Combining Spectral and Texture Features of UAS-Based Multispectral Images for Maize Leaf Area Index Estimation

Zhang, Xuewei; Zhang, Kefei; Sun, Yuqing; Zhao, Yonghui; Zhuang, Huifu; Ban, Wei; Yu, Chen; Fu, Erjiang; Chen, Shuo; Liu, Jinxiang; Yang, Hao

doi:10.3390/rs14020331

Cited by 56 publications

(41 citation statements)

References 57 publications

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“…This study supports the notion that successful feature selection approaches aid in identifying the most important features or aspects that contributed to crop yield prediction. Our result was consistent with the study of (Fashoto et al 2021;Zhang et al 2022), who adopted backward elimination as a feature selection technique in estimating yield. The selection revealed that rainfall precipitation derived from CHIRPS satellite, some VIs such as EVI, GCVI, GNDVI, MSAVI, NDVI, SAVI, age and leaf N were found to be the most significant predictors (P< 0.05) in the yield prediction.…”

Section: Correlation Between Attributes and Yield With Backward Elimi...supporting

confidence: 92%

Oil Palm Yield Prediction Across Blocks Using Multi-Source Data and Machine Learning

Ang

Shafri

Lee³

et al. 2022

Preprint

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Predicting yields on a bigger scale in a timely and accurate manner is essential for preventing climate risk and ensuring food security, particularly in the light of climate change and the escalation of extreme climatic events. Furthermore, crop yield estimates are affected by various factors including weather, nutrients and management practices. In this study, integrating multi-source data (i.e. satellite-derived vegetation indices (VIs), satellite-derived climatic variables (i.e. land surface temperature (LST) and rainfall precipitation, weather station and field-surveys), we built one multiple linear regression (MLR), three machine learnings (XGBoost, support vector regression, and random forest) and one deep learning (deep neural network) model to predict oil palm yield at block-level within the oil palm plantation. Moreover, time-series moving average and backward elimination feature selection techniques were implemented at the pre-processing stage. The yield prediction models were developed and tested using MLR, XGBoost, support vector regression (SVR), random forest (RF) and deep neural network (DNN) algorithms. Their model performances were then compared using evaluation metrics and generated the final spatial prediction map based on the best performance. DNN achieved the best model performances for both selected (R2=0.91; RMSE= 2.92 tonnes per ha; MAE= 2.56 tonnes per ha and MAPE= 0.09) and full predictors (R2=0.76; RMSE of 3.03 tonnes per ha; MAE of 2.88 tonnes per ha; MAPE of 0.10 tonnes per ha). In addition, advanced ensemble ML techniques such as XGBoost may be utilised as a supplementary for oil palm yield prediction at the block level. Among them, MLR recorded the lowest performance. By using backward elimination to identify the most significant predictors, the performance of all models was improved by 5% - 26% for R2, and that decreased by 3% - 31% for RMSE, 7% - 34% for MAE, and 1% - 15% for MAPE, respectively. DNN generates the most accurate statistical metrics, with an increase of around 15% for R2, 11% for RMSE, 32% for MAE and 1% for MAPE. Our study successfully developed efficient, effective and accurate yield prediction models for timely predicting oil palm yield over a large area by integrating data from multiple sources. These can be potentially handled by plantation management to estimate oil palm yields to speed up the decision-making process for sustainable production.

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Section: Correlation Between Attributes and Yield With Backward Elimi...supporting

confidence: 92%

Oil Palm Yield Prediction Across Blocks Using Multi-Source Data and Machine Learning

Ang

Shafri

Lee³

et al. 2022

Preprint

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“… Zhou et al (2021) used drones to diagnose water stress in winter wheat. Zhang et al (2022) also introduced the combination of texture features and vegetation index to improve the model accuracy in the study of maize leaf area index estimation by UAV. Combined with the results of this study, it is feasible to improve the accuracy of cotton yield monitoring model by combining vegetation index and texture feature.…”

Section: Discussionmentioning

confidence: 99%

Cotton Yield Estimation Based on Vegetation Indices and Texture Features Derived From RGB Image

Zhang

et al. 2022

Front. Plant Sci.

