Plant Growth and the Environment

A plant’s growth depends, in part, on its environment. Factors like how much sun a plant gets, how much water is available to it, and a plant’s temperature influence the structure of a plant, and how it grows. To understand plant-environment relationships, people measure plant characteristics along with environmental conditions over time and space. However, collecting these measurements can be extraordinarily time and labor intensive, and expensive. For example, plant height is a good indicator of plant robustness, but to measure this someone would likely need to either measure the height of each plant they are studying, or would need to buy LIDAR-based measurements (usually taken by aircraft). Furthermore, to understand how the environment is driving the measured patterns (in this case height), environmental conditions also must be measured and matched to each plant at each time point. These measurements also need to be replicated so that we can tell if we see a true pattern, or just random noise in the data — this is particularly true across space (like a farm field) where plants growing at different parts of the same field could in fact have vastly different environments.

Imaging technologies have emerged as powerful tools for making scalable plant measurements that are cheaper, and take less time to measure than other approaches, while providing data that is impossible to measure with other techniques. Furthermore, by using successive images of the same plants over time, computer vision and machine learning can identify drivers to plant growth or function (like water loss through transpiration). For example we could capture things like how many tomatoes are growing, how large they are, and how their growth changes in response to weather (like temperature, and sun intensity). We can also sense things like the presence (and even timing of onset) for disease and nutrient deficiency that can be used for management decisions like when to apply pesticides or add fertilizer, or linked to things like environmental stress. Furthermore, because the measurements are from images, there is no destructive sampling, and the only work required is taking the image.

An overhead image of these tomatoes doesn’t show any tomatoes, but an oblique view lets you look between the leaves. Do you see the tomato?

Most plant imagery used today is collected using satellites, or using airplanes. These data are great because they capture vast areas while potentially taking high resolution images of plants. However, most of these images are of only the tops of plants which leaves a wealth of data uncollected. For example, if I wanted to count tomatoes in an image to measure how many tomatoes a farm field has, and how big they are, an overhead view would only show the tomatoes that appear at or above the plant canopy. An oblique view would provide a better picture, but using an airplane or satellite for this would be impractical because of atmospheric interference and the difficulty of piloting a plane close enough to the ground to get an oblique angle.

But, in this use case, there are other better tools we could use like handheld cameras (like cell phones), and UAVs. In fact, these platforms offer tremendous potential for collecting high resolution, and oblique imagery of plants because everybody has cell phone cameras already, and UAVs are quickly becoming more affordable.

That is all I’ve got time to write for now, but look for future posts where I will be exploring how we can better use imagery and camera and UAV platforms to measure plant characteristics and how they change over time and space.