Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed scenes.Much spatial information and spectral signatures of hyperspectral images(HSIs)present greater pot...Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed scenes.Much spatial information and spectral signatures of hyperspectral images(HSIs)present greater potential for detecting and classifying fine crops.The accurate classification of crop kinds utilizing hyperspectral remote sensing imaging(RSI)has become an indispensable application in the agricultural domain.It is significant for the prediction and growth monitoring of crop yields.Amongst the deep learning(DL)techniques,Convolution Neural Network(CNN)was the best method for classifying HSI for their incredible local contextual modeling ability,enabling spectral and spatial feature extraction.This article designs a Hybrid Multi-Strategy Aquila Optimization with a Deep Learning-Driven Crop Type Classification(HMAODL-CTC)algorithm onHSI.The proposed HMAODL-CTC model mainly intends to categorize different types of crops on HSI.To accomplish this,the presented HMAODL-CTC model initially carries out image preprocessing to improve image quality.In addition,the presented HMAODL-CTC model develops dilated convolutional neural network(CNN)for feature extraction.For hyperparameter tuning of the dilated CNN model,the HMAO algorithm is utilized.Eventually,the presented HMAODL-CTC model uses an extreme learning machine(ELM)model for crop type classification.A comprehensive set of simulations were performed to illustrate the enhanced performance of the presented HMAODL-CTC algorithm.Extensive comparison studies reported the improved performance of the presented HMAODL-CTC algorithm over other compared methods.展开更多
Mapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security,notably from climate change and,for that purpose,remote s...Mapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security,notably from climate change and,for that purpose,remote sensing is routinely used.However,identifying specific crop types,cropland,and cropping patterns using space-based observations is challenging because different crop types and cropping patterns have similarity spectral signatures.This study applied a methodology to identify cropland and specific crop types,including tobacco,wheat,barley,and gram,as well as the following cropping patterns:wheat-tobacco,wheat-gram,wheat-barley,and wheat-maize,which are common in Gujranwala District,Pakistan,the study region.The methodology consists of combining optical remote sensing images from Sentinel-2 and Landsat-8 with Machine Learning(ML)methods,namely a Decision Tree Classifier(DTC)and a Random Forest(RF)algorithm.The best time-periods for differentiating cropland from other land cover types were identified,and then Sentinel-2 and Landsat 8 NDVI-based time-series were linked to phenological parameters to determine the different crop types and cropping patterns over the study region using their temporal indices and ML algorithms.The methodology was subsequently evaluated using Landsat images,crop statistical data for 2020 and 2021,and field data on cropping patterns.The results highlight the high level of accuracy of the methodological approach presented using Sentinel-2 and Landsat-8 images,together with ML techniques,for mapping not only the distribution of cropland,but also crop types and cropping patterns when validated at the county level.These results reveal that this methodology has benefits for monitoring and evaluating food security in Pakistan,adding to the evidence base of other studies on the use of remote sensing to identify crop types and cropping patterns in other countries.展开更多
LiDAR data are becoming increasingly available,which has opened up many new applications.One such application is crop type mapping.Accurate crop type maps are critical for monitoring water use,estimating harvests and ...LiDAR data are becoming increasingly available,which has opened up many new applications.One such application is crop type mapping.Accurate crop type maps are critical for monitoring water use,estimating harvests and in precision agriculture.The traditional approach to obtaining maps of cultivated fields is by manually digitizing the fields from satellite or aerial imagery and then assigning crop type labels to each field-often informed by data collected during ground and aerial surveys.However,manual digitizing and labeling is time-consuming,expensive and subject to human error.Automated remote sensing methods is a cost-effective alternative,with machine learning gaining popularity for classifying crop types.This study evaluated the use of LiDAR data,Sentinel-2 imagery,aerial imagery and machine learning for differentiating five crop types in an intensively cultivated area.Different combinations of the three datasets were evaluated along with ten machine learning.The classification results were interpreted by comparing overall accuracies,kappa,standard deviation and f-score.It was found that LiDAR data successfully differentiated between different crop types,with XGBoost providing the highest overall accuracy of 87.8%.Furthermore,the crop type maps produced using the LiDAR data were in general agreement with those obtained by using Sentinel-2 data,with LiDAR obtaining a mean overall accuracy of 84.3%and Sentinel-2 a mean overall accuracy of 83.6%.However,the combination of all three datasets proved to be the most effective at differentiating between the crop types,with RF providing the highest overall accuracy of 94.4%.These findings provide a foundation for selecting the appropriate combination of remotely sensed data sources and machine learning algorithms for operational crop type mapping.展开更多
