scholarly journals Estimation of Soil Nutrient Content Using Hyperspectral Data

Agriculture ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1129
Author(s):  
Yiping Peng ◽  
Lu Wang ◽  
Li Zhao ◽  
Zhenhua Liu ◽  
Chenjie Lin ◽  
...  
Keyword(s):  
Least Squares ◽  
Soil Nutrients ◽  
Cross Validation ◽  
Regional Scale ◽  
Soil Nutrient ◽  
Nutrient Content ◽  
New Method ◽  
Accurate Estimation ◽  
Support Vector ◽  
Soil Nutrient Content

Soil nutrients play a vital role in plant growth and thus the rapid acquisition of soil nutrient content is of great significance for agricultural sustainable development. Hyperspectral remote-sensing techniques allow for the quick monitoring of soil nutrients. However, at present, obtaining accurate estimates proves to be difficult due to the weak spectral features of soil nutrients and the low accuracy of soil nutrient estimation models. This study proposed a new method to improve soil nutrient estimation. Firstly, for obtaining characteristic variables, we employed partial least squares regression (PLSR) fit degree to select an optimal screening algorithm from three algorithms (Pearson correlation coefficient, PCC; least absolute shrinkage and selection operator, LASSO; and gradient boosting decision tree, GBDT). Secondly, linear (multi-linear regression, MLR; ridge regression, RR) and nonlinear (support vector machine, SVM; and back propagation neural network with genetic algorithm optimization, GABP) algorithms with 10-fold cross-validation were implemented to determine the most accurate model for estimating soil total nitrogen (TN), total phosphorus (TP), and total potassium (TK) contents. Finally, the new method was used to map the soil TK content at a regional scale using the soil component spectral variables retrieved by the fully constrained least squares (FCLS) method based on an image from the HuanJing-1A Hyperspectral Imager (HJ-1A HSI) of the Conghua District of Guangzhou, China. The results identified the GBDT-GABP was observed as the most accurate estimation method of soil TN ( of 0.69, the root mean square error of cross-validation (RMSECV) of 0.35 g kg−1 and ratio of performance to interquartile range (RPIQ) of 2.03) and TP ( of 0.73, RMSECV of 0.30 g kg−1 and RPIQ = 2.10), and the LASSO-GABP proved to be optimal for soil TK estimations ( of 0.82, RMSECV of 3.39 g kg−1 and RPIQ = 3.57). Additionally, the highly accurate LASSO-GABP-estimated soil TK (R2 = 0.79) reveals the feasibility of the LASSO-GABP method to retrieve soil TK content at the regional scale.

scholarly journals Evaluating Calibration and Spectral Variable Selection Methods for Predicting Three Soil Nutrients Using Vis-NIR Spectroscopy

2021 ◽  
Vol 13 (19) ◽  
pp. 4000
Author(s):  
Peng Guo ◽  
Ting Li ◽  
Han Gao ◽  
Xiuwan Chen ◽  
Yifeng Cui ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Variable Selection ◽  
Soil Nutrients ◽  
Soil Nutrient ◽  
Nutrient Content ◽  
Support Vector ◽  
Selection Methods ◽  
Calibration Methods ◽  
Soil Nutrient Content

Soil nutrients, including soil available potassium (SAK), soil available phosphorous (SAP), and soil organic matter (SOM), play an important role in farmland soil productivity, food security, and agricultural management. Spectroscopic analysis has proven to be a rapid, nondestructive, and effective technique for predicting soil properties in general and potassium, phosphorous, and organic matter in particular. However, the successful estimation of soil nutrient content by visible and near-infrared (Vis-NIR) reflectance spectroscopy depends on proper calibration methods (including preprocessing transformation methods and multivariate methods for regression analysis) and the selection of appropriate variable selection techniques. In this study, raw spectrum and 13 preprocessing transformations combined with 2 variable selection methods (competitive adaptive reweighted sampling (CARS) and the successive projections algorithm (SPA)) and 2 regression algorithms (support vector machine (SVM) and partial least squares regression (PLSR)), for a total of 56 calibration methods, were investigated for modeling and predicting the above three soil nutrients using hyperspectral Vis-NIR data (400–2450 nm). The results show that first-order derivatives based on logarithmic and inverse transformations (FD-LGRs) can provide better predictions of soil available potassium and phosphorous, and the best form of soil organic matter transformation is SG+MSC. CARS was superior to the SPA in selecting effective variables, and the PLSR model outperformed the SVM models. The best estimation accuracies (R2, RMSE) for soil available potassium, phosphorous, and organic matter were 0.7532, 32.3090 mg/kg; 0.7440, 6.6910 mg/kg; and 0.9009, 3.2103 g/kg, respectively, and their corresponding calibration methods were (FD-LGR)/SPA/PLSR, (FD-LGR)/SPA/PLSR, and SG+MSC/CARS/SVM, respectively. Overall, for the prediction of the soil nutrient content, organic matter was superior to available phosphorous, followed by available potassium. It was concluded that the application of hyperspectral images (Vis-NIR data) was an efficient method for mapping and monitoring soil nutrients at the regional scale, thus contributing to the development of precision agriculture.

