Soil is the major source of infinite lives on Earth and the quality of soil plays significant role on Agriculture practices all around.Hence,the evaluation of soil quality is very important for determining the amount ...Soil is the major source of infinite lives on Earth and the quality of soil plays significant role on Agriculture practices all around.Hence,the evaluation of soil quality is very important for determining the amount of nutrients that the soil require for proper yield.In present decade,the application of deep learning models in many fields of research has created greater impact.The increasing soil data availability of soil data there is a greater demand for the remotely avail open source model,leads to the incorporation of deep learning method to predict the soil quality.With that concern,this paper proposes a novel model called Improved Soil Quality Prediction Model using Deep Learning(ISQP-DL).The work considers the chemical,physical and biological factors of soil in particular area to estimate the soil quality.Firstly,pH rating of soil samples has been collected from the soil testing laboratory from which the acidic range has been categorized through soil test and the same data has been taken as input to the Deep Neural Network Regression(DNNR)model.Secondly,soil nutrient data has been given as second input to the DNNR model.By utilizing this data set,the DNNR method is used to evaluate the fertility rate by which the soil quality has been estimated.For training and testing,the model uses Deep Neural Network Regression(DNNR),by utilizing the dataset.The results show that the proposed model is effective for SQP(Soil Quality Prediction Model)with efficient good fitting and generality is enhanced with input features with higher rate of classification accuracy.The results show that the proposed model achieves 96.7%of accuracy rate compared with existing models.展开更多
We investigated the soil microbiologic characteristics, and the yield and sustainable production of winter wheat, by conducting a long-term fertilization experiment. A single application of N, P and K （NPK） fertiliz...We investigated the soil microbiologic characteristics, and the yield and sustainable production of winter wheat, by conducting a long-term fertilization experiment. A single application of N, P and K （NPK） fertilizer was taken as the control （CK） and three organic fertilization treatments were used： NPK fertilizer＋pig manure （T1）, NPK fertilizer＋straw return （T2）, NPK fertilizer＋pig manure＋straw return （T3）. The results showed that all three organic fertilization treatments （T1, T2 and T3） significantly increased both soil total N （STN） and soil organic carbon （SOC） from 2008 onwards. In 2016, the SOC content and soil C/N ratios for T1, T2 and T3 were significantly higher than those for CK. The three organic fertilization treatments increased soil microbial activity. In 2016, the activity of urease （sucrase） and the soil respiration rate （SRS） for T1, T2 and T3 were significantly higher than those under CK. The organic fertilization treatments also increased the content of soil microbial biomass carbon （SMBC） and microbial biomass nitrogen （SMBN）, the SMBC/SMBN ratio and the microbial quotient （qMB）. The yield for T1, T2 and T3 was significantly higher than that of CK, respectively. Over the nine years of the investigation, the average yield increased by 9.9, 13.2 and 17.4% for T1, T2 and T3, respectively, compared to the initial yield for each treatment, whereas the average yield of CK over the same period was reduced by 6.5%. T1, T2, and T3 lowered the coefficient of variation （CV） of wheat yield and increased the sustainable yield index （SYI）. Wheat grain yield was significantly positively correlated with each of the soil microbial properties （P〈0.01）. These results showed that the long-term application of combined organic and chemical fertilizers can stabilize crop yield and make it more sustainable by improving the properties of the soil.展开更多
Several remotely sensed sea surface salinity（SSS） retrievals with various resolutions from the soil moisture and ocean salinity（SMOS） and Aquarius/SAC-D missions are applied as inputs for retrieving salinity profi...Several remotely sensed sea surface salinity（SSS） retrievals with various resolutions from the soil moisture and ocean salinity（SMOS） and Aquarius/SAC-D missions are applied as inputs for retrieving salinity profiles（S） using multilinear regressions. The performance is evaluated using a total root mean square（RMS） error, different error sources, and the feature resolutions of the retrieved S fields. In the mixed layer of the salinity, the SSS-S regression coefficients are uniformly large. The SSS inputs yield smaller RMS errors in the retrieved S with respect to Argo profiles as their spatial or temporal resolution decreases. The projected SSS errors are dominant, and the retrieved S values are more accurate than those of climatology in the tropics except for the tropical Atlantic, where the regression errors