on sedimentation law or laser diffraction) was used for RUSLE K-factor calculations.To convert K-factor values (for Chernozems) calculated and based on data of the sedimentation method to laser sedimentation – it suggested utilize the coefficient 1.47 (0.68 – vice versa).

Evaluation and comparison of infiltration models for estimating infiltration capacity of different textures of irrigated soils

Accurate estimation of infiltration rates is crucial for effective irrigation system design and evaluation by optimizing irrigation scheduling, preventing soil erosion, and enhancing water use efficiency. This study evaluates and compares selected infiltration models for estimating water infiltration rates in the Shillanat-iv irrigation scheme in northern Ethiopia. Soil samples were collected to determine textural classes using hydrometer soil texture analysis and the United States Department of Agriculture (USDA) textural triangle. The soil textural map of the study was created using the inverse distance weight interpolation technique in ArcGIS version 10.4. Infiltration rates were measured using the double-ring infiltrometer for five soil textures: clay loam, loam, sandy clay loam, clay, and sandy loam. Six infiltration models (Kostiakov, Modified Kostiakov, Revised Modified Kostiakov, Philip, Horton, and Novel) were employed and evaluated using statistical parameters. Model calibration and validation were conducted using data from 38 points within the study area. The parameter values of the infiltration models were optimized using SPSS statistical software using least-squares errors. The results showed that, Revised Modified Kostiakov, Modified Kostiakov, and Novel infiltration models demonstrated superior capability in estimating infiltration rates for clay loam, loam, and sandy loam soil textures, respectively. Horton's model outperformed other models in estimating infiltration rates for both sandy clay loam and clay soil textures. The appropriately fitted infiltration models can be utilized in designing the irrigation system to estimate the infiltration rate of soil textures within the selected irrigation scheme and at similar sites with comparable soil textures.

Morphometric and soil texture analysis of soil material from wasp nests Sceliphron destillatorium (Illiger, 1807) (Apoidea: Sphecidae)

Background. The article presents the data obtained as a result of our research on the nests of an aboriginal wasp species on the territory of Ukraine – Sceliphron destillatorium (Illiger, 1807) that belongs to the family Sphecidae. We collected 54 wasp nests in different regions: Zakarpattia (n = 4), Ivano-Frankivsk (n = 1), Rivne (n = 12) and Lviv (n = 35) Regions. The data are presented as a result of measuring the main morphometric parame­ters of nests (length, width of cells and mass of nests) which are typical of the species under study. The paper also reports the results of the soil texture analysis of the soil material of twelve wasp nests from four different places of “mass nesting” and compares the results with the control samples of the surrounding soils in the corresponding areas. The study aimed at investigating the structure of the nests of S. destillatorium wasps, determining the soil texture of the soil material of nests from different regions of Ukraine and comparing them. Material and Methods. The nests of Sceliphron destillatorium wasps were the object of the study. The morphometric parameters of nests were measured with an automatic digital caliper 0–150 mm and Axis A500 technical chemical scales. The soil texture analysis was performed by the pipette method. The potentiometric method was used to determine the pH of the soil material. In addition, the determination of the CO2 carbonates was carried out according to the volumetric method. Results. We have analysed 54 nests of S. destillatorium. 46 nests were found in “mass nesting places” (in some places several samples were found): in Lviv (3 sites) and Rivne Regions (1 site – Rivne Nature Reserve). The content of granulometric elements was determined in twelve wasp nests from these places and twelve control soil samples were taken around the places of “mass nesting” (three samples of surrounding soil from each location). In order to compare the particle size distribution of elements in all analysed samples (24), we drew cumulative curves and determined coefficients of uniformity and curvature. Conclusions. Nest weighing revealed considerable variations in the mass of the nests – from 10.06 to 222.56 g – depending on the number of cells in a nest. The size of the nests also varied. The number of cells in the nests varied from 2 to 37 (mean = 13; n = 54 nests). As a result of the soil texture analysis of the nest soil material, we determined the percentage of granulometric elements used by the wasps for the construction of their nests. It was found that fine sand was one of the predominant fractions in all twelve nests. The similar content of all five fractions of soil material of wasp nests from different areas indicates that the soil texture of the nests which were built by S. destillatorium probably does not depend on their geographical location.

