Weed identification and removal using machine learning techniques and unmanned ground vehicles

Marco A. Mercado Espinoza; Colby Z. Le; Amar Raheja; Subodh Bhandari

doi:10.1117/12.2557625

15 May 2020 Weed identification and removal using machine learning techniques and unmanned ground vehicles

Marco A. Mercado Espinoza, Colby Z. Le, Amar Raheja, Subodh Bhandari

Proceedings Volume 11414, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V; 114140J (2020) https://doi.org/10.1117/12.2557625
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

This paper presents the use of unmanned ground vehicle (UGV) and machine learning techniques for the identification removal of weeds in lettuce crop. In recent years, breakthroughs in deep learning, computer vision, and miniaturization of electronic devices have paved the way for use of unmanned systems and machine learning techniques for applications that are dull, dirty, and dangerous for humans including agricultural applications. Unmanned systems and machine learning techniques have potential to transform and modernize how the crops are grown and cared. One of the problems every farmer encounters is invasive weeds that can kill or hinder the growth of crops by stealing water, nutrients, and sunlight from the plants. Herbicides are used to kill and stop the growth of weeds. However, use of herbicides increases the cost of production, is labor intensive, and exposes human to dangerous chemicals. Manually removing the weeds is also very labor intensive. Using machine learning techniques and UGVs for the identification and removal of weeds will reduce the cost of production, human exposure to dangerous chemicals, and dependence on human labor. Models were trained using YOLO, Faster R-CNN, and SSD Mobile object detection techniques. For the training of machine learning models, images of the weeds in an experimental lettuce plot was collected throughout the growing season. Validation of the developed models was performed using different data sets than the training data sets in the same plot as well as a different plot. The identified weeds were then removed using the UGV through teleoperation.

Citation Download Citation

Marco A. Mercado Espinoza, Colby Z. Le, Amar Raheja, and Subodh Bhandari "Weed identification and removal using machine learning techniques and unmanned ground vehicles", Proc. SPIE 11414, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V, 114140J (15 May 2020); https://doi.org/10.1117/12.2557625

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available