are abnormally large. Below that level, because of the influence of a sea level anomaly, the areas of high-accuracy S values shift to higher latitudes except in the high-latitude southern oceans, where the projected SSS errors are abnormally large. A spectral analysis suggests that the CATDS-0.25° results are much noisier and that the BEC-L4-0.25° results are much smoother than those of the other retrievals. Aquarius-CAP-1° generates the smallest RMS errors, and Aquarius-V2-1° performs well in depicting large-scale phenomena. BEC-L3-0.25°,which has small RMS errors and remarkable mesoscale energy, is the best fit for portraying mesoscale features in the SSS and retrieved S fields. The current priority for retrieving S is to improve the reliability of satellite SSS especially at middle and high latitudes, by developing advanced algorithms, combining both sensors, or weighing between accuracy and resolutions.展开更多
Introduction:An accurate and reliable detection of soil physicochemical attributes(SPAs)is a difficult and complicated issue in soil science.The SPA may be varied spatially and temporally with the complexity of nature...Introduction:An accurate and reliable detection of soil physicochemical attributes(SPAs)is a difficult and complicated issue in soil science.The SPA may be varied spatially and temporally with the complexity of nature.In the past,SPA detection has been obtained through routine soil chemical and physical laboratory analysis.However,these laboratory methods do not fulfill the rapid requirements.Accordingly,diffuse reflectance spectroscopy(DRS)can be used to nondestructively detect and characterize soil attributes with superior solution.In the present article,we report a study done through spectral curves in the visible(350–700 nm)and near-infrared(700–2500 nm)(VNIR)region of 74 soil specimens which were agglomerated by farming sectors of Phulambri Tehsil of the Aurangabad region of Maharashtra,India.The quantitative analysis of VNIR spectrum was done.Results:The spectra of agglomerated farming soils were acquired by the Analytical Spectral Device(ASD)Field spec 4 spectroradiometer.The soil spectra of the VNIR region were preprocessed to get pure spectra which were the input for regression modeling.The partial least squares regression(PLSR)model was computed to construct the calibration models,which were individually validated for the prediction of SPA from the soil spectrum.The computed model was based on a correlation study between reflected spectra and detected SPA.The detected SPAs were soil organic carbon(SOC),nitrogen(N),soil organic matter(SOM),pH values,electrical conductivity(EC),phosphorus(P),potassium(K),iron(Fe),sand,silt,and clay.The accuracy of the PLSR model-validated determinant(R^(2))values were SOC 0.89,N 0.68,SOM 0.93,pH values 0.82,EC 0.89,P 0.98,K 0.82,Fe 0.94,sand 0.98,silt 0.90,and clay 0.69 with root mean square error of prediction(RMSEP)3.51,4.34,2.66,2.12,4.11,1.41,4.22,1.56,1.89,1.97,and 9.91,respectively.According to the experimental results,the VNIR-DRS was better for detection of SPA and produced more accurate predictions for SPA.Conclusions:In conclusion,the methods examined here offered rapid and novel detection of SPA from reflectance spectroscopy.The outcome of the present research will be apt for precision farming and decision-making.展开更多
文摘Soil is the major source of infinite lives on Earth and the quality of soil plays significant role on Agriculture practices all around.Hence,the evaluation of soil quality is very important for determining the amount of nutrients that the soil require for proper yield.In present decade,the application of deep learning models in many fields of research has created greater impact.The increasing soil data availability of soil data there is a greater demand for the remotely avail open source model,leads to the incorporation of deep learning method to predict the soil quality.With that concern,this paper proposes a novel model called Improved Soil Quality Prediction Model using Deep Learning(ISQP-DL).The work considers the chemical,physical and biological factors of soil in particular area to estimate the soil quality.Firstly,pH rating of soil samples has been collected from the soil testing laboratory from which the acidic range has been categorized through soil test and the same data has been taken as input to the Deep Neural Network Regression(DNNR)model.Secondly,soil nutrient data has been given as second input to the DNNR model.By utilizing this data set,the DNNR method is used to evaluate the fertility rate by which the soil quality has been estimated.For training and testing,the model uses Deep Neural Network Regression(DNNR),by utilizing the dataset.The results show that the proposed model is effective for SQP(Soil Quality Prediction Model)with efficient good fitting and generality is enhanced with input features with higher rate of classification accuracy.The results show that the proposed model achieves 96.7%of accuracy rate compared with existing models.
