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Proceedings Paper

Machine learning models for predicting lettuce health using UAV imageries
Author(s): Frank Pham; Amar Raheja; Subodh Bhandari
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Paper Abstract

This paper presents the development and validation of machine learning models for the prediction of water and nitrogen stresses in lettuce. Linear regression and deep learning neural networks, mainly convolutional neural networks (CNNs), are used to train the machine learning models. The data used for the training include both airborne and proximal sensor data. The airborne data used are digital images collected from unmanned aerial vehicles (UAVs) and the normalized difference vegetation index (NDVI) obtained from airborne multispectral images. Chlorophyll meter, water potential meter, and spectroradiometer are the proximal sensors used. Also used for the training are agronomic measurements such as leaf count and plant height. For the validation of the developed models, two sets of tests were performed. The first test used a set of data similar to the training data, but different from the training data. The second test used aerial images of various random lettuce plots at farms obtained from Google Maps. The second test evaluates the models’ portability and performance in an unknown environment using the data that was not collected from the experimental plot. The goal of the machine learning algorithms is to provide precise detection of nitrogen and water stresses on a plant level basis using just the digital images collected from UAVs. This will help reduce the cost associated with precision agriculture.

Paper Details

Date Published: 14 May 2019
PDF: 14 pages
Proc. SPIE 11008, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV, 110080Q (14 May 2019); doi: 10.1117/12.2519157
Show Author Affiliations
Frank Pham, California State Polytechnic Univ., Pomona (United States)
Amar Raheja, California State Polytechnic Univ., Pomona (United States)
Subodh Bhandari, California State Polytechnic Univ., Pomona (United States)

Published in SPIE Proceedings Vol. 11008:
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV
J. Alex Thomasson; Mac McKee; Robert J. Moorhead, Editor(s)

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