Improving Unmanned Aerial Vehicle Remote Sensing-Based Rice Nitrogen Nutrition Index Prediction with Machine Learning

Zha, Hainie; Miao, Yuxin; Wang, Tiantian; Li, Yue; Zhang, Jing; Sun, Wendong; Feng, Zhengqi; Kuśnierek, Krzysztof

doi:10.3390/rs12020215

Cited by 158 publications

(102 citation statements)

References 83 publications

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“…Through their study, it was demonstrated that data curation could be modeled by these algorithms from a few to a high number of samples and still achieve appropriate results. In comparison with the proposed approach, other machine learning frameworks also adopted similar sample sizes, like 324 leaf measurements that were used to model the water-stress response from lettuce [23], 189 hyperspectral observations that were used to model grapevine water status [63], and 266 observations that were used as training to predict nitrogen content in rice fields [64].…”

Section: Machine Learning Analysis and Hyperspectral Mappingmentioning

confidence: 99%

A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements

et al. 2020

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This paper presents a framework based on machine learning algorithms to predict nutrient content in leaf hyperspectral measurements. This is the first approach to evaluate macro- and micronutrient content with both machine learning and reflectance/first-derivative data. For this, citrus-leaves collected at a Valencia-orange orchard were used. Their spectral data was measured with a Fieldspec ASD FieldSpec® HandHeld 2 spectroradiometer and the surface reflectance and first-derivative spectra from the spectral range of 380 to 1020 nm (640 spectral bands) was evaluated. A total of 320 spectral signatures were collected, and the leaf-nutrient content (N, P, K, Mg, S, Cu, Fe, Mn, and Zn) was associated with them. For this, 204,800 (320 × 640) combinations were used. The following machine learning algorithms were used in this framework: k-Nearest Neighbor (kNN), Lasso Regression, Ridge Regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), and Random Forest (RF). The training methods were assessed based on Cross-Validation and Leave-One-Out. The Relief-F metric of the algorithms’ prediction was used to determine the most contributive wavelength or spectral region associated with each nutrient. This approach was able to return, with high predictions (R2), nutrients like N (0.912), Mg (0.832), Cu (0.861), Mn (0.898), and Zn (0.855), and, to a lesser extent, P (0.771), K (0.763), and S (0.727). These accuracies were obtained with different algorithms, but RF was the most suitable to model most of them. The results indicate that, for the Valencia-orange leaves, surface reflectance data is more suitable to predict macronutrients, while first-derivative spectra is better linked to micronutrients. A final contribution of this study is the identification of the wavelengths responsible for contributing to these predictions.

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Section: Machine Learning Analysis and Hyperspectral Mappingmentioning

confidence: 99%

A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements

et al. 2020

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“…The algorithms have the potential to model several types of datasets using linear and parametric and nonlinear and nonparametric approaches [12,27,28], including multispectral images [29]. Different machine learning algorithms like random forests (RF), decision trees (DT), artificial neural network (ANN), support vector machines (SVM), among many others, have been adopted to attend various applications in agriculture remote sensing [5,[30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Leaf Nitrogen Concentration and Plant Height Prediction for Maize Using UAV-Based Multispectral Imagery and Machine Learning Techniques