Texture prediction of natural soils in the Brazilian Amazon through proximal sensors

Proximal sensors provide fast, low-cost, environmentally friendly, and reliable analyses for the characterization of soils and other materials. Numerous studies have been conducted on soils in temperate regions, but there are knowledge gaps regarding the use of these devices in tropical soils, especially in the Amazon region. In this regard, this study utilized portable proximal sensors of X-ray fluorescence spectroscopy (pXRF) and diffuse reflectance spectroscopy in the visible to near-infrared region (Vis-NIR) for predicting the texture of natural soils in 61 municipalities in the state of Pará, Amazon region, Brazil. The objectives were: i) to investigate the accuracy of soil texture prediction based on data from sensors separately and sensor fusion (pXRF and Vis-NIR data) using two supervised algorithms (Random Forest, RF, and Support Vector Machine, SVM) and ii) to assess the effect of soil horizon (superficial and subsuperficial horizons, and their combination) in predicting the texture of tropical natural soils. In total, 233 soil samples were collected in the 0–20 cm and 80–100 cm depths, equivalent to superficial and subsuperficial horizons in areas with primary or secondary forest cover with at least 20 years of natural regeneration and approximately 20 ha of coverage area. The hydrometer method was used for soil texture analysis. In parallel, a portion of the soil samples was analyzed by pXRF and Vis-NIR, in triplicate, under laboratory conditions. The predictive models with RF were more robust compared to the models obtained with SVM, according to ratio performance interquartile distance (RPIQ), coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The R2 values obtained by pXRF, Vis-NIR, and sensor data combination were, respectively, 0.89, 0.87, and 0.93 for sand; 0.92, 0.90, and 0.93 for clay; and 0.91, 0.67, and 0.93 for silt. Overall, clay prediction models achieved higher R2 values compared to sand and silt models. Soil texture prediction using sensor fusion showed lower RMSE values and higher R2 and RPIQ values, respectively (sand: 7.79, 0.93, 4.69; clay: 5.58, 0.93, 3.86; and silt: 5.72, 0.92, 2.92) compared to the best-performing sensor individually (Vis-NIR). With regard to the optimal model utilizing individual sensor data, Vis-NIR models exhibited reduced error for clay and sand prediction. The effect of combining horizons to a single and bigger dataset was minimally important for the models. The results demonstrate confidence in the use of proximal sensors for soil texture assessment in natural Amazon soils, aiming to reduce costs and the time required for analyses.

Influence of small-scale spatial variability of soil properties on yield formation of winter wheat

BackgroundWith the increasing development of sophisticated precision farming techniques, high-resolution application maps are frequently discussed as a key factor in increasing yield potential. However, yield potential maps based on multiple soil properties measurements are rarely part of current farming practices. Furthermore, small-scale differences in soil properties have not been taken into account.MethodsTo investigate the impact of soil property changes at high resolution on yield, a field trial has been divided into a sampling grid of 42 plots. The soil properties in each plot were determined at three soil depths. Grain yield and yield formation of winter wheat were analyzed at two sites.ResultsMultiple regression analyses of soil properties with yield measures showed that the soil contents of organic carbon, silt, and clay in the top and subsoil explained 45–46% of the variability in grain yield. However, an increasing clay content in the topsoil correlated positively with grain yield and tiller density. In contrast, a higher clay content in the subsoil led to a decrease in grain yield. A cluster analysis of soil texture was deployed to evaluate whether the soil´s small-scale differences caused crucial differences in yield formation. Significant differences in soil organic carbon, yield, and yield formation were observed among clusters in each soil depth.ConclusionThese results show that small-scale lateral and vertical differences in soil properties can strongly impact crop yields and should be considered to improve site-specific cropping techniques further.

