We give thanks for our food, and for the knowledge to improve it through science and technology!
While much of our work focuses on imaging in clinical trial management and preclinical research, we also use our imaging expertise and technology to help other industries, such as agricultural technology (e.g., fertilizers) and food science. In these industries there are limitless applications, but this Thanksgiving we chose to focus on the universally iconic and multi-purpose crop: corn. See our analysis description below.
This time of year is also ideal for reflecting on our many blessings, and to say “Thank You” to all our customers for allowing ImageIQ to play a role in supporting their science and research for the betterment of mankind. For this we are truly grateful and humbled.
This Thanksgiving, we leveraged imaging and image analysis to automatically detect and measure nitrogen content in corn plants by segmenting various corn leaf characteristics. This methodology allows farmers to know which (and when) crops need more (or less) fertilizer. By optimizing their crop management regimen, farmers can take a “sniper,“ rather than “shotgun,” approach to fertilizer application, and in turn save money and protect the environment by reducing localized chemical run-off. For corn plants, we acquired an image of a representative leaf using an SLR camera (or smart phone) and sent a corresponding tissue sample to a lab (“ground truth”) to measure the nitrogen content in the plant. The red-green-blue (RGB) metrics (e.g., pattern, intensity, relative %, etc.) in the corn leaf was cross-correlated to the known nitrogen content after software automatically segmented the leaf from the image and removed any spectral reflection due to surface inconsistencies and wax on the leaf. A test dataset of our sample corn leaves was fed through a neural network algorithm using the image’s mean RGB metrics and known nitrogen content to train the data model and algorithm to accurately and quantitatively measure and predict the nitrogen content in subsequent corn plants, without the need for lab-based analysis of leaf tissue specimens.