scholarly journals Effects of Two Trichoderma Strains on Plant Growth, Rhizosphere Soil Nutrients, and Fungal Community of Pinus sylvestris var. mongolica Annual Seedlings

Forests ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 758 ◽  
Author(s):  
Saiyaremu Halifu ◽  
Xun Deng ◽  
Xiaoshuang Song ◽  
Ruiqing Song
Keyword(s):  
Enzyme Activity ◽  
Community Structure ◽  
Plant Growth ◽  
Soil Nutrients ◽  
Fungal Community ◽  
Rhizosphere Soil ◽  
Soil Nutrient ◽  
Nutrient Content ◽  
Root Structure ◽  
Soil Nutrient Content

Trichoderma spp. are proposed as major plant growth-promoting fungi that widely exist in the natural environment. These strains have the abilities of rapid growth and reproduction and efficient transformation of soil nutrients. Moreover, they can change the plant rhizosphere soil environment and promote plant growth. Pinus sylvestris var. mongolica has the characteristics of strong drought resistance and fast growth and plays an important role in ecological construction and environmental restoration. The effects on the growth of annual seedlings, root structure, rhizosphere soil nutrients, enzyme activity, and fungal community structure of P. sylvestris var. mongolica were studied after inoculation with Trichoderma harzianum E15 and Trichoderma virens ZT05, separately. The results showed that after inoculation with T. harzianum E15 and T. virens ZT05, seedling biomass, root structure index, soil nutrients, and soil enzyme activity were significantly increased compared with the control (p < 0.05). There were significant differences in the effects of T. harzianum E15 and T. virens ZT05 inoculation on the growth and rhizosphere soil nutrient of P. sylvestris var. mongolica (p < 0.05). For the E15 treatment, the seedling height, ground diameter, and total biomass of seedlings were higher than that those of the ZT05 treatment, and the rhizosphere soil nutrient content and enzyme activity of the ZT05 treatment were higher than that of the E15 treatment. The results of alpha and beta diversity analyses showed that the fungi community structure of rhizosphere soil was significantly different (p < 0.05) among the three treatments (inoculated with T. harzianum E15, T. virens ZT05, and not inoculated with Trichoderma). Overall, Trichoderma inoculation was correlated with the change of rhizosphere soil nutrient content.

scholarly journals A Smart Agriculture System Based On Intel Galileo For Detection Of Cultivable Crops And Implemented On Soil Nutrients

2019 ◽  
Vol 8 (9) ◽  
pp. 98-102
Keyword(s):  
Soil Nutrients ◽  
Soil Nutrient ◽  
Nutrient Content ◽  
Ph Sensors ◽  
Food Grains ◽  
The World ◽  
Current Scenario ◽  
Soil Nutrient Content ◽  
Smart Agriculture ◽  
Intel Galileo

The world is in need of food, water, Shelter and without any of these there is no possibility to live. Agriculture is backbone of our nation which indeed supplies food for the entire people but the producers of food are facing many problems like moisture content, fertility of soil which affects the farming. Because of this reason cultivation and production of food grains are decreased. Current scenario is that the farmers need to go research center to test the fertility content of soil and to predict kind of plants can be cultivable in that land. In this paper perfect cultivable plants can be detected properly without approaching soil research centers which consumes more money as well time. By the application of pH sensors the fertility of soil can be measured. By measuring the pH soil nutrient content can be measured and so the suitable cultivable crop for different soil varieties can be predicted. In this view to give a healthful support to farmers, soil nutrients are analyzed and report the requirement using advanced sensors.

scholarly journals Germplasm Screening of Green Manure Rapeseed through the Effects of Short-Term Decomposition on Soil Nutrients and Microorganisms

Agriculture ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1219
Author(s):  
Xiaodan Wang ◽  
Hua Ma ◽  
Chunyun Guan ◽  
Mei Guan
Keyword(s):  
Soil Nutrients ◽  
Green Manure ◽  
Soil Microorganisms ◽  
Soil Nutrient ◽  
Principal Component ◽  
Nutrient Content ◽  
Germplasm Screening ◽  
Significant Difference ◽  
Soil Nutrient Content ◽  
Phosphorus And Potassium

