Book of Extended summaries ISDA

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
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