Machine learning models for predicting lettuce health using UAV imageries

Frank Pham; Amar Raheja; Subodh Bhandari

doi:10.1117/12.2519157

14 May 2019 Machine learning models for predicting lettuce health using UAV imageries

Frank Pham, Amar Raheja, Subodh Bhandari

Proceedings Volume 11008, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV; 110080Q (2019) https://doi.org/10.1117/12.2519157
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

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.

Conference Presentation

Citation Download Citation

Frank Pham, Amar Raheja, and Subodh Bhandari "Machine learning models for predicting lettuce health using UAV imageries", Proc. SPIE 11008, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV, 110080Q (14 May 2019); https://doi.org/10.1117/12.2519157

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