Estimation of nitrogen nutrition index in rice from UAV RGB images coupled with machine learning algorithms

Qiu, Zhengchao; Ma, Fang; Li, Zhenwang; Xu, Xuebin; Ge, Haixiao; Du, Changwen

doi:10.1016/j.compag.2021.106421

Cited by 66 publications

(34 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A gray gradient calibration panel, having 10 calibration plates with different gray gradients, was placed on the ground while the UAV was flying, for reflectivity correction [31]. The reflectance of these plates were spectrally measured in situ using an Analytical Spectral Device (ASD) FieldSpec ® 3 Full-Range spectroradiometer (Analytical Spectral Devices, Inc.; Boulder, CO, USA).…”

Section: Experimental Site and Experimental Designmentioning

confidence: 99%

UAV- and Machine Learning-Based Retrieval of Wheat SPAD Values at the Overwintering Stage for Variety Screening

et al. 2021

View full text Add to dashboard Cite

In recent years, the delay in sowing has become a major obstacle to high wheat yield in Jiangsu Province, one of the major wheat producing areas in China; hence, it is necessary to screen wheat varieties are resilient for late sowing. This study aimed to provide an effective, fast, and non-destructive monitoring method of soil plant analysis development (SPAD) values, which can represent leaf chlorophyll contents, for late-sown winter wheat variety screening. This study acquired multispectral images using an unmanned aerial vehicle (UAV) at the overwintering stage of winter wheat growth, and further processed these images to extract reflectance of five single spectral bands and calculated 26 spectral vegetation indices. Based on these 31 variables, this study combined three variable selection methods (i.e., recursive feature elimination (RFE), random forest (RF), and Pearson correlation coefficient (r)) with four machine learning algorithms (i.e., random forest regression (RFR), linear kernel-based support vector regression (SVR), radial basis function (RBF) kernel-based SVR, and sigmoid kernel-based SVR), resulted in seven SVR models (i.e., RFE-SVR_linear, RF-SVR_linear, RF-SVR_RBF, RF-SVR_sigmoid, r-SVR_linear, r-SVR_RBF, and r-SVR_sigmoid) and three RFR models (i.e., RFE-RFR, RF-RFR, and r-RFR). The performances of the 10 machine learning models were evaluated and compared with each other according to the achieved coefficient of determination (R2), residual prediction deviation (RPD), root mean square error (RMSE), and relative RMSE (RRMSE) in SPAD estimation. Of the 10 models, the best one was the RF-SVR_sigmoid model, which was the combination of the RF variable selection method and the sigmoid kernel-based SVR algorithm. It achieved high accuracy in estimating SPAD values of the wheat canopy (R2 = 0.754, RPD = 2.017, RMSE = 1.716 and RRMSE = 4.504%). The newly developed UAV- and machine learning-based model provided a promising and real time method to monitor chlorophyll contents at the overwintering stage, which can benefit late-sown winter wheat variety screening.

show abstract

Section: Experimental Site and Experimental Designmentioning

confidence: 99%

UAV- and Machine Learning-Based Retrieval of Wheat SPAD Values at the Overwintering Stage for Variety Screening

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In the recent research [8,14,24,[83][84][85], remote sensing methods for assessing precision agriculture and fertilization are divided into two different approaches:…”

Section: Modern Remote Sensing Methods For Optimal Fertilization In P...mentioning

confidence: 99%

The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilization in Precision Agriculture

