Book of Extended summaries ISDA

International Conference on Reimagining Rainfed Agro-ecosystems: Challenges &
Opportunities during 22-24, December 2022 at ICAR-CRIDA, Hyderabad
T5-25P-1296
Non-Destructive and Rapid Estimation of the Foliar Nitrogen of the
Tomato Plant by Proximal Hyperspectral Remote Sensing Pl. Biochemical
Properties: Nitrogen
Md. Zafar, B. U. Choudhury*, H. Das, R, Narzari, and V. K. Mishra
ICAR Research Complex for NEH Region, Umiam, Meghalaya- 793103
* burhan3i@yahoo.com
Dynamic fertilizer management is a means of making soil nutrients available for plant
development, according to their needs, which is the main aim of precision agriculture. Soil
nitrogen (N) is one of the most important essential nutrients for optimal crop growth and
yield. It is a key component of enzymes, vitamins, chlorophyll and other cellular components,
all of which are critical to crop growth and development. It is therefore one of the most
important essential nutrients to optimize crop yields, including tomato plants. Too much
nitrogen can reduce yields while the nitrogen deficit relative to the plant uptake requirement
has also stunted the plant vegetative growth and reduces yields. The destructive measure for
periodic monitoring of the nutritional state of plants in this situation is not a logical option. The
traditional destructive method is time-consuming, error-prone, competence-oriented, and
requires a well-equipped laboratory for measurements. This makes it difficult to remediate in
real time, including fertilizing based on laboratory measurements. However, with advances in
hyperspectral proximal remote sensing technology, plant N estimation can be conducted
quickly and non-destructively while being economic and repetitive in monitoring.
Spectroscopy techniques are a technology that requires the acquisition of data in spectral bands
and based on the most sensitive bands, a predictive model can be developed to predict nitrogen
as well as other plant biochemical properties.
In this study, we have attempted to develop a non-destructive hyperspectral proximal remote
sensing-based estimation method for predicting the N concentration of standing tomato
plants. For this, we used multiple and multi-date foliar spectral reflectance measurements from
an ASD 2 handheld Spectroradiometer and correlated with destructive lab measurements on
multiple occasions. Finally, a predictive model was developed for the estimation of foliar
nitrogen concentrations of tomato plants in the field based on the periodically measured
reflectance and the concentration of foliar nitrogen in the laboratory while using Advanced
Machine Learning Techniques (e.g. Random forest, Support vector machine, Ridge
regression) in addition to Multi-linear regression.
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