  Object detection and semantic segmentation are two of the most widely adopted
deep learning algorithms in agricultural applications. One of the major sources
of variability in image quality acquired in the outdoors for such tasks is
changing lighting condition that can alter the appearance of the objects or the
contents of the entire image. While transfer learning and data augmentation to
some extent reduce the need for large amount of data to train deep neural
networks, the large variety of cultivars and the lack of shared datasets in
agriculture makes wide-scale field deployments difficult. In this paper, we
present a high throughput robust active lighting-based camera system that
generates consistent images in all lighting conditions. We detail experiments
that show the consistency in images quality leading to relatively fewer images
to train deep neural networks for the task of object detection. We further
present results from field experiment under extreme lighting conditions where
images without active lighting significantly lack to provide consistent
results. The experimental results show that on average, deep nets for object
detection trained on consistent data required nearly four times less data to
achieve similar level of accuracy. This proposed work could potentially provide
pragmatic solutions to computer vision needs in agriculture.