Drinking water-type fluorosis is the most harmful endemic disease in China with the largest number of sufferers. Although the implementation of the policy to alter water sources to lower fluoride level has effectively...Drinking water-type fluorosis is the most harmful endemic disease in China with the largest number of sufferers. Although the implementation of the policy to alter water sources to lower fluoride level has effectively controlled the spread of this kind of endemic disease,its prevalence could not thoroughly be stopped because the high-fluoride environmental background in these endemically diseased areas could still do harm to human health through food chain. Therefore,it is necessary to conduct a more deep-going study on the drinking water-type fluoro-sis. To investigate the effect of high fluorine environmental background on crops and human health in the hot spring-type fluorosis-diseased areas,local water,paddy soil,rice,whole vegetables and soils around their roots were sampled for analysis. The results were compared with those of the control groups in fluorosis-free areas which are similar to the fluorosis-diseased areas both in natural background and in social background. It is indicated that rice and vegetables can accumulate water-soluble fluorine either in soils or in irrigating water,and different crops have different abilities of fixing fluorine. The contents of fluorine in different parts of vegetables in the fluorosis-diseased and fluorosis-free areas were statistically categorized. The results showed that the fluorine contents of roots,tubers,leaves and flowers of vegetables in the fluorosis-diseased areas are 3.56,1.17,3.07 and 3.23 mg/kg,respectively. However,comparisons showed that in the fluorosis-free areas,the fluorine contents are 2.17,0.70,1.91 and 2.52 mg/kg,respectively. Moreover,different parts of a crop also show significantly different fluorine fixation abilities. It is demonstrated that the fluorine contents of the strongly metabolic parts are relatively high. For example,the fluo-rine contents of roots,leaves and flowers of vegetables are much higher than those of stems. The fluorine fixation ability of seeds is very weak. In order to reduce the risk of human body’s exposure to fluoride,the impact of hot spring water on the capability of crops to fix fluorine should be reduced as much as possible. It is of great importance to prevent crops from being irrigated with hot spring water and it is advisable to grow crops with relatively low ca-pabilities to enrich fluorine,such as those with seeds or tubers as the main edible parts in the areas which are se-verely affected by hot spring water.展开更多
[Objective] The aim was to explore the feasibility of using single spectrum image to classify crops based on multi-spectral image and Decision Tree Method. [Method] Taking the typical agriculture plantation area in Hu...[Objective] The aim was to explore the feasibility of using single spectrum image to classify crops based on multi-spectral image and Decision Tree Method. [Method] Taking the typical agriculture plantation area in Hulunbeier area, according to field measured spectrum data, the optimum time of main crops, barley, wheat, rapeseed, based on crops spectrum characteristics, by dint of decision-making tree method, and considering spectral matching method, classification of crops was studied such as SAM. [Result] By dint of Landsat TM image gained in the first half of August, based on geographic and atmospheric proof-reading, decision-making tree was constructed. Plantation information about wheat, barley, and rapeseed and plantation grassland was extracted successfully. The general classification accuracy reached 86.90%. Kappa coefficient was 0.831 1. [Conclusion] Taking typical spectrum image as data source, and applying Decision Tree Method to get crops type＇s information had fine application future.展开更多
Taking a three-year fertilization trial in mine reclamation soil from Shanxi Province, China as an example, the effects of different fertilization treatments on soil carbon storage and carbon fixation by corn were stu...Taking a three-year fertilization trial in mine reclamation soil from Shanxi Province, China as an example, the effects of different fertilization treatments on soil carbon storage and carbon fixation by corn were studied in this paper. Four treatments were designed in the experiment, including fertilizer （ F）, organic manure （ M）, half organic manure plus half fertilizer （ FM） and control （CK）. The results showed that fertilization had certain roles in increasing organic carbon storage of mine reclamation soil, and the application of single or combined organic and inorganic fertilizers had the most remarkable influence. Meanwhile, the treatment of single or combined organic and inorganic fertilizers could improve the carbon fixation capacity of corn prominently, and increased soil organic matter input. Thus, the application of organic manure or combined organic and inorganic fertilizer has great contribution to enhancing soil carbon sink and sustainable development of agriculture. However, the combined application of organic and inorganic fertilizer is the best choice for agricultural field based on economic consideration.展开更多
基金This work was supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2023R384)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed scenes.Much spatial information and spectral signatures of hyperspectral images(HSIs)present greater potential for detecting and classifying fine crops.The accurate classification of crop kinds utilizing hyperspectral remote sensing imaging(RSI)has become an indispensable application in the agricultural domain.It is significant for the prediction and growth monitoring of crop yields.Amongst the deep learning(DL)techniques,Convolution Neural Network(CNN)was the best method for classifying HSI for their incredible local contextual modeling ability,enabling spectral and spatial feature extraction.This article designs a Hybrid Multi-Strategy Aquila Optimization with a Deep Learning-Driven Crop Type Classification(HMAODL-CTC)algorithm onHSI.The proposed HMAODL-CTC model mainly intends to categorize different types of crops on HSI.To accomplish this,the presented HMAODL-CTC model initially carries out image preprocessing to improve image quality.In addition,the presented HMAODL-CTC model develops dilated convolutional neural network(CNN)for feature extraction.For hyperparameter tuning of the dilated CNN model,the HMAO algorithm is utilized.Eventually,the presented HMAODL-CTC model uses an extreme learning machine(ELM)model for crop type classification.A comprehensive set of simulations were performed to illustrate the enhanced performance of the presented HMAODL-CTC algorithm.Extensive comparison studies reported the improved performance of the presented HMAODL-CTC algorithm over other compared methods.
