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      Estimation of wheat tiller density using remote sensing data and machine learning methods

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          Abstract

          The tiller density is a key agronomic trait of winter wheat that is essential to field management and yield estimation. The traditional method of obtaining the wheat tiller density is based on manual counting, which is inefficient and error prone. In this study, we established machine learning models to estimate the wheat tiller density in the field using hyperspectral and multispectral remote sensing data. The results showed that the vegetation indices related to vegetation cover and leaf area index are more suitable for tiller density estimation. The optimal mean relative error for hyperspectral data was 5.46%, indicating that the results were more accurate than those for multispectral data, which had a mean relative error of 7.71%. The gradient boosted regression tree (GBRT) and random forest (RF) methods gave the best estimation accuracy when the number of samples was less than around 140 and greater than around 140, respectively. The results of this study support the extension of the tested methods to the large-scale monitoring of tiller density based on remote sensing data.

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          Food security: the challenge of feeding 9 billion people.

          Continuing population and consumption growth will mean that the global demand for food will increase for at least another 40 years. Growing competition for land, water, and energy, in addition to the overexploitation of fisheries, will affect our ability to produce food, as will the urgent requirement to reduce the impact of the food system on the environment. The effects of climate change are a further threat. But the world can produce more food and can ensure that it is used more efficiently and equitably. A multifaceted and linked global strategy is needed to ensure sustainable and equitable food security, different components of which are explored here.
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            Global food demand and the sustainable intensification of agriculture.

            Global food demand is increasing rapidly, as are the environmental impacts of agricultural expansion. Here, we project global demand for crop production in 2050 and evaluate the environmental impacts of alternative ways that this demand might be met. We find that per capita demand for crops, when measured as caloric or protein content of all crops combined, has been a similarly increasing function of per capita real income since 1960. This relationship forecasts a 100-110% increase in global crop demand from 2005 to 2050. Quantitative assessments show that the environmental impacts of meeting this demand depend on how global agriculture expands. If current trends of greater agricultural intensification in richer nations and greater land clearing (extensification) in poorer nations were to continue, ~1 billion ha of land would be cleared globally by 2050, with CO(2)-C equivalent greenhouse gas emissions reaching ~3 Gt y(-1) and N use ~250 Mt y(-1) by then. In contrast, if 2050 crop demand was met by moderate intensification focused on existing croplands of underyielding nations, adaptation and transfer of high-yielding technologies to these croplands, and global technological improvements, our analyses forecast land clearing of only ~0.2 billion ha, greenhouse gas emissions of ~1 Gt y(-1), and global N use of ~225 Mt y(-1). Efficient management practices could substantially lower nitrogen use. Attainment of high yields on existing croplands of underyielding nations is of great importance if global crop demand is to be met with minimal environmental impacts.
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              Yield Trends Are Insufficient to Double Global Crop Production by 2050

              Several studies have shown that global crop production needs to double by 2050 to meet the projected demands from rising population, diet shifts, and increasing biofuels consumption. Boosting crop yields to meet these rising demands, rather than clearing more land for agriculture has been highlighted as a preferred solution to meet this goal. However, we first need to understand how crop yields are changing globally, and whether we are on track to double production by 2050. Using ∼2.5 million agricultural statistics, collected for ∼13,500 political units across the world, we track four key global crops—maize, rice, wheat, and soybean—that currently produce nearly two-thirds of global agricultural calories. We find that yields in these top four crops are increasing at 1.6%, 1.0%, 0.9%, and 1.3% per year, non-compounding rates, respectively, which is less than the 2.4% per year rate required to double global production by 2050. At these rates global production in these crops would increase by ∼67%, ∼42%, ∼38%, and ∼55%, respectively, which is far below what is needed to meet projected demands in 2050. We present detailed maps to identify where rates must be increased to boost crop production and meet rising demands.
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                Author and article information

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                21 December 2022
                2022
                : 13
                : 1075856
                Affiliations
                [1] 1 Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences , Beijing, China
                [2] 2 College of Resource and Environment, University of Chinese Academy of Sciences , Beijing, China
                [3] 3 International Research Center of Big Data for Sustainable Development Goals , Beijing, China
                [4] 4 Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of China , Beijing, China
                [5] 5 Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences , Beijing, China
                [6] 6 Aerospace ShuWei High Tech. Co., Ltd. , Beijing, China
                [7] 7 Beijing Azup Scientific Co., Ltd. , Beijing, China
                [8] 8 Department of Geography, Texas A&M University , TX, United States
                [9] 9 School of Geography and Information Engineering, China University of Geosciences (Wuhan) , Wuhan, China
                Author notes

                Edited by: Huajian Liu, University of Adelaide, Australia

                Reviewed by: Jianjun Du, Beijing Research Center for Information Technology in Agriculture, China; Zipeng Zhang, Xinjiang University, China

                *Correspondence: Bing Zhang, zhangbing@ 123456aircas.ac.cn

                This article was submitted to Technical Advances in Plant Science, a section of the journal Frontiers in Plant Science

                Article
                10.3389/fpls.2022.1075856
                9810811
                f5dc764e-1b5a-4402-953b-1dbd584c0df9
                Copyright © 2022 Hu, Zhang, Peng, Yu, Liu, Xiao, Li, Dong, Fang, Ye, Huang, Lin, Wang, Cheng and Yang

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 21 October 2022
                : 28 November 2022
                Page count
                Figures: 10, Tables: 2, Equations: 4, References: 59, Pages: 14, Words: 7474
                Funding
                Funded by: National Natural Science Foundation of China , doi 10.13039/501100001809;
                Funded by: National Key Research and Development Program of China , doi 10.13039/501100012166;
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                winter wheat,tiller density,uav hyperspectral,vegetation index,random forest,gradient boosted regression trees

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