  Agriculture is vital for human survival and remains a major driver of several
economies around the world; more so in underdeveloped and developing economies.
With increasing demand for food and cash crops, due to a growing global
population and the challenges posed by climate change, there is a pressing need
to increase farm outputs while incurring minimal costs. Previous machine vision
technologies developed for selective weeding have faced the challenge of
reliable and accurate weed detection. We present approaches for plant seedlings
classification with a dataset that contains 4,275 images of approximately 960
unique plants belonging to 12 species at several growth stages. We compare the
performances of two traditional algorithms and a Convolutional Neural Network
(CNN), a deep learning technique widely applied to image recognition, for this
task. Our findings show that CNN-driven seedling classification applications
when used in farming automation has the potential to optimize crop yield and
improve productivity and efficiency when designed appropriately.