基金financial support from the National Key Research and Development Program of China (2017YFD0301106,2016YFD0300203-3)the Science and Technology Innovation Team Support Plan of Universities in Hennan Province,China (18IRTSTHN008)
文摘We investigated the soil microbiologic characteristics, and the yield and sustainable production of winter wheat, by conducting a long-term fertilization experiment. A single application of N, P and K （NPK） fertilizer was taken as the control （CK） and three organic fertilization treatments were used： NPK fertilizer＋pig manure （T1）, NPK fertilizer＋straw return （T2）, NPK fertilizer＋pig manure＋straw return （T3）. The results showed that all three organic fertilization treatments （T1, T2 and T3） significantly increased both soil total N （STN） and soil organic carbon （SOC） from 2008 onwards. In 2016, the SOC content and soil C/N ratios for T1, T2 and T3 were significantly higher than those for CK. The three organic fertilization treatments increased soil microbial activity. In 2016, the activity of urease （sucrase） and the soil respiration rate （SRS） for T1, T2 and T3 were significantly higher than those under CK. The organic fertilization treatments also increased the content of soil microbial biomass carbon （SMBC） and microbial biomass nitrogen （SMBN）, the SMBC/SMBN ratio and the microbial quotient （qMB）. The yield for T1, T2 and T3 was significantly higher than that of CK, respectively. Over the nine years of the investigation, the average yield increased by 9.9, 13.2 and 17.4% for T1, T2 and T3, respectively, compared to the initial yield for each treatment, whereas the average yield of CK over the same period was reduced by 6.5%. T1, T2, and T3 lowered the coefficient of variation （CV） of wheat yield and increased the sustainable yield index （SYI）. Wheat grain yield was significantly positively correlated with each of the soil microbial properties （P〈0.01）. These results showed that the long-term application of combined organic and chemical fertilizers can stabilize crop yield and make it more sustainable by improving the properties of the soil.
基金The National Natural Science Foundation of China under contract No.41276088
文摘Several remotely sensed sea surface salinity（SSS） retrievals with various resolutions from the soil moisture and ocean salinity（SMOS） and Aquarius/SAC-D missions are applied as inputs for retrieving salinity profiles（S） using multilinear regressions. The performance is evaluated using a total root mean square（RMS） error, different error sources, and the feature resolutions of the retrieved S fields. In the mixed layer of the salinity, the SSS-S regression coefficients are uniformly large. The SSS inputs yield smaller RMS errors in the retrieved S with respect to Argo profiles as their spatial or temporal resolution decreases. The projected SSS errors are dominant, and the retrieved S values are more accurate than those of climatology in the tropics except for the tropical Atlantic, where the regression errors are abnormally large. Below that level, because of the influence of a sea level anomaly, the areas of high-accuracy S values shift to higher latitudes except in the high-latitude southern oceans, where the projected SSS errors are abnormally large. A spectral analysis suggests that the CATDS-0.25° results are much noisier and that the BEC-L4-0.25° results are much smoother than those of the other retrievals. Aquarius-CAP-1° generates the smallest RMS errors, and Aquarius-V2-1° performs well in depicting large-scale phenomena. BEC-L3-0.25°,which has small RMS errors and remarkable mesoscale energy, is the best fit for portraying mesoscale features in the SSS and retrieved S fields. The current priority for retrieving S is to improve the reliability of satellite SSS especially at middle and high latitudes, by developing advanced algorithms, combining both sensors, or weighing between accuracy and resolutions.
文摘Introduction:An accurate and reliable detection of soil physicochemical attributes(SPAs)is a difficult and complicated issue in soil science.The SPA may be varied spatially and temporally with the complexity of nature.In the past,SPA detection has been obtained through routine soil chemical and physical laboratory analysis.However,these laboratory methods do not fulfill the rapid requirements.Accordingly,diffuse reflectance spectroscopy(DRS)can be used to nondestructively detect and characterize soil attributes with superior solution.In the present article,we report a study done through spectral curves in the visible(350–700 nm)and near-infrared(700–2500 nm)(VNIR)region of 74 soil specimens which were agglomerated by farming sectors of Phulambri Tehsil of the Aurangabad region of Maharashtra,India.The quantitative analysis of VNIR spectrum was done.Results:The spectra of agglomerated farming soils were acquired by the Analytical Spectral Device(ASD)Field spec 4 spectroradiometer.The soil spectra of the VNIR region were preprocessed to get pure spectra which were the input for regression modeling.The partial least squares regression(PLSR)model was computed to construct the calibration models,which were individually validated for the prediction of SPA from the soil spectrum.The computed model was based on a correlation study between reflected spectra and detected SPA.The detected SPAs were soil organic carbon(SOC),nitrogen(N),soil organic matter(SOM),pH values,electrical conductivity(EC),phosphorus(P),potassium(K),iron(Fe),sand,silt,and clay.The accuracy of the PLSR model-validated determinant(R^(2))values were SOC 0.89,N 0.68,SOM 0.93,pH values 0.82,EC 0.89,P 0.98,K 0.82,Fe 0.94,sand 0.98,silt 0.90,and clay 0.69 with root mean square error of prediction(RMSEP)3.51,4.34,2.66,2.12,4.11,1.41,4.22,1.56,1.89,1.97,and 9.91,respectively.According to the experimental results,the VNIR-DRS was better for detection of SPA and produced more accurate predictions for SPA.Conclusions:In conclusion,the methods examined here offered rapid and novel detection of SPA from reflectance spectroscopy.The outcome of the present research will be apt for precision farming and decision-making.