2022

View full text Add to dashboard Cite

The precision fertilization system is the basis for upgrading conventional intensive agricultural production, while achieving both high and quality yields and minimizing the negative impacts on the environment. This research aims to present the application of both conventional and modern prediction methods in precision fertilization by integrating agronomic components with the spatial component of interpolation and machine learning. While conventional methods were a cornerstone of soil prediction in the past decades, new challenges to process larger and more complex data have reduced their viability in the present. Their disadvantages of lower prediction accuracy, lack of robustness regarding the properties of input soil sample values and requirements for extensive cost- and time-expensive soil sampling were addressed. Specific conventional (ordinary kriging, inverse distance weighted) and modern machine learning methods (random forest, support vector machine, artificial neural networks, decision trees) were evaluated according to their popularity in relevant studies indexed in the Web of Science Core Collection over the past decade. As a shift towards increased prediction accuracy and computational efficiency, an overview of state-of-the-art remote sensing methods for improving precise fertilization was completed, with the accent on open-data and global satellite missions. State-of-the-art remote sensing techniques allowed hybrid interpolation to predict the sampled data supported by remote sensing data such as high-resolution multispectral, thermal and radar satellite or unmanned aerial vehicle (UAV)-based imagery in the analyzed studies. The representative overview of conventional and modern approaches to precision fertilization was performed based on 121 samples with phosphorous pentoxide (P2O5) and potassium oxide (K2O) in a common agricultural parcel in Croatia. It visually and quantitatively confirmed the superior prediction accuracy and retained local heterogeneity of the modern approach. The research concludes that remote sensing data and methods have a significant role in improving fertilization in precision agriculture today and will be increasingly important in the future.

show abstract

“…In the research [15], simple nonlinear regression (SNR), backpropagation neural network (BPNN), and random forest (RF) regression were used to determine the rice N nutrition status with RGB images. In work [16] and [17], the author used support vector machine (SVM), multiple linear regression (SMLR), and Artificial Neural Networks (ANNs) to estimate rice nitrogen nutrition index with UAV RGB images. The work [36] used ANNs and RF to predict the biotic stress of winter wheat.…”

Section: A Digital Imaging For Crop N Estimationmentioning

confidence: 99%

“…Machine learning has shown the effectiveness of solving nonlinear problems from multiple sources [14]. Several machine learning-based methods such as random forest (RF) [15], support vector machine (SVM) [16], and Artificial Neural Networks (ANNs) [17] have been applied to estimate crop stress or N status with satisfactory results [18], [19]. However, these aforementioned methods typically focus on color and channel information.…”

mentioning

confidence: 99%

The self-supervised spectral-spatial attention-based transformer network for automated, accurate prediction of crop nitrogen status from UAV imagery

Zhang¹,

Han²,

Sobeih³

et al. 2021

Preprint

View full text Add to dashboard Cite

Nitrogen (N) fertiliser is routinely applied by farmers to increase crop yields. At present, farmers often over-apply N fertilizer in some locations or timepoints because they do not have high-resolution crop N status data. N-use efficiency can be low, with the remaining N lost to the environment, resulting in high production costs and environmental pollution. Accurate and timely estimation of N status in crops is crucial to improving cropping systems' economic and environmental sustainability. The conventional approaches based on tissue analysis in the laboratory for estimating N status in plants are time consuming and destructive. Recent advances in remote sensing and machine learning have shown promise in addressing the aforementioned challenges in a non-destructive way. We propose a novel deep learning framework: a channel-spatial attention-based vision transformer (CSVT) for estimating crop N status from large images collected from a UAV in a wheat field. Unlike the existing works, the proposed CSVT introduces a Channel Attention Block (CAB) and a Spatial Interaction Block (SIB), which allows capturing nonlinear characteristics of spatial-wise and channel-wise features from UAV digital aerial imagery, for accurate N status prediction in wheat crops. Moreover, since acquiring labeled data is time consuming and costly, local-to-global self-supervised learning is introduced to pre-train the CSVT with extensive unlabelled data. The proposed CSVT has been compared with the state-of-the-art models, tested and validated on both testing and independent datasets. The proposed approach achieved high accuracy (0.96) with good generalizability and reproducibility for wheat N status estimation.

show abstract

Estimation of nitrogen nutrition index in rice from UAV RGB images coupled with machine learning algorithms

Cited by 66 publications

References 28 publications

UAV- and Machine Learning-Based Retrieval of Wheat SPAD Values at the Overwintering Stage for Variety Screening

UAV- and Machine Learning-Based Retrieval of Wheat SPAD Values at the Overwintering Stage for Variety Screening

The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilization in Precision Agriculture

The self-supervised spectral-spatial attention-based transformer network for automated, accurate prediction of crop nitrogen status from UAV imagery

Contact Info

Product

Resources

About