文摘Mapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security,notably from climate change and,for that purpose,remote sensing is routinely used.However,identifying specific crop types,cropland,and cropping patterns using space-based observations is challenging because different crop types and cropping patterns have similarity spectral signatures.This study applied a methodology to identify cropland and specific crop types,including tobacco,wheat,barley,and gram,as well as the following cropping patterns:wheat-tobacco,wheat-gram,wheat-barley,and wheat-maize,which are common in Gujranwala District,Pakistan,the study region.The methodology consists of combining optical remote sensing images from Sentinel-2 and Landsat-8 with Machine Learning(ML)methods,namely a Decision Tree Classifier(DTC)and a Random Forest(RF)algorithm.The best time-periods for differentiating cropland from other land cover types were identified,and then Sentinel-2 and Landsat 8 NDVI-based time-series were linked to phenological parameters to determine the different crop types and cropping patterns over the study region using their temporal indices and ML algorithms.The methodology was subsequently evaluated using Landsat images,crop statistical data for 2020 and 2021,and field data on cropping patterns.The results highlight the high level of accuracy of the methodological approach presented using Sentinel-2 and Landsat-8 images,together with ML techniques,for mapping not only the distribution of cropland,but also crop types and cropping patterns when validated at the county level.These results reveal that this methodology has benefits for monitoring and evaluating food security in Pakistan,adding to the evidence base of other studies on the use of remote sensing to identify crop types and cropping patterns in other countries.
基金This work forms part of a larger project titled“Salt Accumulation and Waterlogging Monitoring System(SAWMS)Development”which was initiated and funded by the Water Research Commission(WRC)of South Africa(contract number K5/2558//4)More information about this project is available in WRC Report No TT 782/18,titled SALT ACCUMULATION AND WATERLOGGING MONITORING SYSTEM(SAWMS)DEVELOPMENT(ISBN 978-0-6392-0084-2)+1 种基金available at www.wrc.org.za.This work was also supported by the National Research Foundation(grant number 112300)The authors would also like to thank www.linguafix.net for their language editing services.
文摘LiDAR data are becoming increasingly available,which has opened up many new applications.One such application is crop type mapping.Accurate crop type maps are critical for monitoring water use,estimating harvests and in precision agriculture.The traditional approach to obtaining maps of cultivated fields is by manually digitizing the fields from satellite or aerial imagery and then assigning crop type labels to each field-often informed by data collected during ground and aerial surveys.However,manual digitizing and labeling is time-consuming,expensive and subject to human error.Automated remote sensing methods is a cost-effective alternative,with machine learning gaining popularity for classifying crop types.This study evaluated the use of LiDAR data,Sentinel-2 imagery,aerial imagery and machine learning for differentiating five crop types in an intensively cultivated area.Different combinations of the three datasets were evaluated along with ten machine learning.The classification results were interpreted by comparing overall accuracies,kappa,standard deviation and f-score.It was found that LiDAR data successfully differentiated between different crop types,with XGBoost providing the highest overall accuracy of 87.8%.Furthermore,the crop type maps produced using the LiDAR data were in general agreement with those obtained by using Sentinel-2 data,with LiDAR obtaining a mean overall accuracy of 84.3%and Sentinel-2 a mean overall accuracy of 83.6%.However,the combination of all three datasets proved to be the most effective at differentiating between the crop types,with RF providing the highest overall accuracy of 94.4%.These findings provide a foundation for selecting the appropriate combination of remotely sensed data sources and machine learning algorithms for operational crop type mapping.
