Yield Prediction in Wheat (&lt;i&gt;Triticum aestivum&lt;/i&gt; L.) using Spectral Reflectance Indices

Chandel, Narendra Singh; Tiwari, P.S.; Singh, K. P.; Jat, Dilip; Gaikwad, Bhaskar; Tripathi, H. P.; Golhani, Kamlesh

doi:10.18520/cs/v116/i2/272-278

Cited by 21 publications

(10 citation statements)

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“…Regarding the relationship between N content and NDVI, the present results corroborated previous findings, as a study carried out on wheat that showed correlation between NDVI measured in different growth stages and grain properties, thereby revealing high relationships (R 2 > 0.90 at heading stage) for both grain/biomass yield and N content [75].…”

Section: Possible Use Of Ndvi Measurements To Predict Plant Grain Wesupporting

confidence: 91%

A Sustainability Assessment of the Greenseeker N Management Tool: A Lysimetric Experiment on Barley

et al. 2020

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A preliminary study was conducted to analyze the sustainability of barley production through: (i) investigating sensor-based nitrogen (N) application on barley performance, compared with conventional N management (CT); (ii) assessing the potential of the Normalized Difference Vegetation Index (NDVI) at different growth stages for within-season predictions of crop parameters; and (iii) evaluating sensor-based fertilization benefits in the form of greenhouse gasses mitigation. Barley was grown under CT, sensor-based management (RF) and with no N fertilization (Control). NDVI measurements and RF fertilization were performed using a GreenSeeker™ 505 hand-held optical sensor. Gas emissions were measured using a static chamber method with a portable gas analyzer. Results showed that barley yield was not statistically different under RF and CF, while they both differed significantly from Control. Highly significant positive correlations were observed between NDVI and production parameters at harvesting from the middle of stem elongation to the medium milk stage across treatments. Our findings suggest that RF is able to decrease CO2 emission in comparison with CF. The relationship between N fertilization and CH4 emission showed high variability. These preliminary results provide an indication of the benefits achieved using a simple proximal sensing methodology to support N fertilization.

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Section: Possible Use Of Ndvi Measurements To Predict Plant Grain Wesupporting

confidence: 91%

A Sustainability Assessment of the Greenseeker N Management Tool: A Lysimetric Experiment on Barley

et al. 2020

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“…The use of hyperspectral data to construct yield estimation models has been widely used in previous yield estimation studies, and all have achieved high model accuracy, consistent with the findings of this paper [105,106]. For example, Chandel et al (2019) [107] used hyperspectral indices to construct a yield prediction regression model and found that the yield of irrigated wheat was estimated with an accuracy of 96%. However, relying on hyperspectral data alone for yield estimation still has some limitations.…”

Section: Discussionsupporting

confidence: 78%

UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

et al. 2022

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Winter wheat is a widely-grown cereal crop worldwide. Using growth-stage information to estimate winter wheat yields in a timely manner is essential for accurate crop management and rapid decision-making in sustainable agriculture, and to increase productivity while reducing environmental impact. UAV remote sensing is widely used in precision agriculture due to its flexibility and increased spatial and spectral resolution. Hyperspectral data are used to model crop traits because of their ability to provide continuous rich spectral information and higher spectral fidelity. In this study, hyperspectral image data of the winter wheat crop canopy at the flowering and grain-filling stages was acquired by a low-altitude unmanned aerial vehicle (UAV), and machine learning was used to predict winter wheat yields. Specifically, a large number of spectral indices were extracted from the spectral data, and three feature selection methods, recursive feature elimination (RFE), Boruta feature selection, and the Pearson correlation coefficient (PCC), were used to filter high spectral indices in order to reduce the dimensionality of the data. Four major basic learner models, (1) support vector machine (SVM), (2) Gaussian process (GP), (3) linear ridge regression (LRR), and (4) random forest (RF), were also constructed, and an ensemble machine learning model was developed by combining the four base learner models. The results showed that the SVM yield prediction model, constructed on the basis of the preferred features, performed the best among the base learner models, with an R2 between 0.62 and 0.73. The accuracy of the proposed ensemble learner model was higher than that of each base learner model; moreover, the R2 (0.78) for the yield prediction model based on Boruta’s preferred characteristics was the highest at the grain-filling stage.

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“…The relationship of NDVI with grain and biomass yields was stronger at the heading stage (r=0.98 and R 2 =0.93) in irrigated wheat. Similar relationship of grain and biomass yields was also observed for NDWI (Chandel et al, 2019).…”

Section: Regression Models For Prediction Of Grain Yieldsupporting

confidence: 80%

Assessing the Maize (Zea mays L.) Crop Performance Using Spectral Indices under Different Sowing Dates and Irrigations Schedules

Pallavi¹,

Sreenivas²,

Yakadri³

et al. 2021

IJBSM

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Field experiment was conducted at ARI, PJTSAU, Hyderabad with four dates of sowing (18th June, 04th July, 19th July and 03rd August in Kharif, 2016 and 01st November, 18th November, 01st December and 17th December in rabi, 2016–17) as main plots and four irrigations regimes (Control, 0.4 IW/CPE, 0.6 IW/CPE and 0.8 IW/CPE in kharif and 0.4 IW/CPE, 0.6 IW/CPE, 0.8 IW/CPE and 1.0 IW/CPE in rabi) as sub-plots in split plot design replicated thrice. Scheduling irrigation at unstressed conditions, 0.8 IW/CPE (I3) and 1.0 IW/CPE (I4) of maize resulted in low reflectance in visible region (400 to 700 nm) and mid infrared (MIR) region (1350–2500 nm) and high in near infrared (NIR) region (700 to 1350 nm) during kharif and rabi respectively. While, under stressed condition, the reflectance was high in visible and MIR region and low in NIR region in rainfed and 0.4 IW/CPE (I0) respectively in kharif and rabi. Significantly higher drymatter, LAI and grain yield was observed in 04th July (D2) and 01st November (D1) sown crop in kharif and rabi respectively. However, spectral indices (SR, PRI, NDWI at 1240, 1640 and 2130 nm, NDII, NMDI, WBI and SWRI) was attained by 18th June (D1) and 01st November (D1) during kharif and rabi respectively. Higher drymatter, LAI, grain yield and spectral indices was recorded with I3 (0.8 IW/CPE) and I4 (1.0 IW/CPE) in kharif and rabi respectively. All the spectral vegetation indices correlated positively with LAI, drymatter and grain yield.

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Yield Prediction in Wheat (<i>Triticum aestivum</i> L.) using Spectral Reflectance Indices

Cited by 21 publications

References 0 publications

A Sustainability Assessment of the Greenseeker N Management Tool: A Lysimetric Experiment on Barley

A Sustainability Assessment of the Greenseeker N Management Tool: A Lysimetric Experiment on Barley

UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

Assessing the Maize (Zea mays L.) Crop Performance Using Spectral Indices under Different Sowing Dates and Irrigations Schedules

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