Convolutional Neural Network for Studying Plant Nutrient Deficiencies

Abstract

We discuss the development of a vision-based plant phenotyping system based on a novel type of robotic system called a food computer. The food computer used in this project is called the GrowBot. It has a host of sensors to help analyse the growth chamber including a Raspberry Pi camera. The project revolved around developing a system to segment the plant canopy from its background and analyse nutrient deficiencies from the images taken by the camera. The pilot project investigated how a segmentation model called U-Net could be used to study the images. One of the drawbacks of many existing vision-based plant phenotyping systems is that their convolutional neural networks (CNNs) were trained to analyse very ideal images of individual leaves. This pilot project tried to address that issue, while at the same time explored how to train the neural networks to learn segmentation from a small image dataset.