Comparison of various modelling approaches for water deficit stress monitoring in rice crop through hyperspectral remote sensing

Krishna, Gopal; Sahoo, Rabi Narayan; Singh, Pratika; Bajpai, Vaishangi; Patra, Himesh; Kumar, Sudhir; Dandapani, Raju; Gupta, Vinod Kumar; Chinnusamy, Viswanathan; Ahmad, Tauqueer; Sahoo, Prachi Misra

doi:10.1016/j.agwat.2018.08.029

Cited by 82 publications

(49 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because of this, many studies focusing on phytosanitary problems are based on spectroscopy. These problems include nutritional deficiencies [5][6][7]; diseases [8], biomass [9], and, as in the present study, water stress [10].…”

Section: Introductionsupporting

confidence: 52%

See 1 more Smart Citation

Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks

et al. 2019

View full text Add to dashboard Cite

Modeling the hyperspectral response of vegetables is important for estimating water stress through a noninvasive approach. This article evaluates the hyperspectral response of water-stress induced lettuce (Lactuca sativa L.) using artificial neural networks (ANN). We evenly split 36 lettuce pots into three groups: control, stress, and bacteria. Hyperspectral response was measured four times, during 14 days of stress induction, with an ASD Fieldspec HandHeld spectroradiometer (325–1075 nm). Both reflectance and absorbance measurements were calculated. Different biophysical parameters were also evaluated. The performance of the ANN approach was compared against other machine learning algorithms. Our results show that the ANN approach could separate the water-stressed lettuce from the non-stressed group with up to 80% accuracy at the beginning of the experiment. Additionally, this accuracy improved at the end of the experiment, reaching an accuracy of up to 93%. Absorbance data offered better accuracy than reflectance data to model it. This study demonstrated that it is possible to detect early stages of water stress in lettuce plants with high accuracy based on an ANN approach applied to hyperspectral data. The methodology has the potential to be applied to other species and cultivars in agricultural fields.

show abstract

Section: Introductionsupporting

confidence: 52%

“…The alteration of these components results in the appearance of visual symptoms, but they are difficult to identify due to their similarity to other problems, such as diseases, malnutrition, and cold damage [14]. An alternative that can identify changes caused by water stress alone is hyperspectral analysis [10,11].…”

Section: Introductionmentioning

confidence: 99%

Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Algorithms like artificial neural networks (ANN), support vector machine (SVM), decision trees (DT), random forests (RF), and others are powerful tools in assisting in UAV-based image analysis [19]. These algorithms performed quite well in current approaches involving plant conditions such as nutritional status [20], water-quantity [21], biomass [19], and chlorophyll content [22]. These studies have also considered the contribution of individual bands and spectral vegetation indices in their evaluations.…”

Section: Introductionmentioning

confidence: 99%

Predicting Canopy Nitrogen Content in Citrus-Trees Using Random Forest Algorithm Associated to Spectral Vegetation Indices from UAV-Imagery

et al. 2019

View full text Add to dashboard Cite

The traditional method of measuring nitrogen content in plants is a time-consuming and labor-intensive task. Spectral vegetation indices extracted from unmanned aerial vehicle (UAV) images and machine learning algorithms have been proved effective in assisting nutritional analysis in plants. Still, this analysis has not considered the combination of spectral indices and machine learning algorithms to predict nitrogen in tree-canopy structures. This paper proposes a new framework to infer the nitrogen content in citrus-tree at a canopy-level using spectral vegetation indices processed with the random forest algorithm. A total of 33 spectral indices were estimated from multispectral images acquired with a UAV-based sensor. Leaf samples were gathered from different planting-fields and the leaf nitrogen content (LNC) was measured in the laboratory, and later converted into the canopy nitrogen content (CNC). To evaluate the robustness of the proposed framework, we compared it with other machine learning algorithms. We used 33,600 citrus trees to evaluate the performance of the machine learning models. The random forest algorithm had higher performance in predicting CNC than all models tested, reaching an R2 of 0.90, MAE of 0.341 g·kg−1 and MSE of 0.307 g·kg−1. We demonstrated that our approach is able to reduce the need for chemical analysis of the leaf tissue and optimizes citrus orchard CNC monitoring.