Assessment of soil properties and trace element accumulation in arid regions: A case study of Kalmykia's central dry steppe zone, Russia

Soil plays a pivotal role in ecosystem health and agricultural productivity. This study focuses on a critical region for soil research, Kalmykia's central dry steppe zone in southern Russia, characterized by arid conditions and unique challenges. Our investigation aimed to evaluate the current state of soil properties and assess trace element accumulation within this environment. The region's distinctive characteristics, including being home to Europe's first desert, present a complex scenario for soil conservation and management. A thorough analysis of key physicochemical properties, including organic matter content, soil texture, pH levels, and the concentrations of trace elements (V, Cr, Co, Ni, Cu, Zn, Sr, and Pb) using established methodologies, was conducted. Our findings revealed several crucial insights into the soil conditions of this arid region. Soil samples predominantly consisted of Haplic Kastanozems Sodic, characterized by low organic carbon content (0.3-1.9%). Soil texture analysis indicated a predominantly light and medium loamy granulometric composition with a prevalence of sandy fractions. Soil pH values ranged from neutral (pH = 7.6-7.9) to slightly alkaline (pH = 8.0-8.4). Furthermore, the study provided the first assessment of soil conditions in residential areas of the Caspian Lowland's arid region. Notably, trace element analysis showed elevated concentrations of several metals, with Sr having the highest levels. Co, Cr, and Zn concentrations did not significantly increase compared to the background values. The results of this soil fertility evaluation hold significance for soil restoration and conservation efforts in this unique and fragile ecosystem. In conclusion, this study underscores the urgent need for soil monitoring and management practices to address soil degradation and desertification driven by overgrazing and erosion. Understanding the physicochemical properties and trace element dynamics in arid regions is essential for developing strategies to restore and conserve these valuable soils.

Effect of Soil Texture on the Productivity of Two Shallot Varieties

The Lake Toba Catchment Area (LTCA) has long been known as a producer of shallots in North Sumatra. The Samosir variety is the mainstay local variety of the farmers. Due to the lack of quality seeds and disease attacks on the Samosir variety, farmers began to cultivate the Bima Brebes variety. This study aims to prove the difference in productivity of shallot varieties Samosir and Bima Brebes cultivated in the LTCA region by considering soil texture as a factor affecting productivity. This study was conducted in 6 shallot producing districts included in the LTCA. For analysis, 30 samples were selected based on the level of shallot productivity, with details of 10 low category production (< 4.5 tons/ha), 10 medium category production (4.5-7.5 tons/ha), and 10 high category production (> 7.5 tons/ha), along with the results of soil texture analysis. Correlation analysis was conducted to find the type of soil fraction that influenced productivity. ANCOVA to analyze the relationship between soil fraction and shallot productivity in each variety and analyze the difference in the productivity of Samosir and Bima Brebes shallots. The results showed that the sand fraction had an effect on shallot productivity in LTCA. The shallot cultivation land in LTCA has a sand fraction of 69-76% (sandy loam texture), where the greater the percentage of sand fraction, the higher the productivity of both shallot varieties. There was no difference in the productivity of Samosir and Bima Brebes shallots varieties. Bima Brebes variety can be a substitute seed for shallot cultivation in LTCA.

Soil Texture Prediction Using Machine Learning Approach for Sustainable Soil Health Management

Soil in the earth acts as a foothold for all crops. Soil texture is the most important soil health indicator being used for the selection of crops, mechanical manipulation, irrigation management, and fertilizer management. The texture of the soil influences the storage and flow of air and water within the soil, as well as root development, the accessibility of plant nutrients, and the activities of different microorganisms. These factors collectively impact the soil's fertility, quality, and soil health. A conventional method of soil texture analysis is cumbersome, time-consuming, and labor-intensive. Machine learning (ML) is a newly emerging technique being used to assess the soil's physical, chemical, and biological properties quickly in real-time. This is an eco-friendly approach since it does not involve any hazardous chemicals. Machine learning can learn complex features and predict nonlinear properties. Convolutional Neural Networks (CNN) employs convolutional layers to automatically learn features from the input data and is widely used in image classification, object detection, and image generation tasks in a short time. Soil texture images are given as input dataset after the completion of image subsetting, data preprocessing, and Image augmentation. This gives a CNN-based soil texture predictive model with a reliable accuracy of 87.50% at a lower cost.

