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Analysis of morphological characteristics of gravels based on digital image processing technology and self-organizing map 作者： XU Tao YU Huan QIU Xia KONG Bo XIANG Qing XU Xiaoyu FU Hao 作者单位： 1. College of Earth Science, Chengdu University of Technology, Chengdu 610059, China; 2. Beijing SuperMap Software Co., Ltd., Beijing 100015, China; 3. Sichuan Real Estate Registration Center, Chengdu 610014, China; 4. Chengdu Institute of Mountain Land and Disasters, Chinese Academy of Sciences, Chengdu 610041, China; 5. School of Earth Systems and Sustainability, Southern Illinois University Carbondale, Carbondale, IL 62901, United States of America; 6. Environmental Resources and Policy, Southern Illinois University Carbondale, Carbondale, IL 62901, United States of America; 7. Sichuan Institute of Land and Space Ecological Restoration and Geohazard Prevention, Chengdu 610081, China 通讯作者： YU Huan Email:[email protected] 提交时间： 2023-03-19 self-organizing map digital image processing morphological characteristics multivariate statistical method environmental monitoring 期刊： Journal of Arid Land 分类： 地球科学 >> 地理学 引用： ChinaXiv:202303.00188 (或此版本 ChinaXiv:202303.00188V1 ) DOI:10.1007/s40333-023-0010-y CSTR:32003.36.ChinaXiv.202303.00188.V1 推荐引用方式： XU Tao,YU Huan,QIU Xia,KONG Bo,XIANG Qing,XU Xiaoyu,FU Hao.(2023).Analysis of morphological characteristics of gravels based on digital image processing technology and self-organizing map.Journal of Arid Land.doi:10.1007/s40333-023-0010-y 版本历史 [V1] 2023-03-19 22:30:43 ChinaXiv:202303.00188V1 下载全文

Analysis of morphological characteristics of gravels based on digital image processing technology and self-organizing map 作者： XU Tao YU Huan QIU Xia KONG Bo XIANG Qing XU Xiaoyu FU Hao 作者单位： 1. College of Earth Science, Chengdu University of Technology, Chengdu 610059, China; 2. Beijing SuperMap Software Co., Ltd., Beijing 100015, China; 3. Sichuan Real Estate Registration Center, Chengdu 610014, China; 4. Chengdu Institute of Mountain Land and Disasters, Chinese Academy of Sciences, Chengdu 610041, China; 5. School of Earth Systems and Sustainability, Southern Illinois University Carbondale, Carbondale, IL 62901, United States of America; 6. Environmental Resources and Policy, Southern Illinois University Carbondale, Carbondale, IL 62901, United States of America; 7. Sichuan Institute of Land and Space Ecological Restoration and Geohazard Prevention, Chengdu 610081, China 通讯作者： YU Huan Email:[email protected] 提交时间： 2023-03-19 self-organizing map digital image processing morphological characteristics multivariate statistical method environmental monitoring 期刊： Journal of Arid Land 分类： 地球科学 >> 地理学 引用： ChinaXiv:202303.00188 (或此版本 ChinaXiv:202303.00188V1 ) DOI:10.1007/s40333-023-0010-y CSTR:32003.36.ChinaXiv.202303.00188.V1 推荐引用方式： XU Tao,YU Huan,QIU Xia,KONG Bo,XIANG Qing,XU Xiaoyu,FU Hao.(2023).Analysis of morphological characteristics of gravels based on digital image processing technology and self-organizing map.Journal of Arid Land.doi:10.1007/s40333-023-0010-y 版本历史 [V1] 2023-03-19 22:30:43 ChinaXiv:202303.00188V1 下载全文

