Book of Extended summaries ISDA
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Book of Extended summaries ISDA

Book of Extended summaries ISDA Book of Extended summaries ISDA

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International Conference on Reimagining Rainfed Agro-ecosystems: Challenges & Opportunities during 22-24, December 2022 at ICAR-CRIDA, Hyderabad Methodology In the present study, tomato as a test crop was grown under stress-free outdoor conditions at the ICAR Research Complex for NEH, Umiam, Meghalaya. Periodic measurements of the spectral reflectance of standing tomato culture were taken in increments of 325 to 1075 nm by handheld field Spectroradiometer (ASD Handheld 2) and correlated with the concentration of N measured in the lab (Jackson, 1973). The reflectance data measured periodically were processed using the SG filter (Savitzky and Golay 1964) to obtain both smoothed and normalized spectra. To select the variables (grouping and predictor), the correlation coefficient and stage discrimination analysis (SDA) were performed on a large data set to identify sensitive wavelength regions). Sensitive wavelength zones were identified as visible (401-702nm) and NIR (702-1020nm). Follow the multiple linear regression (MLR) model and three machine learning algorithms such as Random Forest (RF), Ridge regression (RR), Support vector machine (SVM) regression also being performed while selecting the satisfactory prediction model based on R-square and RMSE values. Results Reflectance in visible and NIR areas has been shown to be sensitive to nitrogen concentration from tomato growth. Plant N concentration was best predicted using reflectance in the visible (401-702 nm) region, more specifically wave bands at 550nm and 678 nm in the visible region. The smoothed spectra yielded satisfactory results with Random Forest Regression (R 2 = 0.91; RMSE: 0.072) in comparison with SVM, MLR and Ridge Regression. Emerging approaches (RS, AI, ML, Drones etc) for crop management &assessment 760 | Page

International Conference on Reimagining Rainfed Agro-ecosystems: Challenges & Opportunities during 22-24, December 2022 at ICAR-CRIDA, Hyderabad Conclusion Tomato plant and the spectral reflectance curve (Smoothened at Vis-NIR) Monitoring nitrogen levels in plants help in periodic remedial actions for optimum plant growth. The predictive model equation developed using machine learning algorithms and progressive discrimination analysis (SDA) in this study could be helpful in estimating tomato leaf nitrogen concentrations quickly and non-destructive. Therefore, we can substitute carefully (with repeated validation on large datasets) the traditional destructive laboratory method, known for the variety of accuracy based on the time and skills. Predictive models for estimating leaf nitrogen using tomato crop spectral reflectance (R = reflectance) Discriminated Sensitive Spectral region (nm) Sensitive bands (nm) Most Sensitive band(nm) Model Predictive Model Smooth 410 – 1020 550, 678, Y = 678 RF 853, 972 0.203+90.24*R678 Smooth 410 – 1020 550, 678, Y = 678 SVM 853, 972 0.696+81.96*R678 Smooth 410 – 1020 550, 678, Y = 678 RR 853, 972 0.653+82.95*R678 Smooth 410 – 1020 550, 678, Y = 678 MLR 853, 972 0.753+81.45*R 678 *RF: Random Forest, SVM: Support vector machine, RR: Ridge regression, MLR: multilinear regression References Kharat R. B, and Deshmukh D. R. R. 2016. Analysis of Effective Leaf Nitrogen Concentrations in Tomato Plant using Vegetation Indices. Int. J. Eng. Sci. Comp, 18(2), 2397-2400. 761 | Page Emerging approaches (RS, AI, ML, Drones etc) for crop management &assessment

International Conference on Reimagining Rainfed Agro-ecosystems: Challenges &<br />

Opportunities during 22-24, December 2022 at ICAR-CRIDA, Hyderabad<br />

Conclusion<br />

Tomato plant and the spectral reflectance curve (Smoothened at Vis-NIR)<br />

Monitoring nitrogen levels in plants help in periodic remedial actions for optimum plant<br />

growth. The predictive model equation developed using machine learning algorithms and<br />

progressive discrimination analysis (SDA) in this study could be helpful in estimating tomato<br />

leaf nitrogen concentrations quickly and non-destructive. Therefore, we can substitute<br />

carefully (with repeated validation on large datasets) the traditional destructive laboratory<br />

method, known for the variety <strong>of</strong> accuracy based on the time and skills.<br />

Predictive models for estimating leaf nitrogen using tomato crop spectral reflectance (R<br />

= reflectance)<br />

Discriminated<br />

Sensitive<br />

Spectral<br />

region<br />

(nm)<br />

Sensitive<br />

bands<br />

(nm)<br />

Most<br />

Sensitive<br />

band(nm)<br />

Model<br />

Predictive Model<br />

Smooth 410 – 1020<br />

550, 678,<br />

Y =<br />

678 RF<br />

853, 972<br />

0.203+90.24*R678<br />

Smooth 410 – 1020<br />

550, 678,<br />

Y =<br />

678 SVM<br />

853, 972<br />

0.696+81.96*R678<br />

Smooth 410 – 1020<br />

550, 678,<br />

Y =<br />

678 RR<br />

853, 972<br />

0.653+82.95*R678<br />

Smooth 410 – 1020<br />

550, 678,<br />

Y =<br />

678 MLR<br />

853, 972<br />

0.753+81.45*R 678<br />

*RF: Random Forest, SVM: Support vector machine, RR: Ridge regression, MLR: multilinear<br />

regression<br />

References<br />

Kharat R. B, and Deshmukh D. R. R. 2016. Analysis <strong>of</strong> Effective Leaf Nitrogen Concentrations<br />

in Tomato Plant using Vegetation Indices. Int. J. Eng. Sci. Comp, 18(2), 2397-2400.<br />

761 | Page Emerging approaches (RS, AI, ML, Drones etc) for crop management &assessment

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