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Yield monitoring is an important parameter to evaluate cotton productivity during cotton harvest. Nondestructive and accurate yield monitoring is of great significance to cotton production. Unmanned aerial vehicle (UAV) remote sensing has fast and repetitive acquisition ability. The visible vegetation indices has the advantages of low cost, small amount of calculation and high resolution. The combination of the UAV and visible vegetation indices has been more and more applied to crop yield monitoring. However, there are some shortcomings in estimating cotton yield based on visible vegetation indices only as the similarity between cotton and mulch film makes it difficult to differentiate them and yields may be saturated based on vegetation index estimates near harvest. Texture feature is another important remote sensing information that can provide geometric information of ground objects and enlarge the spatial information identification based on original image brightness. In this study, RGB images of cotton canopy were acquired by UAV carrying RGB sensors before cotton harvest. The visible vegetation indices and texture features were extracted from RGB images for cotton yield monitoring. Feature parameters were selected in different methods after extracting the information. Linear and nonlinear methods were used to build cotton yield monitoring models based on visible vegetation indices, texture features and their combinations. The results show that (1) vegetation indices and texture features extracted from the ultra-high-resolution RGB images obtained by UAVs were significantly correlated with the cotton yield; (2) The best model was that combined with vegetation indices and texture characteristics RF_ELM model, verification set R2 was 0.9109, and RMSE was 0.91277 t.ha−1. rRMSE was 29.34%. In conclusion, the research results prove that UAV carrying RGB sensor has a certain potential in cotton yield monitoring, which can provide theoretical basis and technical support for field cotton production evaluation.

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“…Stepwise MLR (SMLR) is a modeling method that eliminates covariance by removing unnecessary independent variables through AIC value minimization iterations and selecting significant independent variables to obtain the optimal regression model. The SMLR model has simple logic and clear physical meaning of the independent variables, indicating that it is an interpretable machine learning model ( Yu et al., 2016 ; Han et al., 2019 ; Zhang et al., 2022 ). Partial least-squares regression (PLSR) is one of the widely used machine learning methods that combines the basic functions of MLR, canonical correlation analysis, and principal component analysis.…”

Section: Methodsmentioning

confidence: 99%

Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery

et al. 2022

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Rapid and accurate assessment of yield and nitrogen use efficiency (NUE) is essential for growth monitoring, efficient utilization of fertilizer and precision management. This study explored the potential of a consumer-grade DJI Phantom 4 Multispectral (P4M) camera for yield or NUE assessment in winter wheat by using the universal vegetation indices independent of growth period. Three vegetation indices having a strong correlation with yield or NUE during the entire growth season were determined through Pearson’s correlational analysis, while multiple linear regression (MLR), stepwise MLR (SMLR), and partial least-squares regression (PLSR) methods based on the aforementioned vegetation indices were adopted during different growth periods. The cumulative results showed that the reciprocal ratio vegetation index (repRVI) had a high potential for yield assessment throughout the growing season, and the late grain-filling stage was deemed as the optimal single stage with R2, root mean square error (RMSE), and mean absolute error (MAE) of 0.85, 793.96 kg/ha, and 656.31 kg/ha, respectively. MERIS terrestrial chlorophyll index (MTCI) performed better in the vegetative period and provided the best prediction results for the N partial factor productivity (NPFP) at the jointing stage, with R2, RMSE, and MAE of 0.65, 10.53 kg yield/kg N, and 8.90 kg yield/kg N, respectively. At the same time, the modified normalized difference blue index (mNDblue) was more accurate during the reproductive period, providing the best accuracy for agronomical NUE (aNUE) assessment at the late grain-filling stage, with R2, RMSE, and MAE of 0.61, 7.48 kg yield/kg N, and 6.05 kg yield/kg N, respectively. Furthermore, the findings indicated that model accuracy cannot be improved by increasing the number of input features. Overall, these results indicate that the consumer-grade P4M camera is suitable for early and efficient monitoring of important crop traits, providing a cost-effective choice for the development of the precision agricultural system.

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Combining Spectral and Texture Features of UAS-Based Multispectral Images for Maize Leaf Area Index Estimation

Cited by 56 publications

References 57 publications

Oil Palm Yield Prediction Across Blocks Using Multi-Source Data and Machine Learning

Oil Palm Yield Prediction Across Blocks Using Multi-Source Data and Machine Learning

Cotton Yield Estimation Based on Vegetation Indices and Texture Features Derived From RGB Image

Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery

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