Phenological Measurements of Cover-Cropped Agricultural Systems Using Low-Cost Cameras

In-situ phenological tracking of agricultural surfaces provides guidance for large-scale crop modelling and insights into the growth dynamics of crops. Linking phenological growth curves with CO₂ net ecosystem exchange (NEE) collected from eddy covariance systems improves remote sensing models predicting carbon gains and losses. This information exists for the main agricultural crops – i.e., corn, soy, wheat. However, farmers are changing their management practices to improve soil health and climate resiliency of their lands. Cover cropping during fallow periods is a mainstream practice that is scattered over the landscape with different cover crops growing at different time periods. Thus, few phenological growth curves exist for cover crops. Increasing the spatial coverage of agricultural phenological measurements will improve estimates of the NEE of these systems. A project initiated in 2020 in Elora, Ontario, Canada, aimed to develop growth curves for cover cropped systems and develop a means to increase the spatial coverage of phenological measurements. Small, cost-friendly phenocams were developed using off-the-shelf Raspberry-Pi components (Pi-Cams). These were deployed in a cover-cropped agricultural field site adjacent to a standard phenocam. Growth curves were calculated for the main crop (corn) and for the cover crops. The Pi-Cams produced comparable growth curves for the corn crop and tracked NEE slightly better than the phenocam. The cover-crop growth curve measured by the phenocam related well to the measured NEE; however, the Pi-Cams did not track the NEE as well for the cover crop since the greenness signal was weaker for the shorter cover crop. This could be improved by deploying multiple pi-cams at a shorter distance to the surface. Overall, this work shows the promise for increasing the spatial coverage of phenological tracking using low-cost equipment.