Energy of dispersing of loamy soils to elementary particles using ultrasound

Elementary soil particles are the first soil-specific level in the soil structure hierarchy, which is also the object of the soil texture analysis. To disperse soil solids to elementary soil particles (ESP), it is necessary to break the strong bonds between particles by physical action. An effective way of physical dispersion is to treat soil suspensions with ultrasound. However, depending on the type of soil, the required energy level varies, as it is determined by the stability of the soil structure. In this work the experiment with increasing energy (from 65 to 1 101 J·mL-1) of ultrasonic dispersion at constant power equal to 32.4 W is described. Soil samples from the upper horizons of three types of loamy soils (Retisol, Phaeozem, Chernozem) were used in the experiment. For this purpose, a horn-type ultrasonic disruptor Digital Sonifier S-250D (Branson Ultrasonics, USA) with a stepped solid horn tip (13 mm) was used. It was found that the value of the total energy of dispersion Et required for complete destruction of soil aggregates to ESP depends on soil type and varies within 200–800 J·mL-1 for loamy soils with an organic matter content of 1.8–4.6 g. 100 g-1 soil. For sample preparation of soils for grain-size analysis with a subsequent determination of soil texture class according to Kachinsky classification, the value Et = 250 J·mL-1 is sufficient, as it allows obtaining the maximum amount of physical clay (<10 µm) at the minimum duration of sample preparation.

Soil texture analysis by laser diffraction and sedimentation and sieving–method and instrument comparison with a focus on Nordic and Baltic forest soils

PurposeLaser diffraction (LD) for determination of particle size distribution (PSD) of the fine earth fraction appeared in the 1990s, partly substituting the Sieving and Sedimentation Method (SSM). Whereas previous comparison between the two methods predominantly encompasses agricultural soils, less attention has been given to forest soils, including pre-treatment requirements related to their highly variable contents of carbon and Alox+ Feox. In this small collaborative learning study we compared (1) national SSM results with one type/protocol of LD analysis (Coulter), (2) LD measurements performed on three different LD instruments / laboratories, and (3) the replication error for LD Coulter analysis of predominantly sandy and loamy forest soils.MethodsWe used forest soil samples from Denmark, Norway and Lithuania and their respective national SSM protocols / results. LD analyses were performed on Malvern Mastersizer 2000, Sympatec HELOS version 1999, and Coulter LS230, located at University of Copenhagen, Aarhus University and Helsinki University, respectively. The protocols differed between laboratories, including the use of external ultrasonication prior to LD analysis.ResultsThe clay and silt fractions content (<20 μm) from the LD analysis were not comparable with SSM results, with differences ranging from −0.5 to 22.3 percentage points (pp) for clay. Preliminary results from loamy samples with spodic material suggested inconsistent effects of external ultrasonication to disperse aggregates. The comparison between the three LD instruments showed a range in the clay and silt fractions content of 1.9–5.3 and 6.2–8.1 pp, respectively. Differences may be related to the instruments, protocols, and content of a given particle size fraction. The replication error of the Coulter LD protocol was found to be <3 pp in sandy soils, but up to 10 pp in loamy soils.ConclusionDifferences in the clay fraction results partly affected the classification of soil types. The fast replication of the LD analysis enables more quality control of results. The pedological evaluation of non-silicate constituents and optional pre-treatment steps (e.g., soil organic matter or sesquioxides) remains the same for LD and SSM. For comparison of results, detailed descriptions of the analytical protocol including pre-treatments are needed irrespective of instrument and theoretical approach.

Impact of Biotic Stress on Physiochemical Properties of Soil at Doodhpathri Kashmir

Anthropogenically disturbed soils have unique properties. In most of the ecosystems, especially under disturbed soil conditions, the soil properties are controlled by the accumulated materials. However, the equilibrium between the already present soil mass and the accumulated soil mass is very fragile and is affected by many factors. This study was carried out in the forest soil of Doodhpathri which is a famous tourist place in Kashmir in the spring and summer seasons at three sites (forest, meadow, and deforested sites) in 2019. Soil samples were collected at 15-30 cm depth. We determined soil texture, pH, electrical conductivity (EC), moisture, organic carbon (OC), available macronutrients (Nitrogen, Potassium, Phosphorus, Calcium, and Magnesium), and heavy metals (Copper; Cu, Nickel; Ni, Cadmium; Cd, Manganese; Mn, Lead; Pb, and Zinc; Zn). Soil texture analysis revealed the soils at all the study sites with a major proportion being comprised by the silt fraction and having a silty clay loam character. pH and electrical conductivity were found to be high at the deforested site in the summer. Organic carbon and moisture content were found higher at the forest site in summer and spring respectively. Nitrogen, potassium, and total bacterial count follow the same trend as higher concentration was found at the forest site in the summer while Phosphorus, magnesium, and calcium follow the same trend as their high concentration was found at the meadow site in the summer. Significantly higher values of heavy metals (Cu, Ni, Cd, Mn, Pb, and Zn) were found at the meadow site in the summer. The results could help to formulate conservation strategies for the soil at Doodhpathri that is affected by anthropogenic activities.