show abstract

“…RS allows collecting information about crop production using non-destructive methods [12] on a large scale for many fields at the same time. Hyperspectral (HS) RS provides continuous narrow spectral data from 400 to 2500 nm and have been proved to capture the variations in spectral response of the crop for the detection of nitrogen (N) content [13,14], biomass [15] and water stress [6,16]. Development of HS sensors and their application in estimating crop biomass from multi-year data [17] has gained increasing attention in the recent years.…”

mentioning

confidence: 99%

Multi-Temporal Monsoon Crop Biomass Estimation Using Hyperspectral Imaging

et al. 2019

View full text Add to dashboard Cite

Hyperspectral remote sensing is considered to be an effective tool in crop monitoring and estimation of biomass. Many of the previous approaches are from single year or single date measurements, even though the complete crop growth with multiple years would be required for an appropriate estimation of biomass. The aim of this study was to estimate the fresh matter biomass (FMB) by terrestrial hyperspectral imaging of the three crops (lablab, maize and finger millet) under different levels of nitrogen fertiliser and water supply. Further, the importance of the different spectral regions for the estimation of FMB was assessed. The study was conducted in two experimental layouts (rainfed (R) and irrigated (I)) at the University of Agricultural Sciences, Bengaluru, India. Spectral images and the FMB were collected over three years (2016)(2017)(2018) during the growing season of the crops. Random forest regression method was applied to build FMB models. R 2 validation (R 2 val ) and relative root mean square error prediction (rRMSEP) was used to evaluate the FMB models. The Generalised model (combination of R and I data) performed better for lablab (R 2 val = 0.53, rRMSEP = 13.9%), maize (R 2 val = 0.53, rRMSEP = 18.7%) and finger millet (R 2 val = 0.46, rRMSEP = 18%) than the separate FMB models for R and I. In the best derived model, the most important variables contributing to the estimation of biomass were in the wavelength ranges of 546-910 nm (lablab), 750-794 nm (maize) and 686-814 nm (finger millet). The deviation of predicted and measured FMB did not differ much among the different levels of N and water supply. However, there was a trend of overestimation at the initial stage and underestimation at the later stages of crop growth.to local water availability [2]. Irrigated crop production is a major contributor to the green revolution, which has enabled the country to be self-sufficient [3], accompanied by fertiliser application and other inputs in semi-arid parts of India. Timely fertiliser application with water supply is essential for a successful crop. Spectral data from Remote Sensing (RS) have been studied for many years for an adequate assessment of nutrient and water variability for yield optimisation [4][5][6] RS can be an effective tool in monitoring crop production [7-9] and estimating yield [10,11]. Early estimation of yield may allow better planning and forecasting the market prices and support food security based on the regional, national and global demand and supply. RS allows collecting information about crop production using non-destructive methods [12] on a large scale for many fields at the same time. Hyperspectral (HS) RS provides continuous narrow spectral data from 400 to 2500 nm and have been proved to capture the variations in spectral response of the crop for the detection of nitrogen (N) content [13,14], biomass [15] and water stress [6,16]. Development of HS sensors and their application in estimating crop biomass from multi-year data [17] has gained increasing attention in the recen...

show abstract

Comparison of various modelling approaches for water deficit stress monitoring in rice crop through hyperspectral remote sensing

Cited by 82 publications

References 64 publications

Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks

Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks

Predicting Canopy Nitrogen Content in Citrus-Trees Using Random Forest Algorithm Associated to Spectral Vegetation Indices from UAV-Imagery

Multi-Temporal Monsoon Crop Biomass Estimation Using Hyperspectral Imaging

Contact Info

Product

Resources

About