基金financially supported jointly by the National Natural Science Foundation of China (Grant No.40601004)the Scientific and Technological Project of the Educa-tion Department of Jiangxi Province (No. GJJ08032)+1 种基金the K.C. Wong Education FoundationHong Kong and China’s Post-doctoral Science Funds
文摘Drinking water-type fluorosis is the most harmful endemic disease in China with the largest number of sufferers. Although the implementation of the policy to alter water sources to lower fluoride level has effectively controlled the spread of this kind of endemic disease,its prevalence could not thoroughly be stopped because the high-fluoride environmental background in these endemically diseased areas could still do harm to human health through food chain. Therefore,it is necessary to conduct a more deep-going study on the drinking water-type fluoro-sis. To investigate the effect of high fluorine environmental background on crops and human health in the hot spring-type fluorosis-diseased areas,local water,paddy soil,rice,whole vegetables and soils around their roots were sampled for analysis. The results were compared with those of the control groups in fluorosis-free areas which are similar to the fluorosis-diseased areas both in natural background and in social background. It is indicated that rice and vegetables can accumulate water-soluble fluorine either in soils or in irrigating water,and different crops have different abilities of fixing fluorine. The contents of fluorine in different parts of vegetables in the fluorosis-diseased and fluorosis-free areas were statistically categorized. The results showed that the fluorine contents of roots,tubers,leaves and flowers of vegetables in the fluorosis-diseased areas are 3.56,1.17,3.07 and 3.23 mg/kg,respectively. However,comparisons showed that in the fluorosis-free areas,the fluorine contents are 2.17,0.70,1.91 and 2.52 mg/kg,respectively. Moreover,different parts of a crop also show significantly different fluorine fixation abilities. It is demonstrated that the fluorine contents of the strongly metabolic parts are relatively high. For example,the fluo-rine contents of roots,leaves and flowers of vegetables are much higher than those of stems. The fluorine fixation ability of seeds is very weak. In order to reduce the risk of human body’s exposure to fluoride,the impact of hot spring water on the capability of crops to fix fluorine should be reduced as much as possible. It is of great importance to prevent crops from being irrigated with hot spring water and it is advisable to grow crops with relatively low ca-pabilities to enrich fluorine,such as those with seeds or tubers as the main edible parts in the areas which are se-verely affected by hot spring water.
基金Supported by the Open Subject of Key Lab of Resources Remote-sensing and Digital Agriculture in Agricultural Department(RDA1008)~~
文摘[Objective] The aim was to explore the feasibility of using single spectrum image to classify crops based on multi-spectral image and Decision Tree Method. [Method] Taking the typical agriculture plantation area in Hulunbeier area, according to field measured spectrum data, the optimum time of main crops, barley, wheat, rapeseed, based on crops spectrum characteristics, by dint of decision-making tree method, and considering spectral matching method, classification of crops was studied such as SAM. [Result] By dint of Landsat TM image gained in the first half of August, based on geographic and atmospheric proof-reading, decision-making tree was constructed. Plantation information about wheat, barley, and rapeseed and plantation grassland was extracted successfully. The general classification accuracy reached 86.90%. Kappa coefficient was 0.831 1. [Conclusion] Taking typical spectrum image as data source, and applying Decision Tree Method to get crops type＇s information had fine application future.
基金Supported by the International Science and Technology Cooperation Program of China(2011DFR31230)Major Science and Technology Project of Shanxi Province,China(20121101009)Key Project of Shanxi Academy of Agricultural Sciences,China(2013zd12)
文摘Taking a three-year fertilization trial in mine reclamation soil from Shanxi Province, China as an example, the effects of different fertilization treatments on soil carbon storage and carbon fixation by corn were studied in this paper. Four treatments were designed in the experiment, including fertilizer （ F）, organic manure （ M）, half organic manure plus half fertilizer （ FM） and control （CK）. The results showed that fertilization had certain roles in increasing organic carbon storage of mine reclamation soil, and the application of single or combined organic and inorganic fertilizers had the most remarkable influence. Meanwhile, the treatment of single or combined organic and inorganic fertilizers could improve the carbon fixation capacity of corn prominently, and increased soil organic matter input. Thus, the application of organic manure or combined organic and inorganic fertilizer has great contribution to enhancing soil carbon sink and sustainable development of agriculture. However, the combined application of organic and inorganic fertilizer is the best choice for agricultural field based on economic consideration.