ANALISIS KUALITAS FISIK TANAH PADA LAHAN TANAMAN JERUK DI DESA SIDO MAKMUR KECAMATAN TIWORO KEPULAUAN KABUPATEN MUNA BARAT

Soil is a heterogeneous natural object consisting of solid, liquid and gas components that have dynamic properties and behavior. Soil and water are natural resources that support the life of various living things on earth, as a planting medium for plants, and a place for living things to stand on it, including humans. This study aims to determine the quality of soil physics on citrus plantations at different ages in Sido Makmur Village, Tiworo Islands District. This research was carried out in the Tiworo Islands District, Sido Makmur Village, West Muna Regency and the Testing Laboratory of the Department of Soil Science, Faculty of Agriculture, Halu Oleo University, Kendari, in February - March 2022. The method used in this study was a survey method with a descriptive-quantitative approach (measured). ) namely by conducting direct observations in the field and analysis in the laboratory. From the results of soil texture analysis showed that the land on citrus plants aged 2 years and 5 years old were the most dominant, namely clay texture, and sandy loam texture, low soil water content, medium soil volume weight, good porosity. From the results of the study, it can be concluded that oranges with different ages in Sido prosperous Village have soil physical properties in the form of clay and sandy loam textures, low soil water content, medium soil volume weight, and poor soil pore space

Spatial Analysis of Soil Texture using GIS based Geostatistics Models and Influence of Soil Texture on Soil Hydraulic Conductivity in Melur Block of Madurai District, Tamil Nadu

Background: Soil texture is one of the important property constitute ultimate size particles. Proportions of these ultimate soil particles depend on the pedogenesis and mineralogical composition of parent material and also decide all other soil properties. Due to the imprudent crop selection and imperfect agricultural methods, the very fertile soil is becoming denser and low productive. Geostatistics is a set of Models and tools developed for statistical analysis of continuous data. A critical aspect of agriculture ecosystem and environment modeling is the accurate prediction for the spatial variability of soil properties. Methods: In this study, Geostatistical modeling based Kriging techniques has been used to identify the textural variation of soil in the present scenario of Melur Block of Madurai district. Totally, 150 soil samples were collected across Melur block of Madurai district based on grid soil survey and geo coordinates. Result: Geostatistical soil textural maps have been generated based on Kriging tools and these would be utilized for Land Use Pattern, precision agriculture, conservation practices, as well as land management in the research region. The characterization of the spatial variability and influence of soil texture on hydraulic conductivity is essential to accomplish a better understanding of intricate relations between soil properties and environmental factors.

Ecological Role of <i>Onthophagus taurus</i> (Schreber) in Soil Nutrient Mobilization

Dung beetles play a major role in the pasture ecosystem. The manure recycling activity of dung beetles is linked to their tunneling behavior. The present study was designed to analyze the tunnel pattern and nutrient mobilization by dung beetles, Onthophagus taurus (Schereber, 1759) in different soil types. A simple type of tunnel pattern was observed in all the four types of soil after 30th day of their introduction (10 pairs of male and female) into the experimental setup. However, the maximum number of tunnels was observed in the sandy and sandy clay loam (no. of openings - 15), followed by loamy soil (no. of openings -13). The physical (texture, water holding capacity, porosity, moisture content) and chemical parameters (pH and nutrients) of all the four types of soils were evaluated. Soil texture analysis revealed the texture to be of sandy (yellow soil), sandy clay loam (red and black soil), and loamy sand (brown soil) types. Water holding capacity and the soil porosity were recorded highest in the sandy soil, whereas moisture content was found maximum in the sandy clay loam. Soil nutrient analysis illustrated a significant increase in the amount of nitrogen (N), phosphorus (P), calcium (Ca), sulfur (S), sodium (Na), potassium (K), organic carbon and organic matter. Thus, the present study confirms that tunneling activity of O. taurus enhances the soil nutrients by carrying out dung decomposition.