Analysis of morphological characteristics of gravels based on digital image processing technology and self-organizing map 作者： XU Tao YU Huan QIU Xia KONG Bo XIANG Qing XU Xiaoyu FU Hao 作者单位： 1. College of Earth Science, Chengdu University of Technology, Chengdu 610059, China; 2. Beijing SuperMap Software Co., Ltd., Beijing 100015, China; 3. Sichuan Real Estate Registration Center, Chengdu 610014, China; 4. Chengdu Institute of Mountain Land and Disasters, Chinese Academy of Sciences, Chengdu 610041, China; 5. School of Earth Systems and Sustainability, Southern Illinois University Carbondale, Carbondale, IL 62901, United States of America; 6. Environmental Resources and Policy, Southern Illinois University Carbondale, Carbondale, IL 62901, United States of America; 7. Sichuan Institute of Land and Space Ecological Restoration and Geohazard Prevention, Chengdu 610081, China 通讯作者： YU Huan Email:[email protected] 提交时间： 2023-03-19 self-organizing map digital image processing morphological characteristics multivariate statistical method environmental monitoring 期刊： Journal of Arid Land 分类： 地球科学 >> 地理学 引用： ChinaXiv:202303.00188 (或此版本 ChinaXiv:202303.00188V1 ) DOI:10.1007/s40333-023-0010-y CSTR:32003.36.ChinaXiv.202303.00188.V1 推荐引用方式： XU Tao,YU Huan,QIU Xia,KONG Bo,XIANG Qing,XU Xiaoyu,FU Hao.(2023).Analysis of morphological characteristics of gravels based on digital image processing technology and self-organizing map.Journal of Arid Land.doi:10.1007/s40333-023-0010-y 版本历史 [V1] 2023-03-19 22:30:43 ChinaXiv:202303.00188V1 下载全文

Analysis of morphological characteristics of gravels based on digital image processing technology and self-organizing map 作者： XU Tao YU Huan QIU Xia KONG Bo XIANG Qing XU Xiaoyu FU Hao 作者单位： 1. College of Earth Science, Chengdu University of Technology, Chengdu 610059, China; 2. Beijing SuperMap Software Co., Ltd., Beijing 100015, China; 3. Sichuan Real Estate Registration Center, Chengdu 610014, China; 4. Chengdu Institute of Mountain Land and Disasters, Chinese Academy of Sciences, Chengdu 610041, China; 5. School of Earth Systems and Sustainability, Southern Illinois University Carbondale, Carbondale, IL 62901, United States of America; 6. Environmental Resources and Policy, Southern Illinois University Carbondale, Carbondale, IL 62901, United States of America; 7. Sichuan Institute of Land and Space Ecological Restoration and Geohazard Prevention, Chengdu 610081, China 通讯作者： YU Huan Email:[email protected] 提交时间： 2023-03-19 self-organizing map digital image processing morphological characteristics multivariate statistical method environmental monitoring 期刊： Journal of Arid Land 分类： 地球科学 >> 地理学 引用： ChinaXiv:202303.00188 (或此版本 ChinaXiv:202303.00188V1 ) DOI:10.1007/s40333-023-0010-y CSTR:32003.36.ChinaXiv.202303.00188.V1 推荐引用方式： XU Tao,YU Huan,QIU Xia,KONG Bo,XIANG Qing,XU Xiaoyu,FU Hao.(2023).Analysis of morphological characteristics of gravels based on digital image processing technology and self-organizing map.Journal of Arid Land.doi:10.1007/s40333-023-0010-y 版本历史 [V1] 2023-03-19 22:30:43 ChinaXiv:202303.00188V1 下载全文

The plant requires thirteen different nutrients. The two main types of nutrients are micronutrients and macronutrients. Diseases develop due to deficiency of vital nutrients, resulting in colored spots on the leaves. Plant development is affected by toxicity or lack of one or more of these nutrients, resulting in plant death. As a result, a continuous monitoring system is necessary to know the nutritional status of the plants to enhance production efficiency and output. Optical image recognition-based medical technology can identify indicators of inaccuracy faster than the human eye. Consequently, farmers are prepared to take prompt and effective remedial action. This article investigates the nutrient deficits in plants using image processing techniques.

The plant requires thirteen different nutrients. The two main types of nutrients are micronutrients and macronutrients. Diseases develop due to deficiency of vital nutrients, resulting in colored spots on the leaves. Plant development is affected by toxicity or lack of one or more of these nutrients, resulting in plant death. As a result, a continuous monitoring system is necessary to know the nutritional status of the plants to enhance production efficiency and output. Optical image recognition-based medical technology can identify indicators of inaccuracy faster than the human eye. Consequently, farmers are prepared to take prompt and effective remedial action. This article investigates the nutrient deficits in plants using image processing techniques.

The plant requires thirteen different nutrients. The two main types of nutrients are micronutrients and macronutrients. Diseases develop due to deficiency of vital nutrients, resulting in colored spots on the leaves. Plant development is affected by toxicity or lack of one or more of these nutrients, resulting in plant death. As a result, a continuous monitoring system is necessary to know the nutritional status of the plants to enhance production efficiency and output. Optical image recognition-based medical technology can identify indicators of inaccuracy faster than the human eye. Consequently, farmers are prepared to take prompt and effective remedial action. This article investigates the nutrient deficits in plants using image processing techniques.