Unsupervised weed detection in spinach seedling organic farms
Journal
Proceedings of the 24th International Electrotechnical and Computer Science Conference ERK
Date Issued
2015
Author(s)
Gjorgjevikj, Dejan
Abstract
Weed removal during the early phase of seedling
development is a very important process in agriculture. It helps
the useful plants to sprout quickly and use most of the soil’s
organic materials for their own development. The increasing
number of human population in the world increases the amount
of food that needs to be produced thus the automation of the
process of plant based food production is required. In this paper
we present an unsupervised approach towards automated weed
detection in spinach seedling farms. The images are taken under
natural conditions and their green regions are segmented to
detect the plants in the images. After that, image descriptors are
generated for each plant segment and unsupervised clustering is
performed to separate the weeds from the spinach seedlings. The
results of the unsupervised learning are compared with the
results obtained with supervised learning on the same data. The
conclusions are presented in the paper.
development is a very important process in agriculture. It helps
the useful plants to sprout quickly and use most of the soil’s
organic materials for their own development. The increasing
number of human population in the world increases the amount
of food that needs to be produced thus the automation of the
process of plant based food production is required. In this paper
we present an unsupervised approach towards automated weed
detection in spinach seedling farms. The images are taken under
natural conditions and their green regions are segmented to
detect the plants in the images. After that, image descriptors are
generated for each plant segment and unsupervised clustering is
performed to separate the weeds from the spinach seedlings. The
results of the unsupervised learning are compared with the
results obtained with supervised learning on the same data. The
conclusions are presented in the paper.
Subjects
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