et al. 2020

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Under ideal conditions of nitrogen (N), maize (Zea mays L.) can grow to its full potential, reaching maximum plant height (PH). As a rapid and nondestructive approach, the analysis of unmanned aerial vehicles (UAV)-based imagery may be of assistance to estimate N and height. The main objective of this study is to present an approach to predict leaf nitrogen concentration (LNC, g kg−1) and PH (m) with machine learning techniques and UAV-based multispectral imagery in maize plants. An experiment with 11 maize cultivars under two rates of N fertilization was carried during the 2017/2018 and 2018/2019 crop seasons. The spectral vegetation indices (VI) normalized difference vegetation index (NDVI), normalized difference red-edge index (NDRE), green normalized difference vegetation (GNDVI), and the soil adjusted vegetation index (SAVI) were extracted from the images and, in a computational system, used alongside the spectral bands as input parameters for different machine learning models. A randomized 10-fold cross-validation strategy, with a total of 100 replicates, was used to evaluate the performance of 9 supervised machine learning (ML) models using the Pearson’s correlation coefficient (r), mean absolute error (MAE), coefficient of regression (R²), and root mean square error (RMSE) metrics. The results indicated that the random forest (RF) algorithm performed better, with r and RMSE, respectively, of 0.91 and 1.9 g.kg−¹ for LNC, and 0.86 and 0.17 m for PH. It was also demonstrated that VIs contributed more to the algorithm’s performances than individual spectral bands. This study concludes that the RF model is appropriate to predict both agronomic variables in maize and may help farmers to monitor their plants based upon their LNC and PH diagnosis and use this knowledge to improve their production rates in the subsequent seasons.

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“…Zheng et al compared different machine learning methods (such as random forest (RF), neural network, partial least-squares regression, and regression trees) for estimating N content in winter wheat leaf using UAV multispectral images, and found that the fast processing RF algorithm performed the best among the tested methods [16]. Zha et al evaluated five approaches (single VI, stepwise multiple linear regression, RF, support vector machine, and artificial neural networks) for estimating the rice plant N uptake and N nutrition index in Northeast China, and they concluded that RF machine-learning regression can significantly improve the estimation of rice N status through UAV RS [27]. However, these studies used algorithms to improve the estimation accuracy without considering the image itself.…”

Section: Introductionmentioning

confidence: 99%

Qualifications of Rice Growth Indicators Optimized at Different Growth Stages Using Unmanned Aerial Vehicle Digital Imagery

Qiu

Xiang

et al. 2020

Remote Sensing

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The accurate estimation of the key growth indicators of rice is conducive to rice production, and the rapid monitoring of these indicators can be achieved through remote sensing using the commercial RGB cameras of unmanned aerial vehicles (UAVs). However, the method of using UAV RGB images lacks an optimized model to achieve accurate qualifications of rice growth indicators. In this study, we established a correlation between the multi-stage vegetation indices (VIs) extracted from UAV imagery and the leaf dry biomass, leaf area index, and leaf total nitrogen for each growth stage of rice. Then, we used the optimal VI (OVI) method and object-oriented segmentation (OS) method to remove the noncanopy area of the image to improve the estimation accuracy. We selected the OVI and the models with the best correlation for each growth stage to establish a simple estimation model database. The results showed that the OVI and OS methods to remove the noncanopy area can improve the correlation between the key growth indicators and VI of rice. At the tillering stage and early jointing stage, the correlations between leaf dry biomass (LDB) and the Green Leaf Index (GLI) and Red Green Ratio Index (RGRI) were 0.829 and 0.881, respectively; at the early jointing stage and late jointing stage, the coefficient of determination (R2) between the Leaf Area Index (LAI) and Modified Green Red Vegetation Index (MGRVI) was 0.803 and 0.875, respectively; at the early stage and the filling stage, the correlations between the leaf total nitrogen (LTN) and UAV vegetation index and the Excess Red Vegetation Index (ExR) were 0.861 and 0.931, respectively. By using the simple estimation model database established using the UAV-based VI and the measured indicators at different growth stages, the rice growth indicators can be estimated for each stage. The proposed estimation model database for monitoring rice at the different growth stages is helpful for improving the estimation accuracy of the key rice growth indicators and accurately managing rice production.

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Improving Unmanned Aerial Vehicle Remote Sensing-Based Rice Nitrogen Nutrition Index Prediction with Machine Learning

Cited by 158 publications

References 83 publications

A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements

A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements

Leaf Nitrogen Concentration and Plant Height Prediction for Maize Using UAV-Based Multispectral Imagery and Machine Learning Techniques

Qualifications of Rice Growth Indicators Optimized at Different Growth Stages Using Unmanned Aerial Vehicle Digital Imagery

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