Estimating Crop LAI Using Spectral Feature Extraction and the Hybrid Inversion Method

Liang, Liang; Geng, Di; Yan, Jing; Qiu, Siyi; Di, Liping; Wang, Shuguo; Xu, Lu; Wang, Lijuan; Kang, Jianrong; Li, Li

doi:10.3390/rs12213534

Cited by 23 publications

(20 citation statements)

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“…Previously, several studies have exploited different machine learning models such as RR, SVM, RF, and GP to predict grain yield and physiological attributes of plants on remote sensing data sets [32][33][34][35][36]. This study aims to evaluate the combination of the above base learners to form an ensemble learning approach for grain yield prediction in different growth stages.…”

Section: Discussionmentioning

confidence: 99%

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Assessment of Ensemble Learning to Predict Wheat Grain Yield Based on UAV-Multispectral Reflectance

Fei

Hassan

et al. 2021

Remote Sensing

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Grain yield is increasingly affected by climate factors such as drought and heat. To develop resilient and high-yielding cultivars, high-throughput phenotyping (HTP) techniques are essential for precise decisions in wheat breeding. The ability of unmanned aerial vehicle (UAV)-based multispectral imaging and ensemble learning methods to increase the accuracy of grain yield prediction in practical breeding work is evaluated in this study. For this, 211 winter wheat genotypes were planted under full and limited irrigation treatments, and multispectral data were collected at heading, flowering, early grain filling (EGF), and mid-grain filling (MGF) stages. Twenty multispectral vegetation indices (VIs) were estimated, and VIs with heritability greater than 0.5 were selected to evaluate the models across the growth stages under both irrigation treatments. A framework for ensemble learning was developed by combining multiple base models such as random forest (RF), support vector machine (SVM), Gaussian process (GP), and ridge regression (RR). The R2 values between VIs and grain yield between for individual base models were ranged from 0.468 to 0.580 and 0.537 to 0.598 for grain yield prediction in full and limited irrigation treatments across growth stages, respectively. The prediction results of ensemble models were ranged from 0.491 to 0.616 and 0.560 to 0.616 under full and limited irrigation treatments respectively, and were higher than that of the corresponding base learners. Moreover, the grain yield prediction results were observed high at mid grain filling stage under both full (R2 = 0.625) and limited (R2 = 0.628) irrigation treatments through ensemble learning based stacking of four base learners. Further improvements in ensemble learning models can accelerate the use of UAV-based multispectral data for accurate predictions of complex traits like grain yield in wheat.

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Section: Discussionmentioning

confidence: 99%

“…These models have greatly improved the prediction accuracy for many traits in various plants [28][29][30][31]. For example, RF was successfully applied to evaluate chlorophyll [32], biomass [28,29], and LAI [33] in winter wheat. While SVM and RR showed high prediction accuracy and robustness for predicting wheat and soybean yield [11,34].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Ensemble Learning to Predict Wheat Grain Yield Based on UAV-Multispectral Reflectance

Fei

Hassan

et al. 2021

Remote Sensing

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“…The random forest regression (RFR) [30,31] is an integrated classifier composed of multiple decision trees based on bagging. There is no correlation between the decision trees.…”

Section: Rfr Modelmentioning

confidence: 99%

Non-Destructive Detection of pH Value of Kiwifruit Based on Hyperspectral Fluorescence Imaging Technology

Wang

Chen

et al. 2022

Agriculture

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Non-destructive detection of the pH value of kiwifruit has important practical significance for its quality classification. In this study, hyperspectral fluorescence imaging technology was proposed to quantitatively predict the pH value of kiwifruit non-destructively. Firstly, the SPXY algorithm was used to divide samples into training and prediction sets and three different algorithms were used to preprocess the raw spectral data. Secondly, algorithms such as the iteratively retaining information variables (IRIV), the variable iterative space shrinkage approach (VISSA), the model adaptive space shrinkage (MASS), the random frog (RF), and their combination (i.e., IRIV + VISSA + MASS + RF, IVMR) were used to extract effective variables from the preprocessed spectral data. Moreover, the second extractions, such as IRIV-VISSA and IRIV-MASS, and the third extraction (i.e., IVMR-VISSA-IRIV) were used to further reduce the redundant variables. Based on the effective variables, four regression models—random forest (RF), partial least square (PLSR), extreme learning machines (ELM), and multiple-kernel support vector regression (MK-SVR)—were built and compared for predicting. The results show that IVMR-VISSA-IRIV-MK-SVR had the best prediction results, with RP2, RC2 and RPD of 0.8512, 0.8580, and 2.66, respectively, which verifies that hyperspectral fluorescence imaging technology is reliable for predicting the pH value of kiwifruit non-destructively.

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“…The RTM inversions were developed by running the PROSAIL model 5000 times to generate synthetic reflectance data for a range of possible combinations of rice canopy, leaf and soil properties. The canopy, leaf and soil parameters used within the PROSAIL RTM simulations were drawn from a truncated normal distribution (TND) so that their values followed a normal distribution and not fall below zero at the same time (except for the solar zenith angle and relative azimuth angle; Table 1) following parameter ranges reported in previous applications for rice LAI estimation [45,46]. We used the simulated reflectance data to develop statistical models between the spectral bands collected by Sentinel-2 and Landsat-8 to LAI using a procedure similar to the RF approach described previously in Section 2.3.…”

Section: Validation Of Statistical Yield Modelsmentioning

confidence: 99%

Improving the Performance of Index Insurance Using Crop Models and Phenological Monitoring

et al. 2021

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Extreme weather events cause considerable damage to the livelihoods of smallholder farmers globally. Whilst index insurance can help farmers cope with the financial consequences of extreme weather, a major challenge for index insurance is basis risk, where insurance payouts correlate poorly with actual crop losses. We analyse to what extent the use of crop simulation models and crop phenology monitoring can reduce basis risk in index insurance. Using a biophysical process-based crop model (Agricultural Production System sIMulator (APSIM)) applied for rice producers in Odisha, India, we simulate a synthetic yield dataset to train non-parametric statistical models to predict rice yields as a function of meteorological and phenological conditions. We find that the performance of statistical yield models depends on whether meteorological or phenological conditions are used as predictors and whether one aggregates these predictors by season or crop growth stage. Validating the preferred statistical model with observed yield data, we find that the model explains around 54% of the variance in rice yields at the village cluster (Gram Panchayat) level, outperforming vegetation index-based models that were trained directly on the observed yield data. Our methods and findings can guide efforts to design smart phenology-based index insurance and target yield monitoring resources in smallholder farming environments.

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Estimating Crop LAI Using Spectral Feature Extraction and the Hybrid Inversion Method

Cited by 23 publications

References 100 publications

Assessment of Ensemble Learning to Predict Wheat Grain Yield Based on UAV-Multispectral Reflectance

Assessment of Ensemble Learning to Predict Wheat Grain Yield Based on UAV-Multispectral Reflectance

Non-Destructive Detection of pH Value of Kiwifruit Based on Hyperspectral Fluorescence Imaging Technology

Improving the Performance of Index Insurance Using Crop Models and Phenological Monitoring

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