Sesame yield prediction using UAV-borne hyperspectral imagery, crop traits and genotype category

M.M. Sahoo; R. Tarshish; I. Sabag; Y. Gadri; G. Morota; Z. Peleg; V. Alchanatis; I. Herrmann

Sesame yield prediction using UAV-borne hyperspectral imagery, crop traits and genotype category

In: Precision agriculture '25

Authors:

M.M. Sahoo

M.M. Sahoo
maitreyamohan.sahoo@mail.huji.ac.il

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R. Tarshish

R. Tarshish

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I. Sabag

I. Sabag

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Y. Gadri

Y. Gadri

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G. Morota

G. Morota

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Z. Peleg

Z. Peleg

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V. Alchanatis

V. Alchanatis

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I. Herrmann

I. Herrmann

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Type:: Chapter

Pages:: 546–552

DOI:: https://doi.org/10.1163/9789004725232_071

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Dar Hadith al Hassania

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Crop yield prediction, if done early enough, can help growers and breeders increase production. This research provides a method for predicting sesame yield by integrating information from remote sensing and phenotypic traits. Based on a field-grown sesame diversity panel of 185 genotypes, selected traits were analyzed to explain their contribution to yield. It was found that chlorophyll a variation (i.e., low, variable and high content) provided the maximum basis for categorically predicting sesame yield from remotely sensed hyperspectral imagery using machine learning models. The highlighted spectral features can be used to categorize sesame yield based on chlorophyll content.

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Precision agriculture '25

E-Book ISBN:: 9789004725232

Publisher:: Wageningen Academic

Print Publication Date:: 18 Jun 2025

Subjects
- Life Sciences
  - General
  - Agriculture & Environment

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Sesame yield prediction using UAV-borne hyperspectral imagery, crop traits and genotype category

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Precision agriculture '25

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