Develop a deep learning algorithm for automated visual recognition of pests and

Artificial intelligence applications, in particular deep learning techniques, that
underpin image recognition are transforming society. They are now widely used
in image analysis for a wide range of applications requiring object segmentation,
classification and recognition. Teams at Lincoln have pioneered there use in
medical imaging (especially retinal images to detect eye disease) and pest
recognition. More recently the Lincoln group have developed a highly accurate
system to recognise, count and geo tag locust insects in China. This system has
deployed state of the art ResNet convolutional neural networks for high-speed
pest recognition and these have also been enabled for application on a standard
smart phone. This technology enables a step change in pest and diseases
recognition and quantification, providing novel and powerful tools to assist
agronomic support for multiple fruit pest and diseases.
The development of these systems is not trivial. They require very amounts of
training data and input from expert agronomists. Therefore, in this PhD we will
develop a deep learning image library and algorithms to identify, enumerate and
geo locate a range of critical pests and diseases that impact soft fruit production,
including SWD, Western Flower Thrips, Aphids, Powdery Mildew and Botrytis.
This enables accurate pest and disease monitoring, at least as accurate as
human agronomists, automation of agronomic support and more effective
decision-making by fruit growers. Simple application would include pest counting
and recognition on standard sticky traps as well as more challenging applications
to recognise live pests on crops. Specific attention will be paid to investigate
whether it is possible to detect those strawberry leaves or fruits with latent fungal
infection.