Farmland mulching and optimized irrigation increase water productivity and seed yield by regulating functional parameters of soybean (Glycine max L.) leaves

Tang, Zijun; Lu, Junsheng; Xiang, Youzhen; Shi, Hongzhao; Sun, Tao; Zhang, Wei; Wang, Han; Zhang, Xueyan; Li, Zhijun; Zhang, Fucang

doi:10.1016/j.agwat.2024.108875

Cited by 6 publications

(4 citation statements)

References 59 publications

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“…After performing continuous wavelet transform on the hyperspectral reflectance data, the correlation between the selected spectral indices and winter wheat chlorophyll content showed a significant improvement. When the modeling method was the same, but the input combinations differed, the predictive model accuracies ranked as follows: 2 6 , 2 3 , 2 5 , 2 2 , 2 0 , 2 4 , and 2 7 . On the other hand, when the input combinations were the same but the modeling methods varied, the predictive model accuracies ranked as follows: GA-BP, RF, and SVM.…”

Section: Discussionmentioning

confidence: 99%

“…R 2 , RMSE, and MRE aspects of the comprehensive evaluation of the model accuracy, different modeling combinations for winter wheat chlorophyll content prediction results are shown in Table 3 and Figures 5 and 6. The results showed that R 2 of the winter wheat chlorophyll content estimation model under different scales of continuous wavelet transform were 2 6 , 2 3 , 2 5 , 2 2 , 2 0 , 2 4 , and 2 7 in descending order; RMSE and MRE were 2 6 , 2 3 , 2 5 , 2 2 , 2 0 , 2 4 , and 2 7 in descending order; and RF, GA-BP, and SVM constructed by 2 6 -scale continuous wavelet transform spectral indices of winter wheat were used to predict the chlorophyll content of the model in the validation set. The validation set R 2 values of the chlorophyll content prediction model for winter wheat were 0.858 and 0.859, respectively, both higher than 0.310 (p ≤ 0.01), indicating a highly significant correlation level with better linear fitting results.…”

Section: Winter Wheat Chlorophyll Content Prediction Model Constructionmentioning

confidence: 99%

“…Agronomy 2024, 14, 1309 2 of 18 Chlorophyll is one of the most important pigments in plants; in the process of photosynthesis, chlorophyll plays a crucial role in facilitating energy conversion and sustaining the growth and development of plants. Furthermore, it acts as a protective and regulatory agent in maintaining plant health, thereby aiding in plant adaptation to external environmental conditions [4]. Romina et al [5] used different kinds of chlorophyll instruments to determine the nitrogen content in crops by measuring the chlorophyll content of sweet peppers.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Winter Wheat Chlorophyll Content Based on Wavelet Transform and the Optimal Spectral Index

Liu,

Li,

Xiang

et al. 2024

Agronomy

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Hyperspectral remote sensing technology plays a vital role in advancing modern precision agriculture due to its non-destructive and efficient nature. To achieve accurate monitoring of winter wheat chlorophyll content, this study utilized 68 sets of chlorophyll content data and hyperspectral measurements collected during the jointing stage of winter wheat over two consecutive years (2019–2020), under various fertilization types and nitrogen application levels. Continuous wavelet transform was applied to transform the original reflectance, ranging from 21 to 210, and the correlation matrix method was utilized to identify the spectral index at each scale, with the highest correlation to winter wheat chlorophyll content as the optimal spectral index combination input. Subsequently, winter wheat chlorophyll content prediction models were developed using three machine learning methods: random forest (RF), support vector machine (SVM), and a genetic algorithm-optimized backpropagation neural network (GA-BP). The results indicate that the spectral data processed through continuous wavelet transform at seven scales, from 21 to 27, show the highest correlation with winter wheat chlorophyll content at a scale of 26, with a correlation coefficient of 0.738, compared with the correlation of 0.611 of the original reflectance, and the accuracy is improved by 20.7%. The average highest correlation value between the spectral index at scale 26 and winter wheat chlorophyll content is 0.752. As the scale of wavelet transform increases, the correlation between the spectral index and winter wheat chlorophyll content and the accuracy of the predictive model show a trend of first increasing and then decreasing. The optimal input variables for predicting winter wheat chlorophyll content and the best machine learning method are the spectral data at a scale of 26 processing combined with the GA-BP model. The optimal predictive model has a validation set coefficient of determination (R2) of 0.859, root mean square error (RMSE) of 1.366, and mean relative error (MRE) of 2.920%. The results show that the prediction model can provide a technical basis for improving the hyperspectral inversion accuracy of winter wheat chlorophyll and modern precision agriculture.

