Date of Award

9-1-2025

Degree Type

Thesis

Degree Name

Master of Science (M.S.)

Department

Agricultural and Environmental Sciences

First Advisor

Clement Akumu

Abstract

Abstract Wheat canopy cover and greenness are useful crop performance indicators that provide significant insight into the performance of wheat crops. Understanding the effects of no-till and conventional tillage practices on winter wheat canopy cover and greenness will help inform agronomic decision making in wheat production. Therefore, the aim of this study was to assess the effects of no-till and conventional tillage practices on wheat canopy cover and greenness using drones. Wheat canopy cover mapping was carried out using deep learning supervised classification. The normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI) and optimized soil adjusted vegetation index (OSAVI) were generated as proxies of wheat canopy greenness. Our results showed that throughout the growing season, the mean wheat canopy cover was about 5% greater in conventional tillage plots relative to no-till plots. Similarly, OSAVI had a 1% increase in conventional plots relative to no-till plots. In contrast, wheat canopy greenness NDVI and GNDVI were about 1% higher in no-till plots relative to conventional plots. However, there was no significant difference (p-value >0.05) in wheat canopy and greenness between the no-till and conventional tillage plots. The results of this study will support sustainable tillage management practices and data-driven agronomic decisions in winter wheat production.

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