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ARS Home » Northeast Area » Kearneysville, West Virginia » Appalachian Fruit Research Laboratory » Innovative Fruit Production, Improvement, and Protection » Research » Research Project #429759

Research Project: Computer Vision and Machine Learning for Plant Sensing: Applications for Tracking in Agriculture

Location: Innovative Fruit Production, Improvement, and Protection

Project Number: 8080-21000-030-06-S
Project Type: Non-Assistance Cooperative Agreement

Start Date: Sep 1, 2015
End Date: Aug 31, 2020

The objective for this project is to explore the use of tracking algorithms and other techniques in computer vision for various applications, which may include tracking of insects for the monitoring of pest populations, or tracking of flowers and fruit for flower, or fruit load estimation.

Tracking algorithms are typically limited by needing to define the number of targets in a sequence beforehand. However, in agricultural applications, oftentimes, the number of objects is unknown. New tracking algorithms, such as the Random Finite Set method, allow for estimation not only of the position of the object, but also of the number of objects. With the relaxation of the requirement that the number of objects be known, these new tracking algorithms could then be applied to agricultural problems. Tracking is needed in many areas of agricultural automation, especially when robotics are involved. This project aims to investigate the use of tracking algorithms in an agricultural setting. Since it is unknown how well these algorithms will work in practice, this project may investigate other related topics where the aim is to localize objects of interest in images.