The rapidly emerging fertilizer rapeseed used as green manure has wide applications for use. However, there have been few studies on its decomposition and effects on soil nutrients and microorganisms after its decay. In this study, 12 rapeseed lines to be screened were decomposed through a randomized block field design with two green-manure-specific varieties as the controls. The contents of nitrogen, phosphorus, and potassium from the plants, soil nutrients, and microbial changes after degradation were measured. There were substantial differences in the rates of decomposition and cumulative release of nutrients among the different lines after 30 days of rolling. The contents of phosphorus and potassium in the soil were 1.23–2.03 and 3.93–6.32 times those before decomposition, respectively. In addition, there was a significant difference in the relative content of soil microorganisms at the phylum level after the decomposition of different species of rapeseeds. Most of the top 20 bacterial groups significantly correlated with the characteristics of plant decomposition and soil nutrient content, including Proteobacteria, Actinomycetes, Armatimonadetes, Rokubacteria, and Planctomycetes. A principal component analysis showed that the soil microorganisms and nutrients are the leading factors that enable the evaluation of the decomposing characteristics of green manure rapeseed. Numbers 5 (purple leaf mustard) and 8 (Xiafang self-seeding) were more effective than two controls, which can be used as excellent types of germplasm to promote the breeding of green manure rapeseed.

scholarly journals Effects of Mineral-Solubilizing Microorganisms on Root Growth, Soil Nutrient Content, and Enzyme Activities in the Rhizosphere Soil of Robinia pseudoacacia

Forests ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 60
Author(s):  
Chong Li ◽  
Zhaohui Jia ◽  
Lu Zhai ◽  
Bo Zhang ◽  
Xiaonan Peng ◽  
...  
Keyword(s):  
Root Growth ◽  
Soil Nutrients ◽  
Enzyme Activities ◽  
Anthropogenic Activities ◽  
Robinia Pseudoacacia ◽  
Soil Nutrient ◽  
Nutrient Content ◽  
Mining Sites ◽  
Microbial Resources ◽  
Soil Nutrient Content

Background: Abandoned mining sites are becoming increasingly common due to anthropogenic activities. Consequently, external-soil spray seeding technology has attracted increasing attention as a strategy to remediate them. However, significant challenges remain that greatly inhibit the efficacy of such technologies, such as insufficient nutrients available for plants. Methods: For this study, we designed an experiment, which involved the addition of mineral-solubilizing microorganisms and R. pseudoacacia seedlings to the external-soil spray seeding (ESSS) substrate, and measured the soil nutrients, enzyme activities, and root growth of R. pseudoacacia. Results: First, the combination of certain mineral-solubilizing microorganisms with ESSS advanced its efficiency by increasing the availability of soil nutrients and soil enzymatic activities in association with R. pseudoacacia. Furthermore, the improvement of root growth of R. pseudoacacia was intimately related to soil nutrients, particularly for soil total nitrogen (TN) and total sulfur (TS). In general, the effects of the J2 (combined Bacillus thuringiensis and Gongronella butleri) treatment for soil nutrients, enzyme activities, and plant growth were the strongest. Conclusion: In summary, the results of our experiment revealed that these mineral-solubilizing microorganisms conveyed a promotional effect on R. pseudoacacia seedlings by increasing the soil nutrient content. These results provide basic data and microbial resources for the development and applications of mineral-solubilizing microorganisms for abandoned mine remediation.

scholarly journals Soil Nutrient Content Influences the Abundance of Soil Microbes but Not Plant Biomass at the Small-Scale

PLoS ONE ◽  
2014 ◽  
Vol 9 (3) ◽  
pp. e91998 ◽  
Author(s):  
Kadri Koorem ◽  
Antonio Gazol ◽  
Maarja Öpik ◽  
Mari Moora ◽  
Ülle Saks ◽  
...  
Keyword(s):  
Soil Microbes ◽  
Plant Biomass ◽  
Soil Nutrient ◽  
Nutrient Content ◽  
Small Scale ◽  
Soil Nutrient Content

AGRO SENSORS

2015 ◽  
Vol 3 (4) ◽  
pp. 325-327
Author(s):  
M.R Suja Barathi
Keyword(s):  
Soil Nutrient ◽  
Nutrient Content ◽  
Feedback Mechanism ◽  
Reference Voltage ◽  
Common Input ◽  
Current Output ◽  
Soil Nutrient Content ◽  
The Common ◽  
Effect Transistor ◽  
Resistance Value

Soil nutrient is detected by the use of ISFETs and the corresponding current is obtained from it according to the variation in concentration of the nutrient in soil and their pH. The use of metal oxide coated semiconductors for gas detection is high nowadays. The Ion sensitive field effect transistor has the electrode. This on reaction with the analyst changes its resistance value and corresponding signals are generated and the transducer unit in the sensor gives the varying current output. This could be then processed that is converted to voltage, amplified and then given to a window detector where in the voltage obtained from the sensor could be tested for it be in between two reference voltage levels and these reference voltage levels represent the level of nutrients in the soil either it is deficient, in surplus or in sufficient amounts. This could be used to make the LEDs glow. It proves to be an easy way of observing the soil nutrient content even by an illiterate. The checking of soil nutrient content could be automated and made periodic by the use of microcontroller, the feedback mechanism involving the soil’s nutrient content value as the common input and the ideal or required value as the reference input.

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