An optimized deep network based soil texture prediction and vegetation analysis using satellite image database

SummaryThe usage of satellite images has grown rapidly in digital applications. Predicting features in the satellite images using conventional techniques is not an easy task as the images captured from satellites are more complex with highly noisy features. Hence, traditional imaging algorithms cannot analyze the image features and specify the soil textures. In this work, the Tarakeswar satellite images were taken to estimate the soil textures and crops yielding. Hence, to find the soil's chemical properties and suitable crops, the novel bat‐based U‐Net feature prediction system (BUFPS) was developed with the required features. The imported satellite images were initially filtered and entered into the classification phase to forecast the present chemical features and suitable crops in specific soils. After detecting the chemical features, soil textures like sandy, silt, and clay were categorized; the crops like jute, rice, lentil, and potato were considered. Finally, the planned model is executed in the MATLAB environment and has gained outstanding results by achieving the lowest error rate of 8% and a high prediction score of 92%. The proposed model has achieved exact soil texture analysis for complex satellite images. The gained texture analysis outcome is quite better than other existing models.

In-situ soil texture classification and physical clay content measurement based on multi-source information fusion

Soil texture is one of the most important soil characteristics that affect soil properties. Rapid acquisition of soil texture information is of great significance for accurate farmland management. Traditional soil texture analysis methods are relatively complicated and cannot meet the requirements of temporal and spatial resolution. This research introduced a self-developed vehicle-mounted in-situ soil texture detection system, which can predict the type of soil texture and the particle composition of the texture, and obtain real-time data during the measurement process without preprocessing the soil samples. The detection system is mainly composed of a conductivity measuring device, a camera, an auxiliary mechanical structure, and a control system. The soil electrical conductivity (ECa) and the texture features extracted from the surface image were input into the embedded model to realize real-time texture analysis. In order to find the best model suitable for the detection system, measurements were carried out in three test fields in Northeast and North China to compare the performance of different models applied to the detection system. The results showed that for soil texture classification, ExtraTrees performed best, with Precision, Recall, and F1 all being 0.82. For particle content of soil texture prediction, the R2 of ExtraTrees was 0.77, and RMSE and MAPE were 74.72 and 39.58. It was observed that ECa, Moment of inertia, and Entropy had larger weights in the drawn model influence weight map, and they are the main contributors to predicting soil texture. These results showed the potential of the vehicle-mounted in-situ soil texture detection system, which can provide a basis for fast, cost-effective, and efficient soil texture analysis. Keywords: soil texture, soil sensor, electrical conductivity, soil surface image DOI: 10.25165/j.ijabe.20231601.6918 Citation: Meng C, Yang W, Ren X J, Wang D, Li M Z. In-situ soil texture classification and physical clay content measurement based on multi-source information fusion. Int J Agric & Biol Eng, 2023; 16(1): 203–211.

Smartphone assisting convolutional neural networks for soil texture classification in dry and wet humid conditions in West Guwahati, Assam

Soil texture using a hydrometer or pipette method requires expertise, although these are accurate. A soil expert may help the farmer to detect the soil texture by analyzing the visual texture of the soil, which is not always accurate. This paper presents the smartphone image-based sand and clay soil classification in wet and dry humid conditions using Self Convolution Neural Network (SCNN) and finetuned MobileNet.A soil dataset of 576 soil images was prepared using a low-cost smartphone under natural light conditions. Different augmentation techniques such as shift, range, rotation, and zoom were applied to the soil dataset to increase the number of images in the soil dataset. The best performance of the MobileNet was reported at epoch 15 with a testing and training loss of 0.0091 and 0.0194, respectively. Though the SCNN model performed best at epoch 10 with a testing accuracy of 99.85%, the MobileNet reported less computation time (167.8s) than the SCNN (273.2s). The precision and recall of the models were 99.62 (MobileNet) and 99.84 (SCNN). The accuracy of the SCNN reported itself as the best model, whereas the computing time of the MobileNet reported itself as the best model in different humid conditions. The model can be used to replicate the traditional soil texture analysis method and the farmers can use it for better productivity.