  Uncontrolled growth of weeds can severely affect the crop yield and quality.
Unrestricted use of herbicide for weed removal alters biodiversity and cause
environmental pollution. Instead, identifying weed-infested regions can aid
selective chemical treatment of these regions. Advances in analyzing farm
images have resulted in solutions to identify weed plants. However, a majority
of these approaches are based on supervised learning methods which requires
huge amount of manually annotated images. As a result, these supervised
approaches are economically infeasible for the individual farmer because of the
wide variety of plant species being cultivated. In this paper, we propose a
deep learning-based semi-supervised approach for robust estimation of weed
density and distribution across farmlands using only limited color images
acquired from autonomous robots. This weed density and distribution can be
useful in a site-specific weed management system for selective treatment of
infected areas using autonomous robots. In this work, the foreground vegetation
pixels containing crops and weeds are first identified using a Convolutional
Neural Network (CNN) based unsupervised segmentation. Subsequently, the weed
infected regions are identified using a fine-tuned CNN, eliminating the need
for designing hand-crafted features. The approach is validated on two datasets
of different crop/weed species (1) Crop Weed Field Image Dataset (CWFID), which
consists of carrot plant images and the (2) Sugar Beets dataset. The proposed
method is able to localize weed-infested regions a maximum recall of 0.99 and
estimate weed density with a maximum accuracy of 82.13%. Hence, the proposed
approach is shown to generalize to different plant species without the need for
extensive labeled data.
