How Life Science Vision Systems Increase the Accuracy of Crop Yield Estimation

Life science vision systems have automated many of the agriculture industry’s time-consuming, labor-intensive tasks. Machine vision has improved harvesting, crop control, and weeding methods. Growers now look to vision systems to more accurately predict crop yield estimates.

Benefits of Life Science Vision Systems

Vision systems allow yield variation maps to be generated earlier in the season than traditional methods. Farms can leverage the data to adjust management practices to improve outputs. Vision systems have progressed to the point where some can be used out-of-the-box, requiring no prior training or labeling. Depending on the application, more sophisticated and even custom systems are available to meet specific agricultural challenges. Either way, vision systems allow many processes to be automated.

Vehicle-Mounted Systems

Camera systems can be mounted to vehicles, such as a tractor or utility vehicle, to capture images of shoots, flowers and berries, for example. Vineyards mount cameras to vehicles to take images during an early spray pass.

Vehicle-mounted vision systems also facilitate a stratified sampling process, which significantly reduces the number of samples required to estimate yield with precision. The involves dividing the vineyard into groups, where a probability sample is then drawn from each group.

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Data important to vineyards includes bunch-to-shoot ratios and bunch-to-inflorescence ratios, which contribute most to uncertainty in early season forecasts. There are now smartphone apps for image processing that have been developed for flower and berry counting. The apps speed up data capture and provide an easy interface to process data in a cloud-based yield estimation system.

UAV Imaging

Vision systems mounted to unmanned aerial vehicles, also called UAVs or drones, are growing in popularity for a variety of agricultural applications, including crop yield estimation.

UAVs have an advantage over field-collected data because they can fly over a large area relatively quickly, significantly reducing the time needed to obtain the imaging data. Crop yield estimation requires the collection and analysis of specific data during a crop’s flowering period. UAV vision systems are ideal because they can capture extremely accurate measurements, often in centimeters. The system records daily observations with greatly reduced labor and time costs.

Looking into the Future

Vision systems for agricultural applications like crop yield estimation are outperforming industry standard manual yield estimation methods. Facilitating improved data management practices by providing timely, more accurate data, the agricultural industry is now better equipped to increase the accuracy of yield production.