Orchard Precision - Sensor Technologies and Weather Modeling

Sensor and imaging technologies, along with weather modeling, are being investigated for applications in intensive orchard systems.
Orchard Precision - Sensor Technologies and Weather Modeling - Articles


Photo: Amy Tabb

These technologies have the potential to cross multiple areas of tree fruit production:

  • determining crop load or assessing when to thin
  • determining insect presence/disease infection and eradication
  • monitoring insect population thresholds

As engineers investigate these potentials, they will likely find that different technological approaches will prove successful for different orchard management tasks.

Trials with Crop Imaging, Sensors for Counting Insects, Weather Modeling

In a Specialty Crop Research Initiative project to develop "Comprehensive Automation Technologies for Specialty Crops (CASC)," a transdisciplinary research team addressed sensor technologies for the automation of fruit production. Team collaborators developed and evaluated automation solutions that growers can use to increase labor efficiency, detect insect pests and diseases, monitor plant health, predict crop load and reduce crop damage at harvest.

Team engineers and horticulturists tested algorithms to successfully identify fruit in a tree canopy. Penn State entomologists collected and annotated images of both codling moth and Oriental fruit moth adults within traps. These images, and numerous others, were utilized by Purdue University engineers to test visual algorithms for insect detection. Plant pathologists created a database of images for use in developing and testing image processing methods and algorithms for fire blight.

Engineers and entomologists developed and tested a novel tool for monitoring insect pests--the Z-Trap. Like current traps, it uses pheromones to attract target insects. Its novelties are a high-voltage coil to stun insects entering the trap, bio-impedance sensors to count insects automatically as they fall into the trap, wireless connections to send pest information directly to a server on the farm, and handheld/web-based software to manage the entire system. A web-based user interface called "MyTraps" was developed to allow the user to effectively manage and visualize insect population data collected by Z-Traps.

A newer initiative is to evaluate the MaluSim carbohydrate model for optimizing apple thinning decisions. In cooperation with Dr. Alan Lakso, Cornell University, the model is being evaluated at multiple orchard sites around the state that have on-site weather stations with solar sensors. In 2013 and 2014, the Cornell model was tested using weather data from ZedX as well as weather data collected on-site by North East Weather Applications (NEWA) weather stations. ZedX has developed an electronically delivered site-specific thinning module that will be evaluated as well. We continue to evaluate the MaluSim carbohydrate model for optimizing apple thinning decisions in cooperation with Dr. Alan Lakso. In 2015 information from nine sites was downloaded from the NEWA weather site and disseminated to growers via electronic newsletters every 4 to 5 days.

Future Outlook

Achieving consistently high fruit quality requires vigilant pest management and information on the various environmental stresses that can reduce quality and size as well as blemish the product, or in some cases exclude it from processing or export. While pest monitoring and integrated pest management (IPM) systems are cost-effective practices in specialty crops, the frequency and cost of trap monitoring and identification of specific pest damage has limited the ability of the grower to perceive pest migration at the onset. The development of automated traps, computer vision and improved modeling systems will reduce the need for labor while increasing the accuracy of crop monitoring, resulting in improved crop management, more effective pest monitoring, higher fruit quality and reduced pesticide application.

Prepared by: Rob Crassweller, Tara Baugher, Jim Schupp, Greg Krawczyk, Brian Lehman, Edwin Winzeler, Larry Hull, Amy Tabb, Johnny Park