Lettuce plant health assessment using UAV-based hyperspectral sensor and proximal sensors

Subodh Bhandari; Amar Raheja; Mohammad Chaichi; Frank Pham; Tristan Sherman; Matthew Dohlen; Sharafat Khan

doi:10.1117/12.2557686

18 May 2020 Lettuce plant health assessment using UAV-based hyperspectral sensor and proximal sensors

Subodh Bhandari, Amar Raheja, Mohammad Chaichi, Frank Pham, Tristan Sherman, Matthew Dohlen, Sharafat Khan

Proceedings Volume 11414, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V; 1141404 (2020) https://doi.org/10.1117/12.2557686
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

This paper presents the assessment of lettuce plant health using unmanned aerial vehicle (UAV)-based hyperspectral sensor, proximal sensors, and measurement of agronomic & physiological parameters. Hyperspectral data of lettuce plants at Cal Poly Pomona’s Spadra Farm was collected from a DJI Matric 600 multicopter UAV. An experimental lettuce plot was designed for the study. The plot was divided into several subplots that were subject to different water and nitrogen applications with three replications. Proximal sensors included Handheld spectroradiometer, water potential meter, and chlorophyll meter. The hypespectral data from the UAV and spectroradiometer were used in the determination of several vegetation indices including normalized difference vegetation index (NDVI), water band index (WBI), and modified chlorophyll absorption ratio index (MCARI). These indices were compared with chlorophyll meter data, water potential, plant height, leaf numbers, leaf water content, and leaf nitrogen content. With the hyperspectral data collected so far, MCARI has shown good correlation with chlorophyll meter data and WBI has shown good correlation with leaf water content. The paper will show and discuss all the vegetation indices and their relationship with proximal sensor data, agronomic measurement, and leaf water & nitrogen contents.

Citation Download Citation

Subodh Bhandari, Amar Raheja, Mohammad Chaichi, Frank Pham, Tristan Sherman, Matthew Dohlen, and Sharafat Khan "Lettuce plant health assessment using UAV-based hyperspectral sensor and proximal sensors", Proc. SPIE 11414, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V, 1141404 (18 May 2020); https://doi.org/10.1117/12.2557686

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KEYWORDS

Sensors

Nitrogen

Vegetation

Remote sensing

Unmanned aerial vehicles

Reflectivity

Absorption

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