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Section: Discussionmentioning

confidence: 99%

Section: Winter Wheat Chlorophyll Content Prediction Model Constructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of Winter Wheat Chlorophyll Content Based on Wavelet Transform and the Optimal Spectral Index

Liu,

Li,

Xiang

et al. 2024

Agronomy

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“…Soybean ( Glycine max L.), as one of the major leguminous crops globally, plays a crucial role in global food security and sustainable agriculture [ 1 ]. In arid and semi-arid regions, soybean cultivation faces multiple challenges, often associated with limited water resources and irregular precipitation patterns [ 2 ]. Being a water-consuming crop, soybean requires adequate water for normal growth [ 3 ]; however, water scarcity in dry areas frequently leads to water stress, constraining soybean growth and resulting in yield reduction [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Soybean (Glycine max L.) Leaf Moisture Estimation Based on Multisource Unmanned Aerial Vehicle Image Feature Fusion

Yang,

Li,

Chen

et al. 2024

Plants

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Efficient acquisition of crop leaf moisture information holds significant importance for agricultural production. This information provides farmers with accurate data foundations, enabling them to implement timely and effective irrigation management strategies, thereby maximizing crop growth efficiency and yield. In this study, unmanned aerial vehicle (UAV) multispectral technology was employed. Through two consecutive years of field experiments (2021–2022), soybean (Glycine max L.) leaf moisture data and corresponding UAV multispectral images were collected. Vegetation indices, canopy texture features, and randomly extracted texture indices in combination, which exhibited strong correlations with previous studies and crop parameters, were established. By analyzing the correlation between these parameters and soybean leaf moisture, parameters with significantly correlated coefficients (p < 0.05) were selected as input variables for the model (combination 1: vegetation indices; combination 2: texture features; combination 3: randomly extracted texture indices in combination; combination 4: combination of vegetation indices, texture features, and randomly extracted texture indices). Subsequently, extreme learning machine (ELM), extreme gradient boosting (XGBoost), and back propagation neural network (BPNN) were utilized to model the leaf moisture content. The results indicated that most vegetation indices exhibited higher correlation coefficients with soybean leaf moisture compared with texture features, while randomly extracted texture indices could enhance the correlation with soybean leaf moisture to some extent. RDTI, the random combination texture index, showed the highest correlation coefficient with leaf moisture at 0.683, with the texture combination being Variance1 and Correlation5. When combination 4 (combination of vegetation indices, texture features, and randomly extracted texture indices) was utilized as the input and the XGBoost model was employed for soybean leaf moisture monitoring, the highest level was achieved in this study. The coefficient of determination (R2) of the estimation model validation set reached 0.816, with a root-mean-square error (RMSE) of 1.404 and a mean relative error (MRE) of 1.934%. This study provides a foundation for UAV multispectral monitoring of soybean leaf moisture, offering valuable insights for rapid assessment of crop growth.

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Managing water deficit in wheat through mulches and irrigation techniques in semi-arid regions

Ahmad,

Raza,

Ali

et al. 2024

CEREAL RESEARCH COMMUNICATIONS

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Farmland mulching and optimized irrigation increase water productivity and seed yield by regulating functional parameters of soybean (Glycine max L.) leaves

Cited by 6 publications

References 59 publications

Estimation of Winter Wheat Chlorophyll Content Based on Wavelet Transform and the Optimal Spectral Index

Estimation of Winter Wheat Chlorophyll Content Based on Wavelet Transform and the Optimal Spectral Index

Soybean (Glycine max L.) Leaf Moisture Estimation Based on Multisource Unmanned Aerial Vehicle Image Feature Fusion

Managing water deficit in wheat through mulches and irrigation techniques in semi-arid regions

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