Accurate estimation of sorghum crop water content under different water stress levels using machine learning and hyperspectral data

Tunca, Emre; Köksal, Eyüp Seli̇m; Öztürk, Elif; Akay, Hasan; Taner, Sakine Çetin

doi:10.1007/s10661-023-11536-8

Cited by 6 publications

(8 citation statements)

References 67 publications

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“…With the presented data, RF is superior for producing consistently accurate yield estimates that produce high adjusted R 2 values when regressed with yield collected in the field. These findings contrast with Tunca et al. (2023) , where XGBoost was shown to be slightly more effective at predicting sorghum crop water content than RF.…”

Section: Discussioncontrasting

confidence: 68%

“…In this case, RFE was used to select features that were potentially useful for estimating final yield. To determine which configuration of latent phenotypes could best predict final yields, various limits of maximum selected features were specified from 1 – 30 and the various resulting feature configurations were tested in the ML scripts ( Tunca et al., 2023 ). This iterative process ensured that an effective model could be built while minimizing complexity ( Demir and Sahin, 2023 ).…”

Section: Methodsmentioning

confidence: 99%

“…These yield predictions were subjected to regression analysis against the empirically measured yield from the field. This analytical step was important to gauge the precision and accuracy of the model's predictions across the entirety of the dataset, beyond the confines of merely the test and training subsets, as demonstrated by Tunca et al (2023). Consequently, the regression R 2 was anticipated to have a value intermediary to the test and training metrics (Tunca et al, 2023).…”

Section: Data Analysis Machine Learning and Statisticsmentioning

confidence: 99%

“…This analytical step was important to gauge the precision and accuracy of the model's predictions across the entirety of the dataset, beyond the confines of merely the test and training subsets, as demonstrated by Tunca et al (2023). Consequently, the regression R 2 was anticipated to have a value intermediary to the test and training metrics (Tunca et al, 2023). Regressions were also performed within each year of the study to determine model consistency, since years with vastly different mean yields and levels of variance could lead to inflation of coefficients of determination across years, i.e., two general "groupings" of the data resulting from highly variable environments could serve to anchor one another and artificially increase linear regression R 2 values.…”

Section: Data Analysis Machine Learning and Statisticsmentioning

confidence: 99%

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Yield prediction in a peanut breeding program using remote sensing data and machine learning algorithms

Pugh,

Young,

Ojha

et al. 2024

Front. Plant Sci.

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Peanut is a critical food crop worldwide, and the development of high-throughput phenotyping techniques is essential for enhancing the crop’s genetic gain rate. Given the obvious challenges of directly estimating peanut yields through remote sensing, an approach that utilizes above-ground phenotypes to estimate underground yield is necessary. To that end, this study leveraged unmanned aerial vehicles (UAVs) for high-throughput phenotyping of surface traits in peanut. Using a diverse set of peanut germplasm planted in 2021 and 2022, UAV flight missions were repeatedly conducted to capture image data that were used to construct high-resolution multitemporal sigmoidal growth curves based on apparent characteristics, such as canopy cover and canopy height. Latent phenotypes extracted from these growth curves and their first derivatives informed the development of advanced machine learning models, specifically random forest and eXtreme Gradient Boosting (XGBoost), to estimate yield in the peanut plots. The random forest model exhibited exceptional predictive accuracy (R2 = 0.93), while XGBoost was also reasonably effective (R2 = 0.88). When using confusion matrices to evaluate the classification abilities of each model, the two models proved valuable in a breeding pipeline, particularly for filtering out underperforming genotypes. In addition, the random forest model excelled in identifying top-performing material while minimizing Type I and Type II errors. Overall, these findings underscore the potential of machine learning models, especially random forests and XGBoost, in predicting peanut yield and improving the efficiency of peanut breeding programs.

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Section: Discussioncontrasting

confidence: 68%

Section: Methodsmentioning

confidence: 99%

Section: Data Analysis Machine Learning and Statisticsmentioning

confidence: 99%

Section: Data Analysis Machine Learning and Statisticsmentioning

confidence: 99%

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Yield prediction in a peanut breeding program using remote sensing data and machine learning algorithms

Pugh,

Young,

Ojha

et al. 2024

Front. Plant Sci.

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“…RFE, an adaptive feature selection algorithm, iteratively removes the least important feature variables and ranks feature importance until the optimal feature subset is identified. This approach minimizes the effects of random fluctuations and interference information ( Tunca et al., 2023 ). Yoosefzadeh-Najafabadi et al.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the rice aboveground biomass based on the first derivative spectrum and Boruta algorithm

Nian,

Su,

Yue

et al. 2024

Front. Plant Sci.

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Aboveground biomass (AGB) is regarded as a critical variable in monitoring crop growth and yield. The use of hyperspectral remote sensing has emerged as a viable method for the rapid and precise monitoring of AGB. Due to the extensive dimensionality and volume of hyperspectral data, it is crucial to effectively reduce data dimensionality and select sensitive spectral features to enhance the accuracy of rice AGB estimation models. At present, derivative transform and feature selection algorithms have become important means to solve this problem. However, few studies have systematically evaluated the impact of derivative spectrum combined with feature selection algorithm on rice AGB estimation. To this end, at the Xiaogang Village (Chuzhou City, China) Experimental Base in 2020, this study used an ASD FieldSpec handheld 2 ground spectrometer (Analytical Spectroscopy Devices, Boulder, Colorado, USA) to obtain canopy spectral data at the critical growth stage (tillering, jointing, booting, heading, and maturity stages) of rice, and evaluated the performance of the recursive feature elimination (RFE) and Boruta feature selection algorithm through partial least squares regression (PLSR), principal component regression (PCR), support vector machine (SVM) and ridge regression (RR). Moreover, we analyzed the importance of the optimal derivative spectrum. The findings indicate that (1) as the growth stage progresses, the correlation between rice canopy spectrum and AGB shows a trend from high to low, among which the first derivative spectrum (FD) has the strongest correlation with AGB. (2) The number of feature bands selected by the Boruta algorithm is 19~35, which has a good dimensionality reduction effect. (3) The combination of FD-Boruta-PCR (FB-PCR) demonstrated the best performance in estimating rice AGB, with an increase in R² of approximately 10% ~ 20% and a decrease in RMSE of approximately 0.08% ~ 14%. (4) The best estimation stage is the booting stage, with R2 values between 0.60 and 0.74 and RMSE values between 1288.23 and 1554.82 kg/hm2. This study confirms the accuracy of hyperspectral remote sensing in estimating vegetation biomass and further explores the theoretical foundation and future direction for monitoring rice growth dynamics.

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Comparison of CWSI and Ts-Ta-VIs in moisture monitoring of dryland crops (sorghum and maize) based on UAV remote sensing

Chen,

Zhang

et al. 2024

Journal of Integrative Agriculture

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Accurate estimation of sorghum crop water content under different water stress levels using machine learning and hyperspectral data

Cited by 6 publications

References 67 publications

Yield prediction in a peanut breeding program using remote sensing data and machine learning algorithms

Yield prediction in a peanut breeding program using remote sensing data and machine learning algorithms

Estimation of the rice aboveground biomass based on the first derivative spectrum and Boruta algorithm

Comparison of CWSI and Ts-Ta-VIs in moisture monitoring of dryland crops (sorghum and maize) based on UAV remote sensing

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