A study on cotton yield prediction based on the chlorophyll fluorescence parameters of upper leaves
The early and accurate monitoring of crop yield is important for field management, storage needs, and cash flow budgeting. Traditional cotton yield measurement methods are time-consuming, labor-intensive, and subjective. Chlorophyll fluorescence signals originate from within the plant and have the...
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| Format: | Article |
| Language: | English |
| Published: |
AcademicPres
2022-09-01
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| Series: | Notulae Botanicae Horti Agrobotanici Cluj-Napoca |
| Subjects: | |
| Online Access: | https://www.notulaebotanicae.ro/index.php/nbha/article/view/12775 |
| _version_ | 1818027216213114880 |
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| author | Yiren DING Shizhe QIN Lulu MA Xiangyu CHEN Qiushuang YAO Mi YANG Yiru MA Xin LV Ze ZHANG |
| author_facet | Yiren DING Shizhe QIN Lulu MA Xiangyu CHEN Qiushuang YAO Mi YANG Yiru MA Xin LV Ze ZHANG |
| author_sort | Yiren DING |
| collection | DOAJ |
| description |
The early and accurate monitoring of crop yield is important for field management, storage needs, and cash flow budgeting. Traditional cotton yield measurement methods are time-consuming, labor-intensive, and subjective. Chlorophyll fluorescence signals originate from within the plant and have the advantages of being fast and non-destructive, and the relevant parameters can reflect the intrinsic physiological characteristics of the plant. Therefore, in this study, the top four functional leaves of cotton plants at the beginning of the flocculation stage were used to investigate the pattern of the response of chlorophyll fluorescence parameters (e.g., F0, Fm, Fv/F0, and Fv/Fm) to nitrogen, and the cumulative fluorescence parameters were constructed by combining them with the leaf area index to clarify the correlation between chlorophyll fluorescence parameters and cotton yield. Support vector machine regression (SVM), an artificial neural network (BP), and an XGBoost regression tree were used to establish a cotton yield prediction model. Chlorophyll fluorescence parameters showed the same performance as photosynthetic parameters, which decreased as leaf position decreased. It showed a trend of increasing and then decreasing with increasing N application level, reaching the maximum value at 240 kg·hm-2 of N application. The correlation between fluorescence parameters and yield in the first, second, and third leaves was significantly higher than that in the fourth leaf, and the correlation between fluorescence accumulation and yield in each leaf was significantly higher than that of the fluorescence parameters, with the best performance of Fv/Fm accumulation found in the second leaf. The correlation between Fv/Fm accumulation and yield in the top three leaves combined was significantly higher than that in the top four leaves. The correlation coefficient between Fv/Fm accumulation and yield was the highest, indicating the feasibility of applying chlorophyll fluorescence to estimate yield. Based on the machine learning algorithm used to construct a cotton yield prediction model, the estimation models of Fv/F0 accumulation and yield of the top two leaves combined as well as top three leaves combined were superior. The estimation model coefficient of determination of the top two leaves combined in the BP algorithm was the highest. In general, the Fv/F0 accumulation of the top two leaves combined could more reliably predict cotton yield, which could provide technical support for cotton growth monitoring and precision management.
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| first_indexed | 2024-12-10T04:44:22Z |
| format | Article |
| id | doaj.art-a51246fba44145b186b6ad57708c3420 |
| institution | Directory Open Access Journal |
| issn | 0255-965X 1842-4309 |
| language | English |
| last_indexed | 2024-12-10T04:44:22Z |
| publishDate | 2022-09-01 |
| publisher | AcademicPres |
| record_format | Article |
| series | Notulae Botanicae Horti Agrobotanici Cluj-Napoca |
| spelling | doaj.art-a51246fba44145b186b6ad57708c34202022-12-22T02:01:47ZengAcademicPresNotulae Botanicae Horti Agrobotanici Cluj-Napoca0255-965X1842-43092022-09-0150310.15835/nbha50312775A study on cotton yield prediction based on the chlorophyll fluorescence parameters of upper leavesYiren DING0Shizhe QIN1Lulu MA2Xiangyu CHEN3Qiushuang YAO4Mi YANG5Yiru MA6Xin LV7Ze ZHANG8Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 The early and accurate monitoring of crop yield is important for field management, storage needs, and cash flow budgeting. Traditional cotton yield measurement methods are time-consuming, labor-intensive, and subjective. Chlorophyll fluorescence signals originate from within the plant and have the advantages of being fast and non-destructive, and the relevant parameters can reflect the intrinsic physiological characteristics of the plant. Therefore, in this study, the top four functional leaves of cotton plants at the beginning of the flocculation stage were used to investigate the pattern of the response of chlorophyll fluorescence parameters (e.g., F0, Fm, Fv/F0, and Fv/Fm) to nitrogen, and the cumulative fluorescence parameters were constructed by combining them with the leaf area index to clarify the correlation between chlorophyll fluorescence parameters and cotton yield. Support vector machine regression (SVM), an artificial neural network (BP), and an XGBoost regression tree were used to establish a cotton yield prediction model. Chlorophyll fluorescence parameters showed the same performance as photosynthetic parameters, which decreased as leaf position decreased. It showed a trend of increasing and then decreasing with increasing N application level, reaching the maximum value at 240 kg·hm-2 of N application. The correlation between fluorescence parameters and yield in the first, second, and third leaves was significantly higher than that in the fourth leaf, and the correlation between fluorescence accumulation and yield in each leaf was significantly higher than that of the fluorescence parameters, with the best performance of Fv/Fm accumulation found in the second leaf. The correlation between Fv/Fm accumulation and yield in the top three leaves combined was significantly higher than that in the top four leaves. The correlation coefficient between Fv/Fm accumulation and yield was the highest, indicating the feasibility of applying chlorophyll fluorescence to estimate yield. Based on the machine learning algorithm used to construct a cotton yield prediction model, the estimation models of Fv/F0 accumulation and yield of the top two leaves combined as well as top three leaves combined were superior. The estimation model coefficient of determination of the top two leaves combined in the BP algorithm was the highest. In general, the Fv/F0 accumulation of the top two leaves combined could more reliably predict cotton yield, which could provide technical support for cotton growth monitoring and precision management. https://www.notulaebotanicae.ro/index.php/nbha/article/view/12775cottonchlorophyll fluorescence parametersleaf positionmachine learningyield |
| spellingShingle | Yiren DING Shizhe QIN Lulu MA Xiangyu CHEN Qiushuang YAO Mi YANG Yiru MA Xin LV Ze ZHANG A study on cotton yield prediction based on the chlorophyll fluorescence parameters of upper leaves Notulae Botanicae Horti Agrobotanici Cluj-Napoca cotton chlorophyll fluorescence parameters leaf position machine learning yield |
| title | A study on cotton yield prediction based on the chlorophyll fluorescence parameters of upper leaves |
| title_full | A study on cotton yield prediction based on the chlorophyll fluorescence parameters of upper leaves |
| title_fullStr | A study on cotton yield prediction based on the chlorophyll fluorescence parameters of upper leaves |
| title_full_unstemmed | A study on cotton yield prediction based on the chlorophyll fluorescence parameters of upper leaves |
| title_short | A study on cotton yield prediction based on the chlorophyll fluorescence parameters of upper leaves |
| title_sort | study on cotton yield prediction based on the chlorophyll fluorescence parameters of upper leaves |
| topic | cotton chlorophyll fluorescence parameters leaf position machine learning yield |
| url | https://www.notulaebotanicae.ro/index.php/nbha/